what is the importance of independent variable in research

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A Student’s Guide to the Classification and Operationalization of Variables in the Conceptualization and Design of a Clinical Study: Part 1

Chittaranjan andrade.

1 Dept. of Clinical Psychopharmacology and Neurotoxicology, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, India.

Students without prior research experience may not know how to conceptualize and design a study. This article explains how an understanding of the classification and operationalization of variables is the key to the process. Variables describe aspects of the sample that is under study; they are so called because they vary in value from subject to subject in the sample. Variables may be independent or dependent. Independent variables influence the value of other variables; dependent variables are influenced in value by other variables. A hypothesis states an expected relationship between variables. A significant relationship between an independent and dependent variable does not prove cause and effect; the relationship may partly or wholly be explained by one or more confounding variables. Variables need to be operationalized; that is, defined in a way that permits their accurate measurement. These and other concepts are explained with the help of clinically relevant examples.

Key Message:

This article explains the following concepts: Independent variables, dependent variables, confounding variables, operationalization of variables, and construction of hypotheses.

In any body of research, the subject of study requires to be described and understood. For example, if we wish to study predictors of response to antidepressant drugs (ADs) in patients with major depressive disorder (MDD), we might select patient age, sex, age at onset of MDD, number of previous episodes of depression, duration of current depressive episode, presence of psychotic symptoms, past history of response to ADs, and other patient and illness characteristics as potential predictors. These characteristics or descriptors are called variables. Whether or not the patient responds to AD treatment is also a variable. A solid understanding of variables is the cornerstone in the conceptualization and preparation of a research protocol, and in the framing of study hypotheses. This subject is presented in two parts. This article, Part 1, explains what independent and dependent variables are, how an understanding of these is important in framing hypotheses, and what operationalization of a variable entails.

Variables are defined as characteristics of the sample that are examined, measured, described, and interpreted. Variables are so called because they vary in value from subject to subject in the study. As an example, if we wish to examine the relationship between age and height in a sample of children, age and height are the variables of interest; their values vary from child to child. In the earlier example, patients vary in age, sex, duration of current depressive episode, and response to ADs. Variables are classified as dependent and independent variables and are usually analyzed as categorical or continuous variables.

Independent and Dependent Variables

Independent variables are defined as those the values of which influence other variables. For example, age, sex, current smoking, LDL cholesterol level, and blood pressure are independent variables because their values (e.g., greater age, positive for current smoking, and higher LDL cholesterol level) influence the risk of myocardial infarction. Dependent variables are defined as those the values of which are influenced by other variables. For example, the risk of myocardial infarction is a dependent variable the value of which is influenced by variables such as age, sex, current smoking, LDL cholesterol level, and blood pressure. The risk is higher in older persons, in men, in current smokers, and so on.

There may be a cause–effect relationship between independent and dependent variables. For example, consider a clinical trial with treatment (iron supplement vs placebo) as the independent variable and hemoglobin level as the dependent variable. In children with anemia, an iron supplement will raise the hemoglobin level to a greater extent than will placebo; this is a cause–effect relationship because iron is necessary for the synthesis of hemoglobin. However, consider the variables teeth and weight . An alien from outer space who has no knowledge of human physiology may study human children below the age of 5 years and find that, as the number of teeth increases, weight increases. Should the alien conclude that there is a cause–effect relationship here, and that growing teeth causes weight gain? No, because a third variable, age, is a confounding variable 1 – 3 that is responsible for both increase in the number of teeth and increase in weight. In general, therefore, it is more proper to state that independent variables are associated with variations in the values of the dependent variables rather than state that independent variables cause variations in the values of the dependent variables. For causality to be asserted, other criteria must be fulfilled; this is out of the scope of the present article, and interested readers may refer to Schunemann et al. 4

As a side note, here, whether a particular variable is independent or dependent will depend on the question that is being asked. For example, in a study of factors influencing patient satisfaction with outpatient department (OPD) services, patient satisfaction is the dependent variable. But, in a study of factors influencing OPD attendance at a hospital, OPD attendance is the dependent variable, and patient satisfaction is merely one of many possible independent variables that can influence OPD attendance.

Importance of Variables in Stating the Research Objectives

Students must have a clear idea about what they want to study in order to conceptualize and frame a research protocol. The first matters that they need to address are “What are my research questions?” and “What are my hypotheses?” Both questions can be answered only after choosing the dependent variables and then the independent variables for study.

In the case of a student who is interested in studying predictors of AD outcomes in patients with MDD, treatment response is the dependent variable and patient and clinical characteristics are possible independent variables. So, the selection of dependent and independent variables helps defines the objectives of the study:

• To determine whether sociodemographic variables, such as age and sex, predict the outcome of an episode of depression in MDD patients who are treated with an AD.
• To determine whether clinical variables, such as age at onset of depression, number of previous depressive episodes, duration of current depressive episode, and the presence of soft neurological signs, predict the outcome of an episode of depression in MDD patients who are treated with an AD.

Note that in a formal research protocol, the student will need to state all the independent variables and not merely list examples. The student may also choose to include additional independent variables, such as baseline biochemical, psychophysiological, and neuroradiological measures.

Importance of Variables in Framing Hypotheses

A hypothesis is a clear statement of what the researcher expects to find in the study. As an example, a researcher may hypothesize that longer duration of current depression is associated with poorer response to ADs. In this hypothesis, the duration of the current episode of depression is the independent variable and treatment response is the dependent variable. It should be obvious, now, that a hypothesis can also be defined as the statement of an expected relationship between an independent and a dependent variable . Or, expressed visually, (independent variable) (arrow) (dependent variable) = hypothesis.

It would be a waste of time and energy to do a study to examine only one question: whether duration of current depression predicts treatment response. So, it is usual for research protocols to include many independent variables and many dependent variables in the generation of many hypotheses, as shown in Table 1 . Pairing each variable in the “independent variable” column with each variable in the “dependent variable” column would result in the generation of these hypotheses. Table 2 shows how this is done for age. Sets of hypotheses can likewise be constructed for the remaining independent and dependent variables in Table 1 . Importantly, the student must select one of these hypotheses as the primary hypothesis; the remaining hypotheses, no matter how many they are, would be secondary hypotheses. It is necessary to have only one hypothesis as the primary hypothesis in order to calculate the sample size necessary for an adequately powered study and to reduce the risk of false positive findings in the analysis. 5 In rare situations, two hypotheses may be considered equally important and may be stated as coprimary hypotheses.

Independent Variables and Dependent Variables in a Study on Sociodemographic and Clinical Prediction of Response of Major Depressive Disorder to Antidepressant Drug Treatment

Combinations of Age with Dependent Variables in the Generation of Hypotheses

Operationalization of Variables

In Table 1 , suicidality is listed as an independent variable and severity of depression, as a dependent variable. These variables need to be operationalized; that is, stated in a way that explains how they will be measured. Table 3 presents three ways in which suicidality can be measured and four ways in which (reduction in) the severity of depression can be measured. Now, each way of measurement in the “independent variable” column can be paired with a way of measurement in the “dependent variable” column, making a total of 12 possible hypotheses. In like manner, the many variables listed in Table 1 can each be operationalized in several different ways, resulting in the generation of a very large number of hypotheses. As already stated, the student must select only one hypothesis as the primary hypothesis.

Possible Ways of Operationalization of Suicidality and Depression

HAM-D: Hamilton Depression Rating Scale, MADRS: Montgomery–Asberg Depression Rating Scale.

Much thought should be given to the operationalization of variables because variables that are carelessly operationalized will be poorly measured; the data collected will then be of poor quality, and the study will yield unreliable results. For example, socioeconomic status may be operationalized as lower, middle, or upper class, depending on the patient’s monthly income, on the total monthly income of the family, or using a validated socioeconomic status assessment scale that takes into consideration income, education, occupation, and place of residence. The student must choose the method that would best suit the needs of the study, and the method that has the greatest scientific acceptability. However, it is also permissible to operationalize the same variable in many different ways and to include all these different operationalizations in the study, as shown in Table 3 . This is because conceptualizing variables in different ways can help understand the subject of the study in different ways.

Operationalization of variables requires a consideration of the reliability and validity of the method of operationalization; discussions on reliability and validity are out of the scope of this article. Operationalization of variables also requires specification of the scale of measurement: nominal, ordinal, interval, or ratio; this is also out of the scope of the present article. Finally, operationalization of variables can also specify details of the measurement procedure. As an example, in a study on the use of metformin to reduce olanzapine-associated weight gain, we may state that we will obtain the weight of the patient but fail to explain how we will do it. Better would be to state that the same weighing scale will be used. Still better would be to state that we will use a weighing instrument that works on the principle of moving weights on a levered arm, and that the same instrument will be used for all patients. And best would be to add that we will weigh patients, dressed in standard hospital gowns, after they have voided their bladder but before they have eaten breakfast. When the way in which a variable will be measured is defined, measurement of that variable becomes more objective and uniform

Concluding Notes

The next article, Part 2, will address what categorical and continuous variables are, why continuous variables should not be converted into categorical variables and when this rule can be broken, and what confounding variables are.

Declaration of Conflicting Interests: The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The author received no financial support for the research, authorship, and/or publication of this article.

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Independent Variables in Psychology

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The independent variable (IV) in psychology is the characteristic of an experiment that is manipulated or changed by researchers, not by other variables in the experiment.

For example, in an experiment looking at the effects of studying on test scores, studying would be the independent variable. Researchers are trying to determine if changes to the independent variable (studying) result in significant changes to the dependent variable (the test results).

In general, experiments have these three types of variables: independent, dependent, and controlled.

Identifying the Independent Variable

If you are having trouble identifying the independent variables of an experiment, there are some questions that may help:

• Is the variable one that is being manipulated by the experimenters?
• Are researchers trying to identify how the variable influences another variable?
• Is the variable something that cannot be changed but that is not dependent on other variables in the experiment?

Researchers are interested in investigating the effects of the independent variable on other variables, which are known as dependent variables (DV). The independent variable is one that the researchers either manipulate (such as the amount of something) or that already exists but is not dependent upon other variables (such as the age of the participants).

Below are the key differences when looking at an independent variable vs. dependent variable.

Expected to influence the dependent variable

Doesn't change as a result of the experiment

Can be manipulated by researchers in order to study the dependent variable

Expected to be affected by the independent variable

Expected to change as a result of the experiment

Not manipulated by researchers; its changes occur as a result of the independent variable

There can be all different types of independent variables. The independent variables in a particular experiment all depend on the hypothesis and what the experimenters are investigating.

Independent variables also have different levels. In some experiments, there may only be one level of an IV. In other cases, multiple levels of the IV may be used to look at the range of effects that the variable may have.

In an experiment on the effects of the type of diet on weight loss, for example, researchers might look at several different types of diet. Each type of diet that the experimenters look at would be a different level of the independent variable while weight loss would always be the dependent variable.

To understand this concept, it's helpful to take a look at the independent variable in research examples.

In Organizations

A researcher wants to determine if the color of an office has any effect on worker productivity. In an experiment, one group of workers performs a task in a yellow room while another performs the same task in a blue room. In this example, the color of the office is the independent variable.

In the Workplace

A business wants to determine if giving employees more control over how to do their work leads to increased job satisfaction. In an experiment, one group of workers is given a great deal of input in how they perform their work, while the other group is not. The amount of input the workers have over their work is the independent variable in this example.

In Educational Research

Educators are interested in whether participating in after-school math tutoring can increase scores on standardized math exams. In an experiment, one group of students attends an after-school tutoring session twice a week while another group of students does not receive this additional assistance. In this case, participation in after-school math tutoring is the independent variable.

In Mental Health Research

Researchers want to determine if a new type of treatment will lead to a reduction in anxiety for patients living with social phobia. In an experiment, some volunteers receive the new treatment, another group receives a different treatment, and a third group receives no treatment. The independent variable in this example is the type of therapy .

Sometimes varying the independent variables will result in changes in the dependent variables. In other cases, researchers might find that changes in the independent variables have no effect on the variables that are being measured.

At the outset of an experiment, it is important for researchers to operationally define the independent variable. An operational definition describes exactly what the independent variable is and how it is measured. Doing this helps ensure that the experiments know exactly what they are looking at or manipulating, allowing them to measure it and determine if it is the IV that is causing changes in the DV.

Choosing an Independent Variable

If you are designing an experiment, here are a few tips for choosing an independent variable (or variables):

• Select independent variables that you think will cause changes in another variable. Come up with a hypothesis for what you expect to happen.
• Look at other experiments for examples and identify different types of independent variables.
• Keep your control group and experimental groups similar in other characteristics, but vary only the treatment they receive in terms of the independent variable.   For example, your control group will receive either no treatment or no changes in the independent variable while your experimental group will receive the treatment or a different level of the independent variable.

It is also important to be aware that there may be other variables that might influence the results of an experiment. Two other kinds of variables that might influence the outcome include:

• Extraneous variables : These are variables that might affect the relationships between the independent variable and the dependent variable; experimenters usually try to identify and control for these variables. 
• Confounding variables : When an extraneous variable cannot be controlled for in an experiment, it is known as a confounding variable . 

Extraneous variables can also include demand characteristics (which are clues about how the participants should respond) and experimenter effects (which is when the researchers accidentally provide clues about how a participant will respond).

Kaliyadan F, Kulkarni V. Types of variables, descriptive statistics, and sample size .  Indian Dermatol Online J . 2019;10(1):82-86. doi:10.4103/idoj.IDOJ_468_18

Weiten, W. Psychology: Themes and Variations, 10th ed . Boston, MA: Cengage Learning; 2017.

National Library of Medicine. Dependent and independent variables .

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Definitions

Dependent Variable The variable that depends on other factors that are measured. These variables are expected to change as a result of an experimental manipulation of the independent variable or variables. It is the presumed effect.

Independent Variable The variable that is stable and unaffected by the other variables you are trying to measure. It refers to the condition of an experiment that is systematically manipulated by the investigator. It is the presumed cause.

Cramer, Duncan and Dennis Howitt. The SAGE Dictionary of Statistics . London: SAGE, 2004; Penslar, Robin Levin and Joan P. Porter. Institutional Review Board Guidebook: Introduction . Washington, DC: United States Department of Health and Human Services, 2010; "What are Dependent and Independent Variables?" Graphic Tutorial.

Identifying Dependent and Independent Variables

Don't feel bad if you are confused about what is the dependent variable and what is the independent variable in social and behavioral sciences research . However, it's important that you learn the difference because framing a study using these variables is a common approach to organizing the elements of a social sciences research study in order to discover relevant and meaningful results. Specifically, it is important for these two reasons:

• You need to understand and be able to evaluate their application in other people's research.
• You need to apply them correctly in your own research.

A variable in research simply refers to a person, place, thing, or phenomenon that you are trying to measure in some way. The best way to understand the difference between a dependent and independent variable is that the meaning of each is implied by what the words tell us about the variable you are using. You can do this with a simple exercise from the website, Graphic Tutorial. Take the sentence, "The [independent variable] causes a change in [dependent variable] and it is not possible that [dependent variable] could cause a change in [independent variable]." Insert the names of variables you are using in the sentence in the way that makes the most sense. This will help you identify each type of variable. If you're still not sure, consult with your professor before you begin to write.

Fan, Shihe. "Independent Variable." In Encyclopedia of Research Design. Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 592-594; "What are Dependent and Independent Variables?" Graphic Tutorial; Salkind, Neil J. "Dependent Variable." In Encyclopedia of Research Design , Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 348-349;

Structure and Writing Style

The process of examining a research problem in the social and behavioral sciences is often framed around methods of analysis that compare, contrast, correlate, average, or integrate relationships between or among variables . Techniques include associations, sampling, random selection, and blind selection. Designation of the dependent and independent variable involves unpacking the research problem in a way that identifies a general cause and effect and classifying these variables as either independent or dependent.

The variables should be outlined in the introduction of your paper and explained in more detail in the methods section . There are no rules about the structure and style for writing about independent or dependent variables but, as with any academic writing, clarity and being succinct is most important.

After you have described the research problem and its significance in relation to prior research, explain why you have chosen to examine the problem using a method of analysis that investigates the relationships between or among independent and dependent variables . State what it is about the research problem that lends itself to this type of analysis. For example, if you are investigating the relationship between corporate environmental sustainability efforts [the independent variable] and dependent variables associated with measuring employee satisfaction at work using a survey instrument, you would first identify each variable and then provide background information about the variables. What is meant by "environmental sustainability"? Are you looking at a particular company [e.g., General Motors] or are you investigating an industry [e.g., the meat packing industry]? Why is employee satisfaction in the workplace important? How does a company make their employees aware of sustainability efforts and why would a company even care that its employees know about these efforts?

Identify each variable for the reader and define each . In the introduction, this information can be presented in a paragraph or two when you describe how you are going to study the research problem. In the methods section, you build on the literature review of prior studies about the research problem to describe in detail background about each variable, breaking each down for measurement and analysis. For example, what activities do you examine that reflect a company's commitment to environmental sustainability? Levels of employee satisfaction can be measured by a survey that asks about things like volunteerism or a desire to stay at the company for a long time.

The structure and writing style of describing the variables and their application to analyzing the research problem should be stated and unpacked in such a way that the reader obtains a clear understanding of the relationships between the variables and why they are important. This is also important so that the study can be replicated in the future using the same variables but applied in a different way.

Fan, Shihe. "Independent Variable." In Encyclopedia of Research Design. Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 592-594; "What are Dependent and Independent Variables?" Graphic Tutorial; “Case Example for Independent and Dependent Variables.” ORI Curriculum Examples. U.S. Department of Health and Human Services, Office of Research Integrity; Salkind, Neil J. "Dependent Variable." In Encyclopedia of Research Design , Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 348-349; “Independent Variables and Dependent Variables.” Karl L. Wuensch, Department of Psychology, East Carolina University [posted email exchange]; “Variables.” Elements of Research. Dr. Camille Nebeker, San Diego State University.

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What is an Independent Variable? Importance and Examples

In the realm of scientific research, understanding the relationship between variables is crucial. Independent variables play a fundamental role in experimental design and hypothesis testing. In this article, iLovePhD will delve into the concept of independent variables, explore their significance, and provide relevant examples to facilitate a clear understanding.

What are Independent Variables?

• The Significance of Independent Variables
• Examples of Independent Variables

Learn what independent variables are and why they are important in scientific research. Discover real-life examples that illustrate their role in experimental design and hypothesis testing. Gain a clear understanding of how manipulating independent variables can lead to meaningful conclusions.

Demystifying Independent Variables: Meaning, Importance, and Examples

The independent variable is a key component in scientific experiments. It refers to the factor or condition that researchers manipulate or change to observe its effect on the dependent variable. In other words, the independent variable is the cause, while the dependent variable is the effect being measured.

For example, in a study investigating the impact of sleep duration on cognitive performance, the independent variable would be the sleep duration. Researchers would manipulate the independent variable by assigning different groups of participants to various sleep durations, such as six, eight, or ten hours.

The Significance of Independent Variables Understanding independent variables is essential for several reasons:

A. Control and Causality: By manipulating the independent variable, researchers can exercise control over the experiment and establish a cause-and-effect relationship between variables. This control helps eliminate confounding factors and ensures that any observed effects can be attributed to the independent variable.

B. Replicability: Independent variables are crucial for replicating experiments. When researchers manipulate the same independent variable in multiple experiments, they can examine whether the effects remain consistent. This process strengthens the validity and reliability of scientific findings.

C. Generalization: Independent variables aid in making generalizations about a broader population. By manipulating the independent variable, researchers can study how certain conditions or factors affect a range of individuals or objects, enabling broader insights into various phenomena.

Examples of Independent Variables Let’s explore a few examples of independent variables across different fields:

A. Biology: In a study investigating the effect of fertilizer on plant growth, the independent variable would be the amount of fertilizer applied. Researchers would manipulate this variable by exposing different groups of plants to varying levels of fertilizer concentration.

B. Psychology: To explore the impact of music on mood, researchers may manipulate the independent variable by exposing participants to different genres of music (classical, rock, jazz) and measuring their mood changes using a standardized mood scale.

C. Physics: In an experiment studying the relationship between distance and time for an object in free fall, the independent variable would be the distance. Researchers would manipulate this variable by dropping the object from different heights and measuring the corresponding time it takes to fall.

Independent variables serve as a crucial component of experimental design, allowing researchers to investigate cause-and-effect relationships between variables. By manipulating the independent variable and observing its effects on the dependent variable, researchers can draw meaningful conclusions.

Understanding the role and significance of independent variables is vital for conducting rigorous scientific research and obtaining reliable results.

Also Read: Types of Research Variable in Research with Example

• cause and effect relationship
• control variables
• Data Analysis
• dependent variables
• experimental design
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• independent variables
• manipulating variables
• research conclusions
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Research Variables 101

Independent variables, dependent variables, control variables and more

By: Derek Jansen (MBA) | Expert Reviewed By: Kerryn Warren (PhD) | January 2023

If you’re new to the world of research, especially scientific research, you’re bound to run into the concept of variables , sooner or later. If you’re feeling a little confused, don’t worry – you’re not the only one! Independent variables, dependent variables, confounding variables – it’s a lot of jargon. In this post, we’ll unpack the terminology surrounding research variables using straightforward language and loads of examples .

Overview: Variables In Research

What (exactly) is a variable?

The simplest way to understand a variable is as any characteristic or attribute that can experience change or vary over time or context – hence the name “variable”. For example, the dosage of a particular medicine could be classified as a variable, as the amount can vary (i.e., a higher dose or a lower dose). Similarly, gender, age or ethnicity could be considered demographic variables, because each person varies in these respects.

Within research, especially scientific research, variables form the foundation of studies, as researchers are often interested in how one variable impacts another, and the relationships between different variables. For example:

• How someone’s age impacts their sleep quality
• How different teaching methods impact learning outcomes
• How diet impacts weight (gain or loss)

As you can see, variables are often used to explain relationships between different elements and phenomena. In scientific studies, especially experimental studies, the objective is often to understand the causal relationships between variables. In other words, the role of cause and effect between variables. This is achieved by manipulating certain variables while controlling others – and then observing the outcome. But, we’ll get into that a little later…

The “Big 3” Variables

Variables can be a little intimidating for new researchers because there are a wide variety of variables, and oftentimes, there are multiple labels for the same thing. To lay a firm foundation, we’ll first look at the three main types of variables, namely:

• Independent variables (IV)
• Dependant variables (DV)
• Control variables

What is an independent variable?

Simply put, the independent variable is the “ cause ” in the relationship between two (or more) variables. In other words, when the independent variable changes, it has an impact on another variable.

For example:

• Increasing the dosage of a medication (Variable A) could result in better (or worse) health outcomes for a patient (Variable B)
• Changing a teaching method (Variable A) could impact the test scores that students earn in a standardised test (Variable B)
• Varying one’s diet (Variable A) could result in weight loss or gain (Variable B).

It’s useful to know that independent variables can go by a few different names, including, explanatory variables (because they explain an event or outcome) and predictor variables (because they predict the value of another variable). Terminology aside though, the most important takeaway is that independent variables are assumed to be the “cause” in any cause-effect relationship. As you can imagine, these types of variables are of major interest to researchers, as many studies seek to understand the causal factors behind a phenomenon.

Need a helping hand?

What is a dependent variable?

While the independent variable is the “ cause ”, the dependent variable is the “ effect ” – or rather, the affected variable . In other words, the dependent variable is the variable that is assumed to change as a result of a change in the independent variable.

Keeping with the previous example, let’s look at some dependent variables in action:

• Health outcomes (DV) could be impacted by dosage changes of a medication (IV)
• Students’ scores (DV) could be impacted by teaching methods (IV)
• Weight gain or loss (DV) could be impacted by diet (IV)

In scientific studies, researchers will typically pay very close attention to the dependent variable (or variables), carefully measuring any changes in response to hypothesised independent variables. This can be tricky in practice, as it’s not always easy to reliably measure specific phenomena or outcomes – or to be certain that the actual cause of the change is in fact the independent variable.

As the adage goes, correlation is not causation . In other words, just because two variables have a relationship doesn’t mean that it’s a causal relationship – they may just happen to vary together. For example, you could find a correlation between the number of people who own a certain brand of car and the number of people who have a certain type of job. Just because the number of people who own that brand of car and the number of people who have that type of job is correlated, it doesn’t mean that owning that brand of car causes someone to have that type of job or vice versa. The correlation could, for example, be caused by another factor such as income level or age group, which would affect both car ownership and job type.

To confidently establish a causal relationship between an independent variable and a dependent variable (i.e., X causes Y), you’ll typically need an experimental design , where you have complete control over the environmen t and the variables of interest. But even so, this doesn’t always translate into the “real world”. Simply put, what happens in the lab sometimes stays in the lab!

As an alternative to pure experimental research, correlational or “ quasi-experimental ” research (where the researcher cannot manipulate or change variables) can be done on a much larger scale more easily, allowing one to understand specific relationships in the real world. These types of studies also assume some causality between independent and dependent variables, but it’s not always clear. So, if you go this route, you need to be cautious in terms of how you describe the impact and causality between variables and be sure to acknowledge any limitations in your own research.

What is a control variable?

In an experimental design, a control variable (or controlled variable) is a variable that is intentionally held constant to ensure it doesn’t have an influence on any other variables. As a result, this variable remains unchanged throughout the course of the study. In other words, it’s a variable that’s not allowed to vary – tough life 🙂

As we mentioned earlier, one of the major challenges in identifying and measuring causal relationships is that it’s difficult to isolate the impact of variables other than the independent variable. Simply put, there’s always a risk that there are factors beyond the ones you’re specifically looking at that might be impacting the results of your study. So, to minimise the risk of this, researchers will attempt (as best possible) to hold other variables constant . These factors are then considered control variables.

Some examples of variables that you may need to control include:

• Temperature
• Time of day
• Noise or distractions

Which specific variables need to be controlled for will vary tremendously depending on the research project at hand, so there’s no generic list of control variables to consult. As a researcher, you’ll need to think carefully about all the factors that could vary within your research context and then consider how you’ll go about controlling them. A good starting point is to look at previous studies similar to yours and pay close attention to which variables they controlled for.

Of course, you won’t always be able to control every possible variable, and so, in many cases, you’ll just have to acknowledge their potential impact and account for them in the conclusions you draw. Every study has its limitations , so don’t get fixated or discouraged by troublesome variables. Nevertheless, always think carefully about the factors beyond what you’re focusing on – don’t make assumptions!

Other types of variables

As we mentioned, independent, dependent and control variables are the most common variables you’ll come across in your research, but they’re certainly not the only ones you need to be aware of. Next, we’ll look at a few “secondary” variables that you need to keep in mind as you design your research.

• Moderating variables
• Mediating variables
• Confounding variables
• Latent variables

Let’s jump into it…

What is a moderating variable?

A moderating variable is a variable that influences the strength or direction of the relationship between an independent variable and a dependent variable. In other words, moderating variables affect how much (or how little) the IV affects the DV, or whether the IV has a positive or negative relationship with the DV (i.e., moves in the same or opposite direction).

For example, in a study about the effects of sleep deprivation on academic performance, gender could be used as a moderating variable to see if there are any differences in how men and women respond to a lack of sleep. In such a case, one may find that gender has an influence on how much students’ scores suffer when they’re deprived of sleep.

It’s important to note that while moderators can have an influence on outcomes , they don’t necessarily cause them ; rather they modify or “moderate” existing relationships between other variables. This means that it’s possible for two different groups with similar characteristics, but different levels of moderation, to experience very different results from the same experiment or study design.

What is a mediating variable?

Mediating variables are often used to explain the relationship between the independent and dependent variable (s). For example, if you were researching the effects of age on job satisfaction, then education level could be considered a mediating variable, as it may explain why older people have higher job satisfaction than younger people – they may have more experience or better qualifications, which lead to greater job satisfaction.

Mediating variables also help researchers understand how different factors interact with each other to influence outcomes. For instance, if you wanted to study the effect of stress on academic performance, then coping strategies might act as a mediating factor by influencing both stress levels and academic performance simultaneously. For example, students who use effective coping strategies might be less stressed but also perform better academically due to their improved mental state.

In addition, mediating variables can provide insight into causal relationships between two variables by helping researchers determine whether changes in one factor directly cause changes in another – or whether there is an indirect relationship between them mediated by some third factor(s). For instance, if you wanted to investigate the impact of parental involvement on student achievement, you would need to consider family dynamics as a potential mediator, since it could influence both parental involvement and student achievement simultaneously.

What is a confounding variable?

A confounding variable (also known as a third variable or lurking variable ) is an extraneous factor that can influence the relationship between two variables being studied. Specifically, for a variable to be considered a confounding variable, it needs to meet two criteria:

• It must be correlated with the independent variable (this can be causal or not)
• It must have a causal impact on the dependent variable (i.e., influence the DV)

Some common examples of confounding variables include demographic factors such as gender, ethnicity, socioeconomic status, age, education level, and health status. In addition to these, there are also environmental factors to consider. For example, air pollution could confound the impact of the variables of interest in a study investigating health outcomes.

Naturally, it’s important to identify as many confounding variables as possible when conducting your research, as they can heavily distort the results and lead you to draw incorrect conclusions . So, always think carefully about what factors may have a confounding effect on your variables of interest and try to manage these as best you can.

What is a latent variable?

Latent variables are unobservable factors that can influence the behaviour of individuals and explain certain outcomes within a study. They’re also known as hidden or underlying variables , and what makes them rather tricky is that they can’t be directly observed or measured . Instead, latent variables must be inferred from other observable data points such as responses to surveys or experiments.

For example, in a study of mental health, the variable “resilience” could be considered a latent variable. It can’t be directly measured , but it can be inferred from measures of mental health symptoms, stress, and coping mechanisms. The same applies to a lot of concepts we encounter every day – for example:

• Emotional intelligence
• Quality of life
• Business confidence
• Ease of use

One way in which we overcome the challenge of measuring the immeasurable is latent variable models (LVMs). An LVM is a type of statistical model that describes a relationship between observed variables and one or more unobserved (latent) variables. These models allow researchers to uncover patterns in their data which may not have been visible before, thanks to their complexity and interrelatedness with other variables. Those patterns can then inform hypotheses about cause-and-effect relationships among those same variables which were previously unknown prior to running the LVM. Powerful stuff, we say!

Let’s recap

In the world of scientific research, there’s no shortage of variable types, some of which have multiple names and some of which overlap with each other. In this post, we’ve covered some of the popular ones, but remember that this is not an exhaustive list .

To recap, we’ve explored:

• Independent variables (the “cause”)
• Dependent variables (the “effect”)
• Control variables (the variable that’s not allowed to vary)

If you’re still feeling a bit lost and need a helping hand with your research project, check out our 1-on-1 coaching service , where we guide you through each step of the research journey. Also, be sure to check out our free dissertation writing course and our collection of free, fully-editable chapter templates .

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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Home » Independent Variable – Definition, Types and Examples

Independent Variable – Definition, Types and Examples

Table of Contents

Independent Variable

Definition:

Independent variable is a variable that is manipulated or changed by the researcher to observe its effect on the dependent variable. It is also known as the predictor variable or explanatory variable

The independent variable is the presumed cause in an experiment or study, while the dependent variable is the presumed effect or outcome. The relationship between the independent variable and the dependent variable is often analyzed using statistical methods to determine the strength and direction of the relationship.

Types of Independent Variables

Types of Independent Variables are as follows:

Categorical Independent Variables

These variables are categorical or nominal in nature and represent a group or category. Examples of categorical independent variables include gender, ethnicity, marital status, and educational level.

Continuous Independent Variables

These variables are continuous in nature and can take any value on a continuous scale. Examples of continuous independent variables include age, height, weight, temperature, and blood pressure.

Discrete Independent Variables

These variables are discrete in nature and can only take on specific values. Examples of discrete independent variables include the number of siblings, the number of children in a family, and the number of pets owned.

Binary Independent Variables

These variables are dichotomous or binary in nature, meaning they can take on only two values. Examples of binary independent variables include yes or no questions, such as whether a participant is a smoker or non-smoker.

Controlled Independent Variables

These variables are manipulated or controlled by the researcher to observe their effect on the dependent variable. Examples of controlled independent variables include the type of treatment or therapy given, the dosage of a medication, or the amount of exposure to a stimulus.

Independent Variable and dependent variable Analysis Methods

Following analysis methods that can be used to examine the relationship between an independent variable and a dependent variable:

Correlation Analysis

This method is used to determine the strength and direction of the relationship between two continuous variables. Correlation coefficients such as Pearson’s r or Spearman’s rho are used to quantify the strength and direction of the relationship.

ANOVA (Analysis of Variance)

This method is used to compare the means of two or more groups for a continuous dependent variable. ANOVA can be used to test the effect of a categorical independent variable on a continuous dependent variable.

Regression Analysis

This method is used to examine the relationship between a dependent variable and one or more independent variables. Linear regression is a common type of regression analysis that can be used to predict the value of the dependent variable based on the value of one or more independent variables.

Chi-square Test

This method is used to test the association between two categorical variables. It can be used to examine the relationship between a categorical independent variable and a categorical dependent variable.

This method is used to compare the means of two groups for a continuous dependent variable. It can be used to test the effect of a binary independent variable on a continuous dependent variable.

Measuring Scales of Independent Variable

There are four commonly used Measuring Scales of Independent Variables:

• Nominal Scale : This scale is used for variables that can be categorized but have no inherent order or numerical value. Examples of nominal variables include gender, race, and occupation.
• Ordinal Scale : This scale is used for variables that can be categorized and have a natural order but no specific numerical value. Examples of ordinal variables include levels of education (e.g., high school, bachelor’s degree, master’s degree), socioeconomic status (e.g., low, middle, high), and Likert scales (e.g., strongly disagree, disagree, neutral, agree, strongly agree).
• I nterval Scale : This scale is used for variables that have a numerical value and a consistent unit of measurement but no true zero point. Examples of interval variables include temperature in Celsius or Fahrenheit, IQ scores, and time of day.
• Ratio Scale: This scale is used for variables that have a numerical value, a consistent unit of measurement, and a true zero point. Examples of ratio variables include height, weight, and income.

Independent Variable Examples

Here are some examples of independent variables:

• In a study examining the effects of a new medication on blood pressure, the independent variable would be the medication itself.
• In a study comparing the academic performance of male and female students, the independent variable would be gender.
• In a study investigating the effects of different types of exercise on weight loss, the independent variable would be the type of exercise performed.
• In a study examining the relationship between age and income, the independent variable would be age.
• In a study investigating the effects of different types of music on mood, the independent variable would be the type of music played.
• In a study examining the effects of different teaching strategies on student test scores, the independent variable would be the teaching strategy used.
• In a study investigating the effects of caffeine on reaction time, the independent variable would be the amount of caffeine consumed.
• In a study comparing the effects of two different fertilizers on plant growth, the independent variable would be the type of fertilizer used.

Independent variable vs Dependent variable

Applications of Independent Variable

Applications of Independent Variable in different fields are as follows:

• Scientific experiments : Independent variables are commonly used in scientific experiments to study the cause-and-effect relationships between different variables. By controlling and manipulating the independent variable, scientists can observe how changes in that variable affect the dependent variable.
• Market research: Independent variables are also used in market research to study consumer behavior. For example, researchers may manipulate the price of a product (independent variable) to see how it affects consumer demand (dependent variable).
• Psychology: In psychology, independent variables are often used to study the effects of different treatments or therapies on mental health conditions. For example, researchers may manipulate the type of therapy (independent variable) to see how it affects a patient’s symptoms (dependent variable).
• Education: Independent variables are used in educational research to study the effects of different teaching methods or interventions on student learning outcomes. For example, researchers may manipulate the teaching method (independent variable) to see how it affects student performance on a test (dependent variable).

Purpose of Independent Variable

The purpose of an independent variable is to manipulate or control it in order to observe its effect on the dependent variable. In other words, the independent variable is the variable that is being tested or studied to see if it has an effect on the dependent variable.

The independent variable is often manipulated by the researcher in order to create different experimental conditions. By varying the independent variable, the researcher can observe how the dependent variable changes in response. For example, in a study of the effects of caffeine on memory, the independent variable would be the amount of caffeine consumed, while the dependent variable would be memory performance.

The main purpose of the independent variable is to determine causality. By manipulating the independent variable and observing its effect on the dependent variable, researchers can determine whether there is a causal relationship between the two variables. This is important for understanding how different variables affect each other and for making predictions about how changes in one variable will affect other variables.

When to use Independent Variable

Here are some situations when an independent variable may be used:

• When studying cause-and-effect relationships: Independent variables are often used in studies that aim to establish causal relationships between variables. By manipulating the independent variable and observing the effect on the dependent variable, researchers can determine whether there is a cause-and-effect relationship between the two variables.
• When comparing groups or conditions: Independent variables can also be used to compare groups or conditions. For example, a researcher might manipulate an independent variable (such as a treatment or intervention) and observe the effect on a dependent variable (such as a symptom or behavior) in two different groups of participants (such as a treatment group and a control group).
• When testing hypotheses: Independent variables are used to test hypotheses about how different variables are related. By manipulating the independent variable and observing the effect on the dependent variable, researchers can test whether their hypotheses are supported or not.

Characteristics of Independent Variable

Here are some of the characteristics of independent variables:

• Manipulation: The independent variable is manipulated by the researcher in order to create different experimental conditions. The researcher changes the level or value of the independent variable to observe how it affects the dependent variable.
• Control : The independent variable is controlled by the researcher to ensure that it is the only variable that is changing in the experiment. By controlling other variables that might affect the dependent variable, the researcher can isolate the effect of the independent variable on the dependent variable.
• Categorical or continuous: Independent variables can be either categorical or continuous. Categorical independent variables have distinct categories or levels that are not ordered (e.g., gender, ethnicity), while continuous independent variables are measured on a scale (e.g., age, temperature).
• Treatment : In some experiments, the independent variable represents a treatment or intervention that is being tested. For example, a researcher might manipulate the independent variable by giving participants a new medication or therapy.
• Random assignment : In order to control for extraneous variables and ensure that the independent variable is the only variable that is changing, participants are often randomly assigned to different levels of the independent variable. This helps to ensure that any differences between the groups are not due to pre-existing differences between the participants.

Advantages of Independent Variable

Independent variables have several advantages, including:

• Control : Independent variables allow researchers to control the variables being studied, which helps to establish cause-and-effect relationships. By manipulating the independent variable, researchers can see how changes in that variable affect the dependent variable.
• Replication : Manipulating independent variables allows researchers to replicate studies to confirm or refute previous findings. By controlling the independent variable, researchers can ensure that any differences in the dependent variable are due to the manipulation of the independent variable, rather than other factors.
• Predictive Powe r: Independent variables can be used to predict future outcomes. By examining how changes in the independent variable affect the dependent variable, researchers can make predictions about how the dependent variable will respond in the future.
• Precision : Independent variables can help to increase the precision of a study by allowing researchers to control for extraneous variables that might otherwise confound the results. This can lead to more accurate and reliable findings.
• Generalizability : Independent variables can help to increase the generalizability of a study by allowing researchers to manipulate variables in a way that reflects real-world conditions. This can help to ensure that findings are applicable to a wider range of situations and contexts.

Disadvantages of Independent Variable

Independent variables also have several disadvantages, including:

• Artificiality : In some cases, manipulating the independent variable in a study may create an artificial environment that does not reflect real-world conditions. This can limit the generalizability of the findings.
• Ethical concerns: Manipulating independent variables in some studies may raise ethical concerns, such as when human participants are subjected to potentially harmful or uncomfortable conditions.
• Limitations in measuring variables: Some variables may be difficult or impossible to manipulate in a study. For example, it may be difficult to manipulate someone’s age or gender, which can limit the researcher’s ability to study the effects of these variables.
• Complexity : Some variables may be very complex, making it difficult to determine which variables are independent and which are dependent. This can make it challenging to design a study that effectively examines the relationship between variables.
• Extraneous variables : Even when researchers manipulate the independent variable, other variables may still affect the results. These extraneous variables can confound the results, making it difficult to draw clear conclusions about the relationship between the independent and dependent variables.

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On This Page:

In research, a variable is any characteristic, number, or quantity that can be measured or counted in experimental investigations . One is called the dependent variable, and the other is the independent variable.

In research, the independent variable is manipulated to observe its effect, while the dependent variable is the measured outcome. Essentially, the independent variable is the presumed cause, and the dependent variable is the observed effect.

Variables provide the foundation for examining relationships, drawing conclusions, and making predictions in research studies.

Independent Variable

In psychology, the independent variable is the variable the experimenter manipulates or changes and is assumed to directly affect the dependent variable.

It’s considered the cause or factor that drives change, allowing psychologists to observe how it influences behavior, emotions, or other dependent variables in an experimental setting. Essentially, it’s the presumed cause in cause-and-effect relationships being studied.

For example, allocating participants to drug or placebo conditions (independent variable) to measure any changes in the intensity of their anxiety (dependent variable).

In a well-designed experimental study , the independent variable is the only important difference between the experimental (e.g., treatment) and control (e.g., placebo) groups.

By changing the independent variable and holding other factors constant, psychologists aim to determine if it causes a change in another variable, called the dependent variable.

For example, in a study investigating the effects of sleep on memory, the amount of sleep (e.g., 4 hours, 8 hours, 12 hours) would be the independent variable, as the researcher might manipulate or categorize it to see its impact on memory recall, which would be the dependent variable.

Dependent Variable

In psychology, the dependent variable is the variable being tested and measured in an experiment and is “dependent” on the independent variable.

In psychology, a dependent variable represents the outcome or results and can change based on the manipulations of the independent variable. Essentially, it’s the presumed effect in a cause-and-effect relationship being studied.

An example of a dependent variable is depression symptoms, which depend on the independent variable (type of therapy).

In an experiment, the researcher looks for the possible effect on the dependent variable that might be caused by changing the independent variable.

For instance, in a study examining the effects of a new study technique on exam performance, the technique would be the independent variable (as it is being introduced or manipulated), while the exam scores would be the dependent variable (as they represent the outcome of interest that’s being measured).

Examples in Research Studies

For example, we might change the type of information (e.g., organized or random) given to participants to see how this might affect the amount of information remembered.

In this example, the type of information is the independent variable (because it changes), and the amount of information remembered is the dependent variable (because this is being measured).

For the following hypotheses, name the IV and the DV.

1. Lack of sleep significantly affects learning in 10-year-old boys.

IV……………………………………………………

DV…………………………………………………..

2. Social class has a significant effect on IQ scores.

DV……………………………………………….…

3. Stressful experiences significantly increase the likelihood of headaches.

4. Time of day has a significant effect on alertness.

Operationalizing Variables

To ensure cause and effect are established, it is important that we identify exactly how the independent and dependent variables will be measured; this is known as operationalizing the variables.

Operational variables (or operationalizing definitions) refer to how you will define and measure a specific variable as it is used in your study. This enables another psychologist to replicate your research and is essential in establishing reliability (achieving consistency in the results).

For example, if we are concerned with the effect of media violence on aggression, then we need to be very clear about what we mean by the different terms. In this case, we must state what we mean by the terms “media violence” and “aggression” as we will study them.

Therefore, you could state that “media violence” is operationally defined (in your experiment) as ‘exposure to a 15-minute film showing scenes of physical assault’; “aggression” is operationally defined as ‘levels of electrical shocks administered to a second ‘participant’ in another room.

In another example, the hypothesis “Young participants will have significantly better memories than older participants” is not operationalized. How do we define “young,” “old,” or “memory”? “Participants aged between 16 – 30 will recall significantly more nouns from a list of twenty than participants aged between 55 – 70” is operationalized.

The key point here is that we have clarified what we mean by the terms as they were studied and measured in our experiment.

If we didn’t do this, it would be very difficult (if not impossible) to compare the findings of different studies to the same behavior.

Operationalization has the advantage of generally providing a clear and objective definition of even complex variables. It also makes it easier for other researchers to replicate a study and check for reliability .

For the following hypotheses, name the IV and the DV and operationalize both variables.

1. Women are more attracted to men without earrings than men with earrings.

I.V._____________________________________________________________

D.V. ____________________________________________________________

Operational definitions:

I.V. ____________________________________________________________

2. People learn more when they study in a quiet versus noisy place.

I.V. _________________________________________________________

D.V. ___________________________________________________________

3. People who exercise regularly sleep better at night.

Can there be more than one independent or dependent variable in a study?

Yes, it is possible to have more than one independent or dependent variable in a study.

In some studies, researchers may want to explore how multiple factors affect the outcome, so they include more than one independent variable.

Similarly, they may measure multiple things to see how they are influenced, resulting in multiple dependent variables. This allows for a more comprehensive understanding of the topic being studied.

What are some ethical considerations related to independent and dependent variables?

Ethical considerations related to independent and dependent variables involve treating participants fairly and protecting their rights.

Researchers must ensure that participants provide informed consent and that their privacy and confidentiality are respected. Additionally, it is important to avoid manipulating independent variables in ways that could cause harm or discomfort to participants.

Researchers should also consider the potential impact of their study on vulnerable populations and ensure that their methods are unbiased and free from discrimination.

Ethical guidelines help ensure that research is conducted responsibly and with respect for the well-being of the participants involved.

Can qualitative data have independent and dependent variables?

Yes, both quantitative and qualitative data can have independent and dependent variables.

In quantitative research, independent variables are usually measured numerically and manipulated to understand their impact on the dependent variable. In qualitative research, independent variables can be qualitative in nature, such as individual experiences, cultural factors, or social contexts, influencing the phenomenon of interest.

The dependent variable, in both cases, is what is being observed or studied to see how it changes in response to the independent variable.

So, regardless of the type of data, researchers analyze the relationship between independent and dependent variables to gain insights into their research questions.

Can the same variable be independent in one study and dependent in another?

Yes, the same variable can be independent in one study and dependent in another.

The classification of a variable as independent or dependent depends on how it is used within a specific study. In one study, a variable might be manipulated or controlled to see its effect on another variable, making it independent.

However, in a different study, that same variable might be the one being measured or observed to understand its relationship with another variable, making it dependent.

The role of a variable as independent or dependent can vary depending on the research question and study design.

Educational resources and simple solutions for your research journey

Independent vs Dependent Variables: Definitions & Examples

A variable is an important element of research. It is a characteristic, number, or quantity of any category that can be measured or counted and whose value may change with time or other parameters.  

Variables are defined in different ways in different fields. For instance, in mathematics, a variable is an alphabetic character that expresses a numerical value. In algebra, a variable represents an unknown entity, mostly denoted by a, b, c, x, y, z, etc. In statistics, variables represent real-world conditions or factors. Despite the differences in definitions, in all fields, variables represent the entity that changes and help us understand how one factor may or may not influence another factor.  

Variables in research and statistics are of different types—independent, dependent, quantitative (discrete or continuous), qualitative (nominal/categorical, ordinal), intervening, moderating, extraneous, confounding, control, and composite. In this article we compare the first two types— independent vs dependent variables .  

Table of Contents

What is a variable?  

Researchers conduct experiments to understand the cause-and-effect relationships between various entities. In such experiments, the entities whose values change are called variables. These variables describe the relationships among various factors and help in drawing conclusions in experiments. They help in understanding how some factors influence others. Some examples of variables include age, gender, race, income, weight, etc.   

As mentioned earlier, different types of variables are used in research. Of these, we will compare the most common types— independent vs dependent variables . The independent variable is the cause and the dependent variable is the effect, that is, independent variables influence dependent variables. In research, a dependent variable is the outcome of interest of the study and the independent variable is the factor that may influence the outcome. Let’s explain this with an independent and dependent variable example : In a study to analyze the effect of antibiotic use on microbial resistance, antibiotic use is the independent variable and microbial resistance is the dependent variable because antibiotic use affects microbial resistance.( 1)  

What is an independent variable?  

Here is a list of the important characteristics of independent variables .( 2,3)  

• An independent variable is the factor that is being manipulated in an experiment.  
• In a research study, independent variables affect or influence dependent variables and cause them to change.  
• Independent variables help gather evidence and draw conclusions about the research subject.  
• They’re also called predictors, factors, treatment variables, explanatory variables, and input variables.  
• On graphs, independent variables are usually placed on the X-axis.  
• Example: In a study on the relationship between screen time and sleep problems, screen time is the independent variable because it influences sleep (the dependent variable).  
• In addition, some factors like age are independent variables because other variables such as a person’s income will not change their age.  

Types of independent variables  

Independent variables in research are of the following two types:( 4)  

Quantitative  

Quantitative independent variables differ in amounts or scales. They are numeric and answer questions like “how many” or “how often.”  

Here are a few quantitative independent variables examples :  

• Differences in treatment dosages and frequencies: Useful in determining the appropriate dosage to get the desired outcome.  
• Varying salinities: Useful in determining the range of salinity that organisms can tolerate.  

Qualitative  

Qualitative independent variables are non-numerical variables.  

A few qualitative independent variables examples are listed below:  

• Different strains of a species: Useful in identifying the strain of a crop that is most resistant to a specific disease.  
• Varying methods of how a treatment is administered—oral or intravenous.  

A quantitative variable is represented by actual amounts and a qualitative variable by categories or groups.  

What is a dependent variable ?  

Here are a few characteristics of dependent variables: ( 3)  

• A dependent variable represents a quantity whose value depends on the independent variable and how it is changed.  
• The dependent variable is influenced by the independent variable under various circumstances.  
• It is also known as the response variable and outcome variable.  
• On graphs, dependent variables are placed on the Y-axis.  

Here are a few dependent variable examples :  

• In a study on the effect of exercise on mood, the dependent variable is mood because it may change with exercise.  
• In a study on the effect of pH on enzyme activity, the enzyme activity is the dependent variable because it changes with changing pH.   

Types of dependent variables  

Dependent variables are of two types:( 5)  

Continuous dependent variables

These variables can take on any value within a given range and are measured on a continuous scale, for example, weight, height, temperature, time, distance, etc.  

Categorical or discrete dependent variables

These variables are divided into distinct categories. They are not measured on a continuous scale so only a limited number of values are possible, for example, gender, race, etc.  

Differences between independent and dependent variables  

The following table compares independent vs dependent variables .  

Independent and dependent variable examples  

Listed below are a few examples of research questions from various disciplines and their corresponding independent and dependent variables.( 6)

  Independent vs dependent variables in research  

Experiments usually have at least two variables—independent and dependent. The independent variable is the entity that is being tested and the dependent variable is the result. Classifying independent and dependent variables as discrete and continuous can help in determining the type of analysis that is appropriate in any given research experiment, as shown in the table below. ( 7)  

  Here are some more research questions and their corresponding independent and dependent variables. ( 6)  

  How to identify independent vs dependent variables  

In addition to all the characteristics of independent and dependent variables listed previously, here are few simple steps to identify the variable types in a research question.( 8)  

• Keep in mind that there are no specific words that will always describe dependent and independent variables.  
• If you’re given a paragraph, convert that into a question and identify specific words describing cause and effect.  
• The word representing the cause is the independent variable and that describing the effect is the dependent variable.  

Let’s try out these steps with an example.  

A researcher wants to conduct a study to see if his new weight loss medication performs better than two bestseller alternatives. He wants to randomly select 20 subjects from Richmond, Virginia, aged 20 to 30 years and weighing above 60 pounds. Each subject will be randomly assigned to three treatment groups.  

To identify the independent and dependent variables, we convert this paragraph into a question, as follows: Does the new medication perform better than the alternatives? Here, the medications are the independent variable and their performances or effect on the individuals are the dependent variable.  

Visualizing independent vs dependent variables  

Data visualization is the graphical representation of information by using charts, graphs, and maps. Visualizations help in making data more understandable by making it easier to compare elements, identify trends and relationships (among variables), among other functions.  

Bar graphs, pie charts, and scatter plots are the best methods to graphically represent variables. While pie charts and bar graphs are suitable for depicting categorical data, scatter plots are appropriate for quantitative data. The independent variable is usually placed on the X-axis and the dependent variable on the Y-axis.  

Figure 1 is a scatter plot that depicts the relationship between the number of household members and their monthly grocery expenses. 9 The number of household members is the independent variable and the expenses the dependent variable. The graph shows that as the number of members increases the expenditure also increases.  

Key takeaways   

Let’s summarize the key takeaways about independent vs dependent variables from this article:  

• A variable is any entity being measured in a study.  
• A dependent variable is often the focus of a research study and is the response or outcome. It depends on or varies with changes in other variables.  
• Independent variables cause changes in dependent variables and don’t depend on other variables.  
• An independent variable can influence a dependent variable, but a dependent variable cannot influence an independent variable.  
• An independent variable is the cause and dependent variable is the effect.  

Frequently asked questions  

• What are the different types of variables used in research?  

The following table lists the different types of variables used in research.( 10)  

 2. Why is it important to differentiate between independent vs dependent variables ?  

  Differentiating between independent vs dependent variables is important to ensure the correct application in your own research and also the correct understanding of other studies. An incorrectly framed research question can lead to confusion and inaccurate results. An easy way to differentiate is to identify the cause and effect.  

 3. How are independent and dependent variables used in non-experimental research?  

  So far in this article we talked about variables in relation to experimental research, wherein variables are manipulated or measured to test a hypothesis, that is, to observe the effect on dependent variables. Let’s examine non-experimental research and how variable are used. 11 In non-experimental research, variables are not manipulated but are observed in their natural state. Researchers do not have control over the variables and cannot manipulate them based on their research requirements. For example, a study examining the relationship between income and education level would not manipulate either variable. Instead, the researcher would observe and measure the levels of each variable in the sample population. The level of control researchers have is the major difference between experimental and non-experimental research. Another difference is the causal relationship between the variables. In non-experimental research, it is not possible to establish a causal relationship because other variables may be influencing the outcome.  

  4. Are there any advantages and disadvantages of using independent vs dependent variables ?

  Here are a few advantages and disadvantages of both independent and dependent variables.( 12)

Advantages: 

• Dependent variables are not liable to any form of bias because they cannot be manipulated by researchers or other external factors.  
• Independent variables are easily obtainable and don’t require complex mathematical procedures to be observed, like dependent variables. This is because researchers can easily manipulate these variables or collect the data from respondents.  
• Some independent variables are natural factors and cannot be manipulated. They are also easily obtainable because less time is required for data collection.

Disadvantages: 

• Obtaining dependent variables is a very expensive and effort- and time-intensive process because these variables are obtained from longitudinal research by solving complex equations.  
• Independent variables are prone to researcher and respondent bias because they can be manipulated, and this may affect the study results.  

We hope this article has provided you with an insight into the use and importance of independent vs dependent variables , which can help you effectively use variables in your next research study.    

• Kaliyadan F, Kulkarni V. Types of variables, descriptive statistics, and sample size. Indian Dermatol Online J. 2019 Jan-Feb; 10(1): 82–86. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6362742/  
• What Is an independent variable? (with uses and examples). Indeed website. Accessed March 11, 2024. https://www.indeed.com/career-advice/career-development/what-is-independent-variable  
• Independent and dependent variables: Differences & examples. Statistics by Jim website. Accessed March 10, 2024. https://statisticsbyjim.com/regression/independent-dependent-variables/  
• Independent variable. Biology online website. Accessed March 9, 2024. https://www.biologyonline.com/dictionary/independent-variable#:~:text=The%20independent%20variable%20in%20research,how%20many%20or%20how%20often .  
• Dependent variables: Definition and examples. Clubz Tutoring Services website. Accessed March 10, 2024. https://clubztutoring.com/ed-resources/math/dependent-variable-definitions-examples-6-7-2/  
• Research topics with independent and dependent variables. Good research topics website. Accessed March 12, 2024. https://goodresearchtopics.com/research-topics-with-independent-and-dependent-variables/  
• Levels of measurement and using the correct statistical test. Univariate quantitative methods. Accessed March 14, 2024. https://web.pdx.edu/~newsomj/uvclass/ho_levels.pdf  
• Easiest way to identify dependent and independent variables. Afidated website. Accessed March 15, 2024. https://www.afidated.com/2014/07/how-to-identify-dependent-and.html  
• Choosing data visualizations. Math for the people website. Accessed March 14, 2024. https://web.stevenson.edu/mbranson/m4tp/version1/environmental-racism-choosing-data-visualization.html  
• Trivedi C. Types of variables in scientific research. Concepts Hacked website. Accessed March 15, 2024. https://conceptshacked.com/variables-in-scientific-research/  
• Variables in experimental and non-experimental research. Statistics solutions website. Accessed March 14, 2024. https://www.statisticssolutions.com/variables-in-experimental-and-non-experimental-research/#:~:text=The%20independent%20variable%20would%20be,state%20instead%20of%20manipulating%20them .  
• Dependent vs independent variables: 11 key differences. Formplus website. Accessed March 15, 2024. https://www.formpl.us/blog/dependent-independent-variables

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What Is an Independent Variable? Definition and Examples

The independent variable is the variable that is controlled or changed in a scientific experiment to test its effect on the dependent variable . It doesn’t depend on another variable and isn’t changed by any factors an experimenter is trying to measure. The independent variable is denoted by the letter  x  in an experiment or graph.

INDEPENDENT VARIABLE EXAMPLE

Two classic examples of independent variables are age and time. They may be measured, but not controlled. In experiments, even if measured time isn’t the variable, it may relate to duration or intensity.

For example, a scientist is testing the effect of light and dark on the behavior of moths by turning a light on and off. The independent variable is the amount of light and the moth’s reaction is the dependent variable.

For another example, say you are measuring whether amount of sleep affects test scores. The hours of sleep would be the independent variable while the test scores would be dependent variable.

A change in the independent variable directly causes a change in the dependent variable. If you have a hypothesis written such that you’re looking at whether  x  affects  y , the  x  is always the independent variable and the  y  is the dependent variable.

GRAPHING THE INDEPENDENT VARIABLE

If the dependent and independent variables are plotted on a graph, the x-axis would be the independent variable and the y-axis would be the dependent variable. You can remember this using the DRY MIX acronym, where DRY means dependent or responsive variable is on the y-axis, while MIX means the manipulated or independent variable is on the x-axis.

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Dependent vs. Independent Variables in Research

Introduction

Independent and dependent variables in research, can qualitative data have independent and dependent variables.

Experiments rely on capturing the relationship between independent and dependent variables to understand causal patterns. Researchers can observe what happens when they change a condition in their experiment or if there is any effect at all.

It's important to understand the difference between the independent variable and dependent variable. We'll look at the notion of independent and dependent variables in this article. If you are conducting experimental research, defining the variables in your study is essential for realizing rigorous research .

In experimental research, a variable refers to the phenomenon, person, or thing that is being measured and observed by the researcher. A researcher conducts a study to see how one variable affects another and make assertions about the relationship between different variables.

A typical research question in an experimental study addresses a hypothesized relationship between the independent variable manipulated by the researcher and the dependent variable that is the outcome of interest presumably influenced by the researcher's manipulation.

Take a simple experiment on plants as an example. Suppose you have a control group of plants on one side of a garden and an experimental group of plants on the other side. All things such as sunlight, water, and fertilizer being equal, both plants should be expected to grow at the same rate.

Now imagine that the plants in the experimental group are given a new plant fertilizer under the assumption that they will grow faster. Then you will need to measure the difference in growth between the two groups in your study.

In this case, the independent variable is the type of fertilizer used on your plants while the dependent variable is the rate of growth among your plants. If there is a significant difference in growth between the two groups, then your study provides support to suggest that the fertilizer causes higher rates of plant growth.

What is the key difference between independent and dependent variables?

The independent variable is the element in your study that you intentionally change, which is why it can also be referred to as the manipulated variable.

You manipulate this variable to see how it might affect the other variables you observe, all other factors being equal. This means that you can observe the cause and effect relationships between one independent variable and one or multiple dependent variables.

Independent variables are directly manipulated by the researcher, while dependent variables are not. They are "dependent" because they are affected by the independent variable in the experiment. Researchers can thus study how manipulating the independent variable leads to changes in the main outcome of interest being measured as the dependent variable.

Note that while you can have multiple dependent variables, it is challenging to establish research rigor for multiple independent variables. If you are making so many changes in an experiment, how do you know which change is responsible for the outcome produced by the study? Studying more than one independent variable would require running an experiment for each independent variable to isolate its effects on the dependent variable.

This being said, it is certainly possible to employ a study design that involves multiple independent and dependent variables, as is the case with what is called a factorial experiment. For example, a psychological study examining the effects of sleep and stress levels on work productivity and social interaction would have two independent variables and two dependent variables, respectively.

Such a study would be complex and require careful planning to establish the necessary research rigor , however. If possible, consider narrowing your research to the examination of one independent variable to make it more manageable and easier to understand.

Independent variable examples

Let's consider an experiment in the social studies. Suppose you want to determine the effectiveness of a new textbook compared to current textbooks in a particular school.

The new textbook is supposed to be better, but how can you prove it? Besides all the selling points that the textbook publisher makes, how do you know if the new textbook is any good? A rigorous study examining the effects of the textbook on classroom outcomes is in order.

The textbook given to students makes up the independent variable in your experimental study. The shift from the existing textbooks to the new one represents the manipulation of the independent variable in this study.

Dependent variable examples

In any experiment, the dependent variable is observed to measure how it is affected by changes to the independent variable. Outcomes such as test scores and other performance metrics can make up the data for the dependent variable.

Now that we are changing the textbook in the experiment above, we should examine if there are any effects.

To do this, we will need two classrooms of students. As best as possible, the two sets of students should be of similar proficiency (or at least of similar backgrounds) and placed within similar conditions for teaching and learning (e.g., physical space, lesson planning).

The control group in our study will be one set of students using the existing textbook. By examining their performance, we can establish a baseline. The performance of the experimental group, which is the set of students using the new textbook, can then be compared with the baseline performance.

As a result, the change in the test scores make up the data for our dependent variable. We cannot directly affect how well students perform on the test, but we can conclude from our experiment whether the use of the new textbook might impact students' performance.

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How do you know if a variable is independent or dependent?

We can typically think of an independent variable as something a researcher can directly change. In the above example, we can change the textbook used by the teacher in class. If we're talking about plants, we can change the fertilizer.

Conversely, the dependent variable is something that we do not directly influence or manipulate. Strictly speaking, we cannot directly manipulate a student's performance on a test or the rate of growth of a plant, not without other factors such as new teaching methods or new fertilizer, respectively.

Understanding the distinction between a dependent variable and an independent variable is key to experimental research. Ultimately, the distinction can be reduced to which element in a study has been directly influenced by the researcher.

Other variables

Given the potential complexities encountered in research, there is essential terminology for other variables in any experimental study. You might employ this terminology or encounter them while reading other research.

A control variable is any factor that the researcher tries to keep constant as the independent variable changes. In the plant experiment described earlier in this article, the sunlight and water are each a controlled variable while the type of fertilizer used is the manipulated variable across control and experimental groups.

To ensure research rigor, the researcher needs to keep these control variables constant to dispel any concerns that differences in growth rate were being driven by sunlight or water, as opposed to the fertilizer being used.

Extraneous variables refer to any unwanted influence on the dependent variable that may confound the analysis of the study. For example, if bugs or animals ate the plants in your fertilizer study, this was greatly impact the rates of plant growth. This is why it would be important to control the environment and protect it from such threats.

Finally, independent variables can go by different names such as subject variables or predictor variables. Dependent variables can also be referred to as the responding variable or outcome variable. Whatever the language, they all serve the same role of influencing the dependent variable in an experiment.

The use of the word " variables " is typically associated with quantitative and confirmatory research. Naturalistic qualitative research typically does not employ experimental designs or establish causality. Qualitative research often draws on observations , interviews , focus groups , and other forms of data collection that are allow researchers to study the naturally occurring "messiness" of the social world, rather than controlling all variables to isolate a cause-and-effect relationship.

In limited circumstances, the idea of experimental variables can apply to participant observations in ethnography , where the researcher should be mindful of their influence on the environment they are observing.

However, the experimental paradigm is best left to quantitative studies and confirmatory research questions. Qualitative researchers in the social sciences are oftentimes more interested in observing and describing socially-constructed phenomena rather than testing hypotheses .

Nonetheless, the notion of independent and dependent variables does hold important lessons for qualitative researchers. Even if they don't employ variables in their study design, qualitative researchers often observe how one thing affects another. A theoretical or conceptual framework can then suggest potential cause-and-effect relationships in their study.

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Independent Variables (Definition + 43 Examples)

Have you ever wondered how scientists make discoveries and how researchers come to understand the world around us? A crucial tool in their kit is the concept of the independent variable, which helps them delve into the mysteries of science and everyday life.

An independent variable is a condition or factor that researchers manipulate to observe its effect on another variable, known as the dependent variable. In simpler terms, it’s like adjusting the dials and watching what happens! By changing the independent variable, scientists can see if and how it causes changes in what they are measuring or observing, helping them make connections and draw conclusions.

In this article, we’ll explore the fascinating world of independent variables, journey through their history, examine theories, and look at a variety of examples from different fields.

History of the Independent Variable

Once upon a time, in a world thirsty for understanding, people observed the stars, the seas, and everything in between, seeking to unlock the mysteries of the universe.

The story of the independent variable begins with a quest for knowledge, a journey taken by thinkers and tinkerers who wanted to explain the wonders and strangeness of the world.

Origins of the Concept

The seeds of the idea of independent variables were sown by Sir Francis Galton , an English polymath, in the 19th century. Galton wore many hats—he was a psychologist, anthropologist, meteorologist, and a statistician!

It was his diverse interests that led him to explore the relationships between different factors and their effects. Galton was curious—how did one thing lead to another, and what could be learned from these connections?

As Galton delved into the world of statistical theories , the concept of independent variables started taking shape.

He was interested in understanding how characteristics, like height and intelligence, were passed down through generations.

Galton’s work laid the foundation for later thinkers to refine and expand the concept, turning it into an invaluable tool for scientific research.

Evolution over Time

After Galton’s pioneering work, the concept of the independent variable continued to evolve and grow. Scientists and researchers from various fields adopted and adapted it, finding new ways to use it to make sense of the world.

They discovered that by manipulating one factor (the independent variable), they could observe changes in another (the dependent variable), leading to groundbreaking insights and discoveries.

Through the years, the independent variable became a cornerstone in experimental design . Researchers in fields like physics, biology, psychology, and sociology used it to test hypotheses, develop theories, and uncover the laws that govern our universe.

The idea that originated from Galton’s curiosity had bloomed into a universal key, unlocking doors to knowledge across disciplines.

Importance in Scientific Research

Today, the independent variable stands tall as a pillar of scientific research. It helps scientists and researchers ask critical questions, test their ideas, and find answers. Without independent variables, we wouldn’t have many of the advancements and understandings that we take for granted today.

The independent variable plays a starring role in experiments, helping us learn about everything from the smallest particles to the vastness of space. It helps researchers create vaccines, understand social behaviors, explore ecological systems, and even develop new technologies.

In the upcoming sections, we’ll dive deeper into what independent variables are, how they work, and how they’re used in various fields.

Together, we’ll uncover the magic of this scientific concept and see how it continues to shape our understanding of the world around us.

What is an Independent Variable?

Embarking on the captivating journey of scientific exploration requires us to grasp the essential terms and ideas. It's akin to a treasure hunter mastering the use of a map and compass.

In our adventure through the realm of independent variables, we’ll delve deeper into some fundamental concepts and definitions to help us navigate this exciting world.

Variables in Research

In the grand tapestry of research, variables are the gems that researchers seek. They’re elements, characteristics, or behaviors that can shift or vary in different circumstances.

Picture them as the myriad of ingredients in a chef’s kitchen—each variable can be adjusted or modified to create a myriad of dishes, each with a unique flavor!

Understanding variables is essential as they form the core of every scientific experiment and observational study.

Types of Variables

Independent Variable The star of our story, the independent variable, is the one that researchers change or control to study its effects. It’s like a chef experimenting with different spices to see how each one alters the taste of the soup. The independent variable is the catalyst, the initial spark that sets the wheels of research in motion.

Dependent Variable The dependent variable is the outcome we observe and measure . It’s the altered flavor of the soup that results from the chef’s culinary experiments. This variable depends on the changes made to the independent variable, hence the name!

Observing how the dependent variable reacts to changes helps scientists draw conclusions and make discoveries.

Control Variable Control variables are the unsung heroes of scientific research. They’re the constants, the elements that researchers keep the same to ensure the integrity of the experiment.

Imagine if our chef used a different type of broth each time he experimented with spices—the results would be all over the place! Control variables keep the experiment grounded and help researchers be confident in their findings.

Confounding Variables Imagine a hidden rock in a stream, changing the water’s flow in unexpected ways. Confounding variables are similar—they are external factors that can sneak into experiments and influence the outcome , adding twists to our scientific story.

These variables can blur the relationship between the independent and dependent variables, making the results of the study a bit puzzly. Detecting and controlling these hidden elements helps researchers ensure the accuracy of their findings and reach true conclusions.

There are of course other types of variables, and different ways to manipulate them called " schedules of reinforcement ," but we won't get into that too much here.

Role of the Independent Variable

Manipulation When researchers manipulate the independent variable, they are orchestrating a symphony of cause and effect. They’re adjusting the strings, the brass, the percussion, observing how each change influences the melody—the dependent variable.

This manipulation is at the heart of experimental research. It allows scientists to explore relationships, unravel patterns, and unearth the secrets hidden within the fabric of our universe.

Observation With every tweak and adjustment made to the independent variable, researchers are like seasoned detectives, observing the dependent variable for changes, collecting clues, and piecing together the puzzle.

Observing the effects and changes that occur helps them deduce relationships, formulate theories, and expand our understanding of the world. Every observation is a step towards solving the mysteries of nature and human behavior.

Identifying Independent Variables

Characteristics Identifying an independent variable in the vast landscape of research can seem daunting, but fear not! Independent variables have distinctive characteristics that make them stand out.

They’re the elements that are deliberately changed or controlled in an experiment to study their effects on the dependent variable. Recognizing these characteristics is like learning to spot footprints in the sand—it leads us to the heart of the discovery!

In Different Types of Research The world of research is diverse and varied, and the independent variable dons many guises! In the field of medicine, it might manifest as the dosage of a drug administered to patients.

In psychology, it could take the form of different learning methods applied to study memory retention. In each field, identifying the independent variable correctly is the golden key that unlocks the treasure trove of knowledge and insights.

As we forge ahead on our enlightening journey, equipped with a deeper understanding of independent variables and their roles, we’re ready to delve into the intricate theories and diverse examples that underscore their significance.

Independent Variables in Research

Now that we’re acquainted with the basic concepts and have the tools to identify independent variables, let’s dive into the fascinating ocean of theories and frameworks.

These theories are like ancient scrolls, providing guidelines and blueprints that help scientists use independent variables to uncover the secrets of the universe.

Scientific Method

What is it and How Does it Work? The scientific method is like a super-helpful treasure map that scientists use to make discoveries. It has steps we follow: asking a question, researching, guessing what will happen (that's a hypothesis!), experimenting, checking the results, figuring out what they mean, and telling everyone about it.

Our hero, the independent variable, is the compass that helps this adventure go the right way!

How Independent Variables Lead the Way In the scientific method, the independent variable is like the captain of a ship, leading everyone through unknown waters.

Scientists change this variable to see what happens and to learn new things. It’s like having a compass that points us towards uncharted lands full of knowledge!

Experimental Design

The Basics of Building Constructing an experiment is like building a castle, and the independent variable is the cornerstone. It’s carefully chosen and manipulated to see how it affects the dependent variable. Researchers also identify control and confounding variables, ensuring the castle stands strong, and the results are reliable.

Keeping Everything in Check In every experiment, maintaining control is key to finding the treasure. Scientists use control variables to keep the conditions consistent, ensuring that any changes observed are truly due to the independent variable. It’s like ensuring the castle’s foundation is solid, supporting the structure as it reaches for the sky.

Hypothesis Testing

Making Educated Guesses Before they start experimenting, scientists make educated guesses called hypotheses . It’s like predicting which X marks the spot of the treasure! It often includes the independent variable and the expected effect on the dependent variable, guiding researchers as they navigate through the experiment.

Independent Variables in the Spotlight When testing these guesses, the independent variable is the star of the show! Scientists change and watch this variable to see if their guesses were right. It helps them figure out new stuff and learn more about the world around us!

Statistical Analysis

Figuring Out Relationships After the experimenting is done, it’s time for scientists to crack the code! They use statistics to understand how the independent and dependent variables are related and to uncover the hidden stories in the data.

Experimenters have to be careful about how they determine the validity of their findings, which is why they use statistics. Something called "experimenter bias" can get in the way of having true (valid) results, because it's basically when the experimenter influences the outcome based on what they believe to be true (or what they want to be true!).

How Important are the Discoveries? Through statistical analysis, scientists determine the significance of their findings. It’s like discovering if the treasure found is made of gold or just shiny rocks. The analysis helps researchers know if the independent variable truly had an effect, contributing to the rich tapestry of scientific knowledge.

As we uncover more about how theories and frameworks use independent variables, we start to see how awesome they are in helping us learn more about the world. But we’re not done yet!

Up next, we’ll look at tons of examples to see how independent variables work their magic in different areas.

Examples of Independent Variables

Independent variables take on many forms, showcasing their versatility in a range of experiments and studies. Let’s uncover how they act as the protagonists in numerous investigations and learning quests!

Science Experiments

1) plant growth.

Consider an experiment aiming to observe the effect of varying water amounts on plant height. In this scenario, the amount of water given to the plants is the independent variable!

2) Freezing Water

Suppose we are curious about the time it takes for water to freeze at different temperatures. The temperature of the freezer becomes the independent variable as we adjust it to observe the results!

3) Light and Shadow

Have you ever observed how shadows change? In an experiment, adjusting the light angle to observe its effect on an object’s shadow makes the angle of light the independent variable!

4) Medicine Dosage

In medical studies, determining how varying medicine dosages influence a patient’s recovery is essential. Here, the dosage of the medicine administered is the independent variable!

5) Exercise and Health

Researchers might examine the impact of different exercise forms on individuals’ health. The various exercise forms constitute the independent variable in this study!

6) Sleep and Wellness

Have you pondered how the sleep duration affects your well-being the following day? In such research, the hours of sleep serve as the independent variable!

7) Learning Methods

Psychologists might investigate how diverse study methods influence test outcomes. Here, the different study methods adopted by students are the independent variable!

8) Mood and Music

Have you experienced varied emotions with different music genres? The genre of music played becomes the independent variable when researching its influence on emotions!

9) Color and Feelings

Suppose researchers are exploring how room colors affect individuals’ emotions. In this case, the room colors act as the independent variable!

Environment

10) rainfall and plant life.

Environmental scientists may study the influence of varying rainfall levels on vegetation. In this instance, the amount of rainfall is the independent variable!

11) Temperature and Animal Behavior

Examining how temperature variations affect animal behavior is fascinating. Here, the varying temperatures serve as the independent variable!

12) Pollution and Air Quality

Investigating the effects of different pollution levels on air quality is crucial. In such studies, the pollution level is the independent variable!

13) Internet Speed and Productivity

Researchers might explore how varying internet speeds impact work productivity. In this exploration, the internet speed is the independent variable!

14) Device Type and User Experience

Examining how different devices affect user experience is interesting. Here, the type of device used is the independent variable!

15) Software Version and Performance

Suppose a study aims to determine how different software versions influence system performance. The software version becomes the independent variable!

16) Teaching Style and Student Engagement

Educators might investigate the effect of varied teaching styles on student engagement. In such a study, the teaching style is the independent variable!

17) Class Size and Learning Outcome

Researchers could explore how different class sizes influence students’ learning. Here, the class size is the independent variable!

18) Homework Frequency and Academic Achievement

Examining the relationship between the frequency of homework assignments and academic success is essential. The frequency of homework becomes the independent variable!

19) Telescope Type and Celestial Observation

Astronomers might study how different telescopes affect celestial observation. In this scenario, the telescope type is the independent variable!

20) Light Pollution and Star Visibility

Investigating the influence of varying light pollution levels on star visibility is intriguing. Here, the level of light pollution is the independent variable!

21) Observation Time and Astronomical Detail

Suppose a study explores how observation duration affects the detail captured in astronomical images. The duration of observation serves as the independent variable!

22) Community Size and Social Interaction

Sociologists may examine how the size of a community influences social interactions. In this research, the community size is the independent variable!

23) Cultural Exposure and Social Tolerance

Investigating the effect of diverse cultural exposure on social tolerance is vital. Here, the level of cultural exposure is the independent variable!

24) Economic Status and Educational Attainment

Researchers could explore how different economic statuses impact educational achievements. In such studies, economic status is the independent variable!

25) Training Intensity and Athletic Performance

Sports scientists might study how varying training intensities affect athletes’ performance. In this case, the training intensity is the independent variable!

26) Equipment Type and Player Safety

Examining the relationship between different sports equipment and player safety is crucial. Here, the type of equipment used is the independent variable!

27) Team Size and Game Strategy

Suppose researchers are investigating how the size of a sports team influences game strategy. The team size becomes the independent variable!

28) Diet Type and Health Outcome

Nutritionists may explore the impact of various diets on individuals’ health. In this exploration, the type of diet followed is the independent variable!

29) Caloric Intake and Weight Change

Investigating how different caloric intakes influence weight change is essential. In such a study, the caloric intake is the independent variable!

30) Food Variety and Nutrient Absorption

Researchers could examine how consuming a variety of foods affects nutrient absorption. Here, the variety of foods consumed is the independent variable!

Real-World Examples of Independent Variables

Isn't it fantastic how independent variables play such an essential part in so many studies? But the excitement doesn't stop there!

Now, let’s explore how findings from these studies, led by independent variables, make a big splash in the real world and improve our daily lives!

Healthcare Advancements

31) treatment optimization.

By studying different medicine dosages and treatment methods as independent variables, doctors can figure out the best ways to help patients recover quicker and feel better. This leads to more effective medicines and treatment plans!

32) Lifestyle Recommendations

Researching the effects of sleep, exercise, and diet helps health experts give us advice on living healthier lives. By changing these independent variables, scientists uncover the secrets to feeling good and staying well!

Technological Innovations

33) speeding up the internet.

When scientists explore how different internet speeds affect our online activities, they’re able to develop technologies to make the internet faster and more reliable. This means smoother video calls and quicker downloads!

34) Improving User Experience

By examining how we interact with various devices and software, researchers can design technology that’s easier and more enjoyable to use. This leads to cooler gadgets and more user-friendly apps!

Educational Strategies

35) enhancing learning.

Investigating different teaching styles, class sizes, and study methods helps educators discover what makes learning fun and effective. This research shapes classrooms, teaching methods, and even homework!

36) Tailoring Student Support

By studying how students with diverse needs respond to different support strategies, educators can create personalized learning experiences. This means every student gets the help they need to succeed!

Environmental Protection

37) conserving nature.

Researching how rainfall, temperature, and pollution affect the environment helps scientists suggest ways to protect our planet. By studying these independent variables, we learn how to keep nature healthy and thriving!

38) Combating Climate Change

Scientists studying the effects of pollution and human activities on climate change are leading the way in finding solutions. By exploring these independent variables, we can develop strategies to combat climate change and protect the Earth!

Social Development

39) building stronger communities.

Sociologists studying community size, cultural exposure, and economic status help us understand what makes communities happy and united. This knowledge guides the development of policies and programs for stronger societies!

40) Promoting Equality and Tolerance

By exploring how exposure to diverse cultures affects social tolerance, researchers contribute to fostering more inclusive and harmonious societies. This helps build a world where everyone is respected and valued!

Enhancing Sports Performance

41) optimizing athlete training.

Sports scientists studying training intensity, equipment type, and team size help athletes reach their full potential. This research leads to better training programs, safer equipment, and more exciting games!

42) Innovating Sports Strategies

By investigating how different game strategies are influenced by various team compositions, researchers contribute to the evolution of sports. This means more thrilling competitions and matches for us to enjoy!

Nutritional Well-Being

43) guiding healthy eating.

Nutritionists researching diet types, caloric intake, and food variety help us understand what foods are best for our bodies. This knowledge shapes dietary guidelines and helps us make tasty, yet nutritious, meal choices!

44) Promoting Nutritional Awareness

By studying the effects of different nutrients and diets, researchers educate us on maintaining a balanced diet. This fosters a greater awareness of nutritional well-being and encourages healthier eating habits!

As we journey through these real-world applications, we witness the incredible impact of studies featuring independent variables. The exploration doesn’t end here, though!

Let’s continue our adventure and see how we can identify independent variables in our own observations and inquiries! Keep your curiosity alive, and let’s delve deeper into the exciting realm of independent variables!

Identifying Independent Variables in Everyday Scenarios

So, we’ve seen how independent variables star in many studies, but how about spotting them in our everyday life?

Recognizing independent variables can be like a treasure hunt – you never know where you might find one! Let’s uncover some tips and tricks to identify these hidden gems in various situations.

1) Asking Questions

One of the best ways to spot an independent variable is by asking questions! If you’re curious about something, ask yourself, “What am I changing or manipulating in this situation?” The thing you’re changing is likely the independent variable!

For example, if you’re wondering whether the amount of sunlight affects how quickly your laundry dries, the sunlight amount is your independent variable!

2) Making Observations

Keep your eyes peeled and observe the world around you! By watching how changes in one thing (like the amount of rain) affect something else (like the height of grass), you can identify the independent variable.

In this case, the amount of rain is the independent variable because it’s what’s changing!

3) Conducting Experiments

Get hands-on and conduct your own experiments! By changing one thing and observing the results, you’re identifying the independent variable.

If you’re growing plants and decide to water each one differently to see the effects, the amount of water is your independent variable!

4) Everyday Scenarios

In everyday scenarios, independent variables are all around!

When you adjust the temperature of your oven to bake cookies, the oven temperature is the independent variable.

Or if you’re deciding how much time to spend studying for a test, the study time is your independent variable!

5) Being Curious

Keep being curious and asking “What if?” questions! By exploring different possibilities and wondering how changing one thing could affect another, you’re on your way to identifying independent variables.

If you’re curious about how the color of a room affects your mood, the room color is the independent variable!

6) Reviewing Past Studies

Don’t forget about the treasure trove of past studies and experiments! By reviewing what scientists and researchers have done before, you can learn how they identified independent variables in their work.

This can give you ideas and help you recognize independent variables in your own explorations!

Exercises for Identifying Independent Variables

Ready for some practice? Let’s put on our thinking caps and try to identify the independent variables in a few scenarios.

Remember, the independent variable is what’s being changed or manipulated to observe the effect on something else! (You can see the answers below)

Scenario One: Cooking Time

You’re cooking pasta for dinner and want to find out how the cooking time affects its texture. What is the independent variable?

Scenario Two: Exercise Routine

You decide to try different exercise routines each week to see which one makes you feel the most energetic. What is the independent variable?

Scenario Three: Plant Fertilizer

You’re growing tomatoes in your garden and decide to use different types of fertilizer to see which one helps them grow the best. What is the independent variable?

Scenario Four: Study Environment

You’re preparing for an important test and try studying in different environments (quiet room, coffee shop, library) to see where you concentrate best. What is the independent variable?

Scenario Five: Sleep Duration

You’re curious to see how the number of hours you sleep each night affects your mood the next day. What is the independent variable?

By practicing identifying independent variables in different scenarios, you’re becoming a true independent variable detective. Keep practicing, stay curious, and you’ll soon be spotting independent variables everywhere you go.

Independent Variable: The cooking time is the independent variable. You are changing the cooking time to observe its effect on the texture of the pasta.

Independent Variable: The type of exercise routine is the independent variable. You are trying out different exercise routines each week to see which one makes you feel the most energetic.

Independent Variable: The type of fertilizer is the independent variable. You are using different types of fertilizer to observe their effects on the growth of the tomatoes.

Independent Variable: The study environment is the independent variable. You are studying in different environments to see where you concentrate best.

Independent Variable: The number of hours you sleep is the independent variable. You are changing your sleep duration to see how it affects your mood the next day.

Whew, what a journey we’ve had exploring the world of independent variables! From understanding their definition and role to diving into a myriad of examples and real-world impacts, we’ve uncovered the treasures hidden in the realm of independent variables.

The beauty of independent variables lies in their ability to unlock new knowledge and insights, guiding us to discoveries that improve our lives and the world around us.

By identifying and studying these variables, we embark on exciting learning adventures, solving mysteries and answering questions about the universe we live in.

Remember, the joy of discovery doesn’t end here. The world is brimming with questions waiting to be answered and mysteries waiting to be solved.

Keep your curiosity alive, continue exploring, and who knows what incredible discoveries lie ahead.

Related posts:

• Confounding Variable in Psychology (Examples + Definition)
• 19+ Experimental Design Examples (Methods + Types)
• Variable Interval Reinforcement Schedule (Examples)
• Variable Ratio Reinforcement Schedule (Examples)
• State Dependent Memory + Learning (Definition and Examples)

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Roles of Independent and Dependent Variables in Research

Explore the essential roles of independent and dependent variables in research. This guide delves into their definitions, significance in experiments, and their critical relationship. Learn how these variables are the foundation of research design, influencing hypothesis testing, theory development, and statistical analysis, empowering researchers to understand and predict outcomes of research studies.

Table of Contents

Introduction.

At the very base of scientific inquiry and research design , variables act as the fundamental steps, guiding the rhythm and direction of research. This is particularly true in human behavior research, where the quest to understand the complexities of human actions and reactions hinges on the meticulous manipulation and observation of these variables. At the heart of this endeavor lie two different types of variables, namely: independent and dependent variables, whose roles and interplay are critical in scientific discovery.

Understanding the distinction between independent and dependent variables is not merely an academic exercise; it is essential for anyone venturing into the field of research. This article aims to demystify these concepts, offering clarity on their definitions, roles, and the nuances of their relationship in the study of human behavior, and in science generally. We will cover hypothesis testing and theory development, illuminating how these variables serve as the cornerstone of experimental design and statistical analysis.

The significance of grasping the difference between independent and dependent variables extends beyond the confines of academia. It empowers researchers to design robust studies, enables critical evaluation of research findings, and fosters an appreciation for the complexity of human behavior research. As we delve into this exploration, our objective is clear: to equip readers with a deep understanding of these fundamental concepts, enhancing their ability to contribute to the ever-evolving field of human behavior research.

Chapter 1: The Role of Independent Variables in Human Behavior Research

In the realm of human behavior research, independent variables are the keystones around which studies are designed and hypotheses are tested. Independent variables are the factors or conditions that researchers manipulate or observe to examine their effects on dependent variables, which typically reflect aspects of human behavior or psychological phenomena. Understanding the role of independent variables is crucial for designing robust research methodologies, ensuring the reliability and validity of findings.

Defining Independent Variables

Independent variables are those variables that are changed or controlled in a scientific experiment to test the effects on dependent variables. In studies focusing on human behavior, these can range from psychological interventions (e.g., cognitive-behavioral therapy), environmental adjustments (e.g., noise levels, lighting, smells, etc), to societal factors (e.g., social media use). For example, in an experiment investigating the impact of sleep on cognitive performance, the amount of sleep participants receive is the independent variable. 

Selection and Manipulation

Selecting an independent variable requires careful consideration of the research question and the theoretical framework guiding the study. Researchers must ensure that their chosen variable can be effectively, and consistently manipulated or measured and is ethically and practically feasible, particularly when dealing with human subjects.

Manipulating an independent variable involves creating different conditions (e.g., treatment vs. control groups) to observe how changes in the variable affect outcomes. For instance, researchers studying the effect of educational interventions on learning outcomes might vary the type of instructional material (digital vs. traditional) to assess differences in student performance.

Challenges in Human Behavior Research

Manipulating independent variables in human behavior research presents unique challenges. Ethical considerations are paramount, as interventions must not harm participants. For example, studies involving vulnerable populations or sensitive topics require rigorous ethical oversight to ensure that the manipulation of independent variables does not result in adverse effects.

Practical limitations also come into play, such as controlling for extraneous variables that could influence the outcomes. In the aforementioned example of sleep and cognitive performance, factors like caffeine consumption or stress levels could confound the results. Researchers employ various methodological strategies, such as random assignment and controlled environments, to mitigate these influences.

Chapter 2: Dependent Variables: Measuring Human Behavior

The dependent variable in human behavior research acts as a mirror, reflecting the outcomes or effects resulting from variations in the independent variable. It is the aspect of human experience or behavior that researchers aim to understand, predict, or change through their studies. This section explores how dependent variables are measured, the significance of their accurate measurement, and the inherent challenges in capturing the complexities of human behavior.

Defining Dependent Variables

Dependent variables are the responses or outcomes that researchers measure in an experiment, expecting them to vary as a direct result of changes in the independent variable. In the context of human behavior research, dependent variables could include measures of emotional well-being, cognitive performance, social interactions, or any other aspect of human behavior influenced by the experimental manipulation. For instance, in a study examining the effect of exercise on stress levels, stress level would be the dependent variable, measured through various psychological assessments or physiological markers.

Measurement Methods and Tools

Measuring dependent variables in human behavior research involves a diverse array of methodologies, ranging from self-reported questionnaires and interviews to physiological measurements and behavioral observations. The choice of measurement tool depends on the nature of the dependent variable and the objectives of the study.

• Self-reported Measures: Often used for assessing psychological states or subjective experiences, such as anxiety, satisfaction, or mood. These measures rely on participants’ introspection and honesty, posing challenges in terms of accuracy and bias.
• Behavioral Observations: Involve the direct observation and recording of participants’ behavior in natural or controlled settings. This method is used for behaviors that can be externally observed and quantified, such as social interactions or task performance.
• Physiological Measurements: Include the use of technology to measure physical responses that indicate psychological states, such as heart rate, cortisol levels, or brain activity. These measures can provide objective data about the physiological aspects of human behavior.

Reliability and Validity

The reliability and validity of the measurement of dependent variables are critical to the integrity of human behavior research.

• Reliability refers to the consistency of a measure; a reliable tool yields similar results under consistent conditions.
• Validity pertains to the accuracy of the measure; a valid tool accurately reflects the concept it aims to measure.

Ensuring reliability and validity often involves the use of established measurement instruments with proven track records, pilot testing new instruments, and applying rigorous statistical analyses to evaluate measurement properties.

Challenges in Measuring Human Behavior

Measuring human behavior presents challenges due to its complexity and the influence of multiple, often interrelated, variables. Researchers must contend with issues such as participant bias, environmental influences, and the subjective nature of many psychological constructs. Additionally, the dynamic nature of human behavior means that it can change over time, necessitating careful consideration of when and how measurements are taken.

Section 3: Relationship between Independent and Dependent Variables

Understanding the relationship between independent and dependent variables is at the core of research in human behavior. This relationship is what researchers aim to elucidate, whether they seek to explain, predict, or influence human actions and psychological states. This section explores the nature of this relationship, the means by which it is analyzed, and common misconceptions that may arise.

The Nature of the Relationship

The relationship between independent and dependent variables can manifest in various forms—direct, indirect, linear, nonlinear, and may be moderated or mediated by other variables. At its most basic, this relationship is often conceptualized as cause and effect: the independent variable (the cause) influences the dependent variable (the effect). For instance, increased physical activity (independent variable) may lead to decreased stress levels (dependent variable).

Analyzing the Relationship

Statistical analyses play a pivotal role in examining the relationship between independent and dependent variables. Techniques vary depending on the nature of the variables and the research design, ranging from simple correlation and regression analyses for quantifying the strength and form of relationships, to complex multivariate analyses for exploring relationships among multiple variables simultaneously.

• Correlation Analysis : Used to determine the degree to which two variables are related. However, it’s crucial to note that correlation does not imply causation.
• Regression Analysis : Goes a step further by not only assessing the strength of the relationship but also predicting the value of the dependent variable based on the independent variable.
• Experimental Design : Provides a more robust framework for inferring causality, where manipulation of the independent variable and control of confounding factors allow researchers to directly observe the impact on the dependent variable.

Causality vs. Correlation

A fundamental consideration in human behavior research is the distinction between causality and correlation. Causality implies that changes in the independent variable cause changes in the dependent variable. Correlation, on the other hand, indicates that two variables are related but does not establish a cause-effect relationship. Confounding variables may influence both, creating the appearance of a direct relationship where none exists. Understanding this distinction is crucial for accurate interpretation of research findings.

Common Misinterpretations

The complexity of human behavior and the myriad factors that influence it often lead to challenges in interpreting the relationship between independent and dependent variables. Researchers must be wary of:

• Overestimating the strength of causal relationships based on correlational data.
• Ignoring potential confounding variables that may influence the observed relationship.
• Assuming the directionality of the relationship without adequate evidence.

This exploration highlights the importance of understanding independent and dependent variables in human behavior research. Independent variables act as the initiating factors in experiments, influencing the observed behaviors, while dependent variables reflect the results of these influences, providing insights into human emotions and actions. 

Ethical and practical challenges arise, especially in experiments involving human participants, necessitating careful consideration to respect participants’ well-being. The measurement of these variables is critical for testing theories and validating hypotheses, with their relationship offering potential insights into causality and correlation within human behavior. 

Rigorous statistical analysis and cautious interpretation of findings are essential to avoid misconceptions. Overall, the study of these variables is fundamental to advancing human behavior research, guiding researchers towards deeper understanding and potential interventions to improve the human condition.

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The Essential Guide to Independent and Dependent Variables in Data Analysis

You will learn the critical differences and applications of independent and dependent variables in data science.

Introduction

In data analysis, independent and dependent variables are the backbone of understanding how various elements interact within a study. Whether you’re a student stepping into the world of research, a seasoned data scientist, or a professional analyzing business trends, grasping the roles of these variables is crucial.

Independent variables, often predictors or causes, are the factors that we expect to influence outcomes. They are the variables that researchers manipulate or select in an experiment to observe their effect on other variables. On the other hand, dependent variables are those outcomes or effects that are influenced or changed due to the manipulation of the independent variables. They are what researchers measure in an experiment.

The distinction and interaction between these two variables are foundational across diverse research fields – from psychological studies to biological experiments and from market research to technological advancements. Their correct identification and application determine a study’s direction and the validity of its conclusions. This guide aims to demystify these concepts, highlighting their critical roles in experimental design and data analysis. As we delve into the specifics of independent and dependent variables, you will gain insights essential for aspiring or professional data analysts.

• Independent variables are the predictors or causes in a study, shaping the outcomes.
• Dependent variables change in response to the independent variable’s influence.
• The relationship between these variables is foundational in experimental designs.
• Misidentifying these variables can lead to incorrect data interpretations.
• These variables are essential in regression analysis, determining causation.

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Understanding Independent Variables

Defining independent variables in research.

Independent variables stand at the forefront of experimentation and analysis in the research world. These are the variables that researchers actively manipulate or choose to observe their impact on other variables, commonly known as dependent variables. The role of an independent variable is to provide a basis for comparison and to drive the experiment or study forward. Its manipulation or variation allows researchers to observe changes, draw conclusions, and predict the behavior of the dependent variables.

Independent Variables in Various Contexts

The nature of independent variables can vary greatly depending on the field of study. For example, in a clinical trial, the independent variable might be a new medication or treatment method. In a psychological study, it could be a specific therapeutic intervention. In economics, it might be a change in interest rates. These examples illustrate how independent variables are not confined to any discipline but are fundamental to research across all science and social science domains.

The Importance of Correct Identification

Correctly identifying the independent variable in a study is a critical step in research design. Misidentification can lead to flawed experiments and inaccurate conclusions. It is the influence or change of the independent variable that researchers seek to understand about the dependent variable. This relationship is the cornerstone of hypothesis testing, where researchers form predictions about how changes in the independent variable will affect the dependent variable. Therefore, accurately identifying the independent variable directly impacts the validity and reliability of the research findings.

Exploring Dependent Variables

Defining dependent variables and their distinction from independent variables.

In the data analysis landscape, dependent variables emerge as the responses or effects influenced by independent variables. These are the outcomes that researchers measure and analyze to understand the impact of changes in the independent variables. Unlike independent variables, which are manipulated or chosen by the researcher, dependent variables are observed to see how they respond to these manipulations. This distinction is crucial as it sets the stage for effective research design and data interpretation.

Examples of Dependent Variables Across Different Fields

Dependent variables manifest in various forms across different research disciplines. In a medical study, a dependent variable could be the patient’s response to a treatment, measured in terms of recovery rates or symptom reduction. In an educational setting, student performance scores can be a dependent variable, changing in response to different teaching methods (the independent variable). In environmental research, a lake’s pollution level could be dependent on factors like industrial activity. These examples underscore the breadth of dependent variables’ applicability, showcasing their pivotal role in diverse research contexts.

Implications of Dependent Variables in Data Interpretation

The correct interpretation of dependent variables is a cornerstone of research. Through these variables, the effectiveness or impact of the independent variable is gauged. Misinterpretation or incorrect measurement of dependent variables can lead to faulty conclusions, potentially skewing the entire outcome of a study. Hence, understanding the nature, variability, and response patterns of dependent variables is imperative. Researchers must rigorously analyze these variables to draw reliable and valid conclusions, advancing knowledge in their field of study.

The Relationship Between Independent and Dependent Variables

Interaction of independent and dependent variables in research.

The interaction between independent and dependent variables forms the crux of scientific inquiry and data analysis. This interaction is a simple cause-and-effect relationship and a nuanced interplay that shapes research outcomes. Researchers manipulate or alter independent variables to observe their effect on dependent variables. The response of the dependent variable to these manipulations reveals critical insights, enabling researchers to understand and quantify the relationship between the two.

Significance in Experimental Design

In experimental design, the relationship between independent and dependent variables is paramount. This relationship directs the structure of the experiment, influencing everything from the hypothesis formation to the method of data collection and analysis. The clarity of this relationship determines the experiment’s ability to test hypotheses accurately and yield meaningful results. It also influences the choice of statistical methods used for analysis, as different types of relationships may require different analytical approaches.

Practical Examples and Case Studies

To illustrate this relationship, consider a study in agricultural science where the growth of a crop (dependent variable) is analyzed in response to different fertilizer types (independent variable). Another example is psychology, where a researcher might examine the impact of therapy methods (independent variable) on patient stress levels (dependent variable). These practical examples highlight how the interplay between independent and dependent variables is critical in deriving conclusions and advancing knowledge in various fields.

Common Misconceptions and Pitfalls

Addressing common misunderstandings about independent and dependent variables.

One prevalent misconception is that independent and dependent variables are inherently related in a causal relationship. While this can be true in experimental designs, it is not a universal rule. In observational studies, these variables may show correlation without causation. Another standard error is assuming that these variables are static throughout different phases of research. Their roles can be context-dependent and vary according to the study’s design and objectives.

Consequences of Misidentifying Independent and Dependent Variables

Misidentifying these variables can significantly impact the integrity and outcomes of a research study. When the independent variable is incorrectly identified, the study might fail to address the research question effectively, leading to invalid conclusions. Similarly, incorrect identification of a dependent variable can result in inaccurate measurements and data analysis, skewing the study’s results. Such errors undermine the research’s validity and can lead to wasted resources and misinformed decisions based on the findings.

Tips on Avoiding These Pitfalls in Research

To avoid these pitfalls, researchers should:

1. Clearly Define Research Questions:  A well-structured research question helps correctly identify the variables.

2. Understand the Study Design:  Different designs (experimental, observational) impact the roles of these variables.

3. Seek Peer Input:  Collaborating or consulting with peers can provide a fresh perspective and help identify any oversights in variable identification.

4. Review Literature:  Examining similar studies can offer insights into appropriate variable identification and usage.

5. Pilot Studies:  Conducting preliminary studies or pilot tests can help clarify the roles of variables before the full-scale research.

This comprehensive guide has navigated the intricate world of independent and dependent variables, laying a foundation for understanding their pivotal roles in data analysis. We began by defining these variables and establishing how independent variables act as influencers in research. The dependent variables are the subjects of influence, changing in response to the former. This semantic distinction forms the bedrock of experimental and observational studies across various disciplines.

We explored how these variables function in different contexts, showing their universal applicability, from clinical trials in medicine to economic analyses. The importance of correctly identifying these variables was underscored, highlighting how misidentification can lead to flawed conclusions and ineffective research.

Our journey delved into the relationship between these variables, emphasizing their interplay as the essence of scientific inquiry. We addressed common misconceptions, shedding light on the nuances of their interaction, and provided practical advice to avoid pitfalls in research.

In advanced analysis scenarios, like regression, we discussed the enhanced roles of independent and dependent variables. These scenarios demonstrate the complexities of data interpretation and the need for precise variable analysis, especially in the evolving landscape of data science.

The insights provided in this guide are essential for anyone engaged in data analysis, from students to seasoned professionals. Understanding the dynamics of independent and dependent variables is not just about mastering a concept; it’s about equipping oneself with the tools to uncover truths, make informed decisions, and contribute meaningfully to the vast field of research.

As we conclude, remember that the concepts of independent and dependent variables are more than terminologies; they are the lenses through which we can view and understand the complex patterns and relationships in data. Embracing this understanding will undoubtedly enhance your capabilities in data analysis, research design, and beyond.

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What Makes a Variable Qualitative or Quantitative?

• What is an Independent Variable in an Experiment?
• In Science, What is a Dependent Variable?

Frequently Asked Questions (FAQs)

Q1: What is an Independent Variable?  It’s a variable in research manipulated or controlled to see its effect on a dependent variable.

Q2: What is a Dependent Variable?  This variable is observed and measured to see the effect of an independent variable.

Q3: How do Independent and Dependent Variables Interact?  The independent variable is thought to influence or cause changes in the dependent variable.

Q4: Why are These Variables Important in Research?  Understanding these variables is crucial for designing experiments and interpreting results accurately.

Q5: Can There Be More Than One Independent Variable in an Experiment?  Yes, experiments can have multiple independent variables to explore complex relationships.

Q6: How Do You Identify These Variables in a Study?  Identify the cause (independent) and effect (dependent) elements in the research question.

Q7: What are Examples of Independent and Dependent Variables?  In a study on education, teaching methods could be independent, and student performance could be dependent.

Q8: How Do These Variables Affect Data Analysis?  Correct identification is essential for accurate statistical analysis and drawing valid conclusions.

Q9: Can a Variable be Both Independent and Dependent?  In different studies or contexts, the same variable might play different roles.

Q10: Why is the Distinction Between These Variables Critical?  Understanding their roles helps in forming hypotheses and interpreting data in research.

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Frequently asked questions

Why are independent and dependent variables important.

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

Frequently asked questions: Methodology

Attrition refers to participants leaving a study. It always happens to some extent—for example, in randomized controlled trials for medical research.

Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group . As a result, the characteristics of the participants who drop out differ from the characteristics of those who stay in the study. Because of this, study results may be biased .

Action research is conducted in order to solve a particular issue immediately, while case studies are often conducted over a longer period of time and focus more on observing and analyzing a particular ongoing phenomenon.

Action research is focused on solving a problem or informing individual and community-based knowledge in a way that impacts teaching, learning, and other related processes. It is less focused on contributing theoretical input, instead producing actionable input.

Action research is particularly popular with educators as a form of systematic inquiry because it prioritizes reflection and bridges the gap between theory and practice. Educators are able to simultaneously investigate an issue as they solve it, and the method is very iterative and flexible.

A cycle of inquiry is another name for action research . It is usually visualized in a spiral shape following a series of steps, such as “planning → acting → observing → reflecting.”

To make quantitative observations , you need to use instruments that are capable of measuring the quantity you want to observe. For example, you might use a ruler to measure the length of an object or a thermometer to measure its temperature.

Criterion validity and construct validity are both types of measurement validity . In other words, they both show you how accurately a method measures something.

While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something.

Construct validity is often considered the overarching type of measurement validity . You need to have face validity , content validity , and criterion validity in order to achieve construct validity.

Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.

• Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related. This type of validity is also called divergent validity .

You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.

• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related

Content validity shows you how accurately a test or other measurement method taps  into the various aspects of the specific construct you are researching.

In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity.

The higher the content validity, the more accurate the measurement of the construct.

If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question.

Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.

When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.

For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).

On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analyzing whether each one covers the aspects that the test was designed to cover.

A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.

Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants.

Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.

Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample .

This means that you cannot use inferential statistics and make generalizations —often the goal of quantitative research . As such, a snowball sample is not representative of the target population and is usually a better fit for qualitative research .

Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones.

Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias .

Snowball sampling is best used in the following cases:

• If there is no sampling frame available (e.g., people with a rare disease)
• If the population of interest is hard to access or locate (e.g., people experiencing homelessness)
• If the research focuses on a sensitive topic (e.g., extramarital affairs)

The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.

Reproducibility and replicability are related terms.

• Reproducing research entails reanalyzing the existing data in the same manner.
• Replicating (or repeating ) the research entails reconducting the entire analysis, including the collection of new data . 
• A successful reproduction shows that the data analyses were conducted in a fair and honest manner.
• A successful replication shows that the reliability of the results is high.

Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups.

The main difference is that in stratified sampling, you draw a random sample from each subgroup ( probability sampling ). In quota sampling you select a predetermined number or proportion of units, in a non-random manner ( non-probability sampling ).

Purposive and convenience sampling are both sampling methods that are typically used in qualitative data collection.

A convenience sample is drawn from a source that is conveniently accessible to the researcher. Convenience sampling does not distinguish characteristics among the participants. On the other hand, purposive sampling focuses on selecting participants possessing characteristics associated with the research study.

The findings of studies based on either convenience or purposive sampling can only be generalized to the (sub)population from which the sample is drawn, and not to the entire population.

Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample.

On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data.

Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.

However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.

In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection, using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.

A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.

Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

An observational study is a great choice for you if your research question is based purely on observations. If there are ethical, logistical, or practical concerns that prevent you from conducting a traditional experiment , an observational study may be a good choice. In an observational study, there is no interference or manipulation of the research subjects, as well as no control or treatment groups .

It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.

While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise.

Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.

Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method.

Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface.

Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.

You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity .

When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.

Construct validity is often considered the overarching type of measurement validity ,  because it covers all of the other types. You need to have face validity , content validity , and criterion validity to achieve construct validity.

Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity.

There are two subtypes of construct validity.

• Convergent validity : The extent to which your measure corresponds to measures of related constructs
• Discriminant validity : The extent to which your measure is unrelated or negatively related to measures of distinct constructs

Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting.

The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects.

Naturalistic observation is a qualitative research method where you record the behaviors of your research subjects in real world settings. You avoid interfering or influencing anything in a naturalistic observation.

You can think of naturalistic observation as “people watching” with a purpose.

A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it “depends” on your independent variable.

In statistics, dependent variables are also called:

• Response variables (they respond to a change in another variable)
• Outcome variables (they represent the outcome you want to measure)
• Left-hand-side variables (they appear on the left-hand side of a regression equation)

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.

Independent variables are also called:

• Explanatory variables (they explain an event or outcome)
• Predictor variables (they can be used to predict the value of a dependent variable)
• Right-hand-side variables (they appear on the right-hand side of a regression equation).

As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups. Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions , which can bias your responses.

Overall, your focus group questions should be:

• Open-ended and flexible
• Impossible to answer with “yes” or “no” (questions that start with “why” or “how” are often best)
• Unambiguous, getting straight to the point while still stimulating discussion
• Unbiased and neutral

A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when: 

• You already have a very clear understanding of your topic. Perhaps significant research has already been conducted, or you have done some prior research yourself, but you already possess a baseline for designing strong structured questions.
• You are constrained in terms of time or resources and need to analyze your data quickly and efficiently.
• Your research question depends on strong parity between participants, with environmental conditions held constant.

More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias is the tendency for interview participants to give responses that will be viewed favorably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias can also occur in observations if the participants know they’re being observed. They might alter their behavior accordingly.

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:

• You have prior interview experience. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.
• Your research question is exploratory in nature. Participant answers can guide future research questions and help you develop a more robust knowledge base for future research.

An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.

Unstructured interviews are best used when:

• You are an experienced interviewer and have a very strong background in your research topic, since it is challenging to ask spontaneous, colloquial questions.
• Your research question is exploratory in nature. While you may have developed hypotheses, you are open to discovering new or shifting viewpoints through the interview process.
• You are seeking descriptive data, and are ready to ask questions that will deepen and contextualize your initial thoughts and hypotheses.
• Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts.

The four most common types of interviews are:

• Structured interviews : The questions are predetermined in both topic and order. 
• Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
• Unstructured interviews : None of the questions are predetermined.
• Focus group interviews : The questions are presented to a group instead of one individual.

Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research .

In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.

Deductive reasoning is also called deductive logic.

There are many different types of inductive reasoning that people use formally or informally.

Here are a few common types:

• Inductive generalization : You use observations about a sample to come to a conclusion about the population it came from.
• Statistical generalization: You use specific numbers about samples to make statements about populations.
• Causal reasoning: You make cause-and-effect links between different things.
• Sign reasoning: You make a conclusion about a correlational relationship between different things.
• Analogical reasoning: You make a conclusion about something based on its similarities to something else.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Triangulation can help:

• Reduce research bias that comes from using a single method, theory, or investigator
• Enhance validity by approaching the same topic with different tools
• Establish credibility by giving you a complete picture of the research problem

But triangulation can also pose problems:

• It’s time-consuming and labor-intensive, often involving an interdisciplinary team.
• Your results may be inconsistent or even contradictory.

There are four main types of triangulation :

• Data triangulation : Using data from different times, spaces, and people
• Investigator triangulation : Involving multiple researchers in collecting or analyzing data
• Theory triangulation : Using varying theoretical perspectives in your research
• Methodological triangulation : Using different methodologies to approach the same topic

Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure. 

Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field. It acts as a first defense, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.

Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.

In general, the peer review process follows the following steps: 

• First, the author submits the manuscript to the editor.
• Reject the manuscript and send it back to author, or 
• Send it onward to the selected peer reviewer(s) 
• Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made. 
• Lastly, the edited manuscript is sent back to the author. They input the edits, and resubmit it to the editor for publication.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.

Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem.

Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.

Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors.

Dirty data can come from any part of the research process, including poor research design , inappropriate measurement materials, or flawed data entry.

Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data.

For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do.

After data collection, you can use data standardization and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.

Every dataset requires different techniques to clean dirty data , but you need to address these issues in a systematic way. You focus on finding and resolving data points that don’t agree or fit with the rest of your dataset.

These data might be missing values, outliers, duplicate values, incorrectly formatted, or irrelevant. You’ll start with screening and diagnosing your data. Then, you’ll often standardize and accept or remove data to make your dataset consistent and valid.

Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors , but cleaning your data helps you minimize or resolve these.

Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities.

Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured.

In this process, you review, analyze, detect, modify, or remove “dirty” data to make your dataset “clean.” Data cleaning is also called data cleansing or data scrubbing.

Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.

These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.

Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations .

You can only guarantee anonymity by not collecting any personally identifying information—for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.

You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.

Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.

Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.

Scientists and researchers must always adhere to a certain code of conduct when collecting data from others .

These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.

In multistage sampling , you can use probability or non-probability sampling methods .

For a probability sample, you have to conduct probability sampling at every stage.

You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.

Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame.

But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples .

These are four of the most common mixed methods designs :

• Convergent parallel: Quantitative and qualitative data are collected at the same time and analyzed separately. After both analyses are complete, compare your results to draw overall conclusions. 
• Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
• Explanatory sequential: Quantitative data is collected and analyzed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualize your quantitative findings.
• Exploratory sequential: Qualitative data is collected and analyzed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.

No, the steepness or slope of the line isn’t related to the correlation coefficient value. The correlation coefficient only tells you how closely your data fit on a line, so two datasets with the same correlation coefficient can have very different slopes.

To find the slope of the line, you’ll need to perform a regression analysis .

Correlation coefficients always range between -1 and 1.

The sign of the coefficient tells you the direction of the relationship: a positive value means the variables change together in the same direction, while a negative value means they change together in opposite directions.

The absolute value of a number is equal to the number without its sign. The absolute value of a correlation coefficient tells you the magnitude of the correlation: the greater the absolute value, the stronger the correlation.

These are the assumptions your data must meet if you want to use Pearson’s r :

• Both variables are on an interval or ratio level of measurement
• Data from both variables follow normal distributions
• Your data have no outliers
• Your data is from a random or representative sample
• You expect a linear relationship between the two variables

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your sampling methods or criteria for selecting subjects
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

Questionnaires can be self-administered or researcher-administered.

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or through mail. All questions are standardized so that all respondents receive the same questions with identical wording.

Researcher-administered questionnaires are interviews that take place by phone, in-person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

You can organize the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomization can minimize the bias from order effects.

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires.

The third variable and directionality problems are two main reasons why correlation isn’t causation .

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.

The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.

Correlation describes an association between variables : when one variable changes, so does the other. A correlation is a statistical indicator of the relationship between variables.

Causation means that changes in one variable brings about changes in the other (i.e., there is a cause-and-effect relationship between variables). The two variables are correlated with each other, and there’s also a causal link between them.

While causation and correlation can exist simultaneously, correlation does not imply causation. In other words, correlation is simply a relationship where A relates to B—but A doesn’t necessarily cause B to happen (or vice versa). Mistaking correlation for causation is a common error and can lead to false cause fallacy .

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

• In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
• In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .

A correlation reflects the strength and/or direction of the association between two or more variables.

• A positive correlation means that both variables change in the same direction.
• A negative correlation means that the variables change in opposite directions.
• A zero correlation means there’s no relationship between the variables.

Random error  is almost always present in scientific studies, even in highly controlled settings. While you can’t eradicate it completely, you can reduce random error by taking repeated measurements, using a large sample, and controlling extraneous variables .

You can avoid systematic error through careful design of your sampling , data collection , and analysis procedures. For example, use triangulation to measure your variables using multiple methods; regularly calibrate instruments or procedures; use random sampling and random assignment ; and apply masking (blinding) where possible.

Systematic error is generally a bigger problem in research.

With random error, multiple measurements will tend to cluster around the true value. When you’re collecting data from a large sample , the errors in different directions will cancel each other out.

Systematic errors are much more problematic because they can skew your data away from the true value. This can lead you to false conclusions ( Type I and II errors ) about the relationship between the variables you’re studying.

Random and systematic error are two types of measurement error.

Random error is a chance difference between the observed and true values of something (e.g., a researcher misreading a weighing scale records an incorrect measurement).

Systematic error is a consistent or proportional difference between the observed and true values of something (e.g., a miscalibrated scale consistently records weights as higher than they actually are).

On graphs, the explanatory variable is conventionally placed on the x-axis, while the response variable is placed on the y-axis.

• If you have quantitative variables , use a scatterplot or a line graph.
• If your response variable is categorical, use a scatterplot or a line graph.
• If your explanatory variable is categorical, use a bar graph.

The term “ explanatory variable ” is sometimes preferred over “ independent variable ” because, in real world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent.

Multiple independent variables may also be correlated with each other, so “explanatory variables” is a more appropriate term.

The difference between explanatory and response variables is simple:

• An explanatory variable is the expected cause, and it explains the results.
• A response variable is the expected effect, and it responds to other variables.

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

• A control group that receives a standard treatment, a fake treatment, or no treatment.
• Random assignment of participants to ensure the groups are equivalent.

Depending on your study topic, there are various other methods of controlling variables .

There are 4 main types of extraneous variables :

• Demand characteristics : environmental cues that encourage participants to conform to researchers’ expectations.
• Experimenter effects : unintentional actions by researchers that influence study outcomes.
• Situational variables : environmental variables that alter participants’ behaviors.
• Participant variables : any characteristic or aspect of a participant’s background that could affect study results.

An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.

A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.

In a factorial design, multiple independent variables are tested.

If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.

Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful .

Advantages:

• Only requires small samples
• Statistically powerful
• Removes the effects of individual differences on the outcomes

Disadvantages:

• Internal validity threats reduce the likelihood of establishing a direct relationship between variables
• Time-related effects, such as growth, can influence the outcomes
• Carryover effects mean that the specific order of different treatments affect the outcomes

While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design .

• Prevents carryover effects of learning and fatigue.
• Shorter study duration.
• Needs larger samples for high power.
• Uses more resources to recruit participants, administer sessions, cover costs, etc.
• Individual differences may be an alternative explanation for results.

Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a dice to randomly assign participants to groups.

Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.

In contrast, random assignment is a way of sorting the sample into control and experimental groups.

Random sampling enhances the external validity or generalizability of your results, while random assignment improves the internal validity of your study.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomization. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

“Controlling for a variable” means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.

Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.

If something is a mediating variable :

• It’s caused by the independent variable .
• It influences the dependent variable
• When it’s taken into account, the statistical correlation between the independent and dependent variables is higher than when it isn’t considered.

A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

There are three key steps in systematic sampling :

• Define and list your population , ensuring that it is not ordered in a cyclical or periodic order.
• Decide on your sample size and calculate your interval, k , by dividing your population by your target sample size.
• Choose every k th member of the population as your sample.

Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling .

Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.

For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 x 5 = 15 subgroups.

You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.

Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure.

For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.

In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).

Once divided, each subgroup is randomly sampled using another probability sampling method.

Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.

However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole.

There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.

• In single-stage sampling , you collect data from every unit within the selected clusters.
• In double-stage sampling , you select a random sample of units from within the clusters.
• In multi-stage sampling , you repeat the procedure of randomly sampling elements from within the clusters until you have reached a manageable sample.

Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample.

The clusters should ideally each be mini-representations of the population as a whole.

If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,

If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.

The American Community Survey  is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.

Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data is then collected from as large a percentage as possible of this random subset.

Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment .

Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity  as they can use real-world interventions instead of artificial laboratory settings.

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference with a true experiment is that the groups are not randomly assigned.

Blinding is important to reduce research bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity .

If participants know whether they are in a control or treatment group , they may adjust their behavior in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.

• In a single-blind study , only the participants are blinded.
• In a double-blind study , both participants and experimenters are blinded.
• In a triple-blind study , the assignment is hidden not only from participants and experimenters, but also from the researchers analyzing the data.

Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment .

A true experiment (a.k.a. a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyze your data.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.

In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

The process of turning abstract concepts into measurable variables and indicators is called operationalization .

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g. understanding the needs of your consumers or user testing your website)
• You can control and standardize the process for high reliability and validity (e.g. choosing appropriate measurements and sampling methods )

However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomization , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both!

You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment .

• The type of soda – diet or regular – is the independent variable .
• The level of blood sugar that you measure is the dependent variable – it changes depending on the type of soda.

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

Using careful research design and sampling procedures can help you avoid sampling bias . Oversampling can be used to correct undercoverage bias .

Some common types of sampling bias include self-selection bias , nonresponse bias , undercoverage bias , survivorship bias , pre-screening or advertising bias, and healthy user bias.

Sampling bias is a threat to external validity – it limits the generalizability of your findings to a broader group of people.

A sampling error is the difference between a population parameter and a sample statistic .

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

There are seven threats to external validity : selection bias , history, experimenter effect, Hawthorne effect , testing effect, aptitude-treatment and situation effect.

The two types of external validity are population validity (whether you can generalize to other groups of people) and ecological validity (whether you can generalize to other situations and settings).

The external validity of a study is the extent to which you can generalize your findings to different groups of people, situations, and measures.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyze behavior over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data is available for analysis; other times your research question may only require a cross-sectional study to answer it.

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .

Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction and attrition .

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

Discrete and continuous variables are two types of quantitative variables :

• Discrete variables represent counts (e.g. the number of objects in a collection).
• Continuous variables represent measurable amounts (e.g. water volume or weight).

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

• The  independent variable  is the amount of nutrients added to the crop field.
• The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

I nternal validity is the degree of confidence that the causal relationship you are testing is not influenced by other factors or variables .

External validity is the extent to which your results can be generalized to other contexts.

The validity of your experiment depends on your experimental design .

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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What is an independent variable?

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14 February 2023

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Independent variables are features or values fixed within the population or study under investigation. An example might be a subject's age within a study - other variables, such as what they eat, how long they sleep, and how much TV they watch wouldn't change the subject's age. 

On the other hand, a dependent variable can be influenced by other factors or variables. For example, how well you perform on a series of tests (a dependent variable) could be influenced by how long you study or how much sleep you get before the night of the exam. 

A better understanding of independent variables, specifically the types, how they function in research contexts, and how to distinguish them from dependent variables, will assist you in determining how to identify them in your studies. 

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• Types of independent variables

Independent variables can be of several types, depending on the hypothesis and research. However, the most common types are experimental independent variables and subject variables.

Experimental independent variables

Experimental variables are those that can be directly manipulated in a study. In other words, these are independent variables that you can manipulate to discover how they influence your dependent variables. 

For example, you may have two study groups split by independent variables: one receiving a new drug treatment and one receiving a placebo. These types of studies generally require the random assignment of research participants to different groups to observe how results vary based on the influence of different independent variables.

A proper experiment requires you to randomly assign different levels of an independent variable to your participants.

Random assignment helps you control participant characteristics, so they don't affect your experimental results. This helps you to have confidence that your dependent variable results come solely from the experimental independent variable manipulation.

Subject variables

Subject variables are independent variables that can't be changed in a study but can be used to categorize study participants. They are mostly features that differ between study subjects. For instance, as a social researcher, you can use gender identification, race, education level, or income as key independent variables to classify your research subjects.

Unlike experimental variables, subject variables necessitate a quasi-experimental approach because there is no random assignment. This type of independent variable comprises features and attributes inherent within study participants; therefore, they cannot be assigned randomly. 

Instead, you can develop a research approach in which you evaluate the findings of different groups of participants based on their features. It is important to note that any research design that uses non-random assignment is vulnerable to study biases such as sampling and selection bias.

• What is the importance of independent variables?

As noted previously, independent variables are critical in developing a study design. This is because they assist researchers in determining cause-and-effect relationships. Controlled experiments require minimal to no outside influence to make conclusions. 

Identifying independent variables is one way to eliminate external influences and achieve greater certainty that research results are representative. By controlling for outside influences as much as possible, you can make meaningful inferences about the link between independent and dependent variables.

In most cases, changes in the independent variables cause changes in the dependent variables. For example, if you change an independent variable such as age, you might expect a dependent variable such as cognitive function or running speed to change if the age difference is large. However, there are situations when variations in the independent variables do not influence the dependent variable.

• How can you choose an independent variable?

Choosing independent variables within your research will be driven by the objectives of your study. Start by formulating a hypothesis about the outcome you anticipate, and then choose independent variables that you believe will significantly influence the dependent variables.

Make sure you have experimental and control groups that have identical features. They should only differ based on the treatment they get for the independent variable. In this case, your control group will undergo no treatment or changes in the independent variable, versus the experimental group, which will receive the treatment or a wide variation of the independent variable.

• How to include an independent variable in an experiment

The type of study or experiment greatly impacts the nature of an independent variable. If you are doing an experiment involving a control condition or group, you will need to monitor and define the values of the independent variables you are using within test condition groups.

In an observational experiment, the explanatory variables' values are not predetermined, but instead are observed in their natural surroundings.

Model specification is the process of deciding which independent variables to incorporate into a statistical model. It involves extensive study, numerous specific topics, and statistical aspects.

Including one independent variable in a regression model entails performing a simple regression, while for more than one independent variable, it is a multiple regression. The names might be different, but the analysis, interpretation, and assumptions are all the same.

• What are some examples of independent variables?

To better understand the concept of independent variables, have a look at these few examples used in different contexts:

Mental health context: As a medical researcher, you may be interested in finding out whether a new type of treatment can reduce anxiety in people suffering from a social anxiety disorder. Your study can include three groups of patients. One group receives the new treatment, another gets a different treatment, and the last gets no treatment. The type of treatment is the independent variable.

Workplace context: In this case, you may want to know if giving employees greater control over how they perform their duties results in increased job satisfaction. Your study will involve two groups of employees, one with a lot of say over how they do their jobs and the other without. In this scenario, the independent variable is the amount of control the employees have over their job.

Educational context: You can conduct a study to see if after-school math tutoring improves student performance on standardized math tests. In this example, one group of students will attend an after-school tutoring session three times a week, whereas another group will not receive this extra help. The independent variable is the involvement in after-school math tutoring sessions.

Organization context: You may want to know if the color of an office affects work efficiency. Your research will consider a group of employees working in white or yellow rooms. The independent variable is the color of the office.

• What is a dependent variable?

A dependent variable changes as a result of the manipulation of the independent variable. In a nutshell, it is what you test or measure in an experiment. It is also known as a response variable since it responds to changes in another variable, or known as an outcome variable because it represents the outcome you want to measure.

Statisticians also denote these as left-hand side variables because they are typically found on the left-hand side of a regression model. Typically, dependent variables are plotted on the y-axis of graphs. 

For instance, in a study designed to evaluate how a certain treatment affects the symptoms of psychological disorders, the dependent variable might be identified as the severity of the symptoms a patient experiences. The treatment used would be the independent variable.

The results of an experiment are important because they can assist you in determining the extent to which changes in your independent variable cause variations in your dependent variable. They can also help forecast the degree to which your dependent variable will vary due to changes in the independent variable.

• Identifying independent vs. dependent variables

It can be challenging to differentiate between independent and dependent variables, especially when designing comprehensive research. In some circumstances, a dependent variable from one research study will be used as an independent variable in another. The key is to pay close attention to the study design.

Recognizing independent variables

To recognize independent variables in research, focus on determining whether the variable causes variation in another variable. Independent variables are also manipulated variables whose values are determined by the researchers. In certain experiments, notably in medicine, they are described as risk factors; whereas in others, they are referred to as experimental factors.

Keep in mind that control groups and treatments are often independent variables. And studies that use this approach tend to classify independent variables as categorical grouping variables that establish the experimental groups.

The approaches used to identify independent variables in observational research differ slightly. In these studies, independent variables explain, predict, or correlate with variation in the dependent variable. The study results are also changed or regulated by a variable. If you see an estimated impact size, it is an independent variable, irrespective of the type of study you are reading or designing.

Recognizing dependent variables

To identify dependent variables, you must first determine if the variable is measurable within the research. Also, determine whether the variable relies on another variable in the experiment. If you discover that a variable is only subject to change or variability after other variables have been changed, it may be a dependent variable.

• Independent and dependent variables in research

Both independent and dependent variables are mainly used in quasi-experimental and experimental studies. When conducting research, you can generate descriptive statistics to illustrate results. Following that, you would choose a suitable statistical test to validate your hypothesis. 

The kind of variable, measurement level, and several independent variable levels will significantly influence your chosen test. Many studies use either the ANOVA or the t-test for data analysis and to obtain answers to research questions .

• Other key variables

Other variables, in addition to independent and dependent variables, may have a major impact on a research outcome. Thus, it is vital to identify and take control of extraneous variables since they can cause variation in the relationship between the independent and dependent variables.

Some examples of extraneous variables include demand characteristics and experimenter effects. When these variables cannot be controlled in an experiment, they are usually called confounding variables .

• Visualizing independent and dependent variables

You can use either a chart or a graph to visualize quantitative research results. Graphs have a typical display in which the independent variables lie on the horizontal x-axis and the dependent variables on the vertical y-axis. The presentation of data will depend on the nature of the variables in your research questions.

• The lowdown

Having a working knowledge of independent and dependent variables is key to understanding how research projects work. There are various ways to think of independent variables. However, the best approach is to picture the independent variable as what you change and the dependent variable as what is influenced due to the variation. 

In other words, consider the independent variable the cause and the dependent variable the effect. When visualizing these variables in a graph, place the independent variable on the x-axis and the dependent variable on the y-axis.

It is also essential to remember that there are other variables aside from the independent and dependent variables that might impact the outcome of an experiment. As a result, you should identify and control extraneous variables as much as possible to make a valid conclusion about the study findings.

What are the dependent and independent variables in research?

An independent variable in research or an experiment is what the researcher manipulates or changes. The dependent variable, on the other hand, is what is measured. In general, the independent variable is in charge of influencing the dependent variable.

What are the variables in research examples?

In research or an experiment, a variable refers to something that can be tested. You can use independent and dependent variables to design research .

Can a variable be both independent and dependent at the same time?

No, because a dependent variable is reliant on the independent variable. Thus, a variable in a study can only be the cause (independent) or the effect (dependent). However, there are also cases in which a dependent variable from one study is used as an independent variable in another.

Can a study have more than one independent or dependent variable?

Yes, however, a study must include various research questions for multiple independent and dependent variables to be effective.

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Organizing Your Social Sciences Research Paper: Independent and Dependent Variables

• Purpose of Guide
• Writing a Research Proposal
• Design Flaws to Avoid
• Independent and Dependent Variables
• Narrowing a Topic Idea
• Broadening a Topic Idea
• The Research Problem/Question
• Academic Writing Style
• Choosing a Title
• Making an Outline
• Paragraph Development
• The C.A.R.S. Model
• Background Information
• Theoretical Framework
• Citation Tracking
• Evaluating Sources
• Reading Research Effectively
• Primary Sources
• Secondary Sources
• What Is Scholarly vs. Popular?
• Is it Peer-Reviewed?
• Qualitative Methods
• Quantitative Methods
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism [linked guide]
• Annotated Bibliography
• Grading Someone Else's Paper

Definitions

Dependent Variable The variable that depends on other factors that are measured. These variables are expected to change as a result of an experimental manipulation of the independent variable or variables. It is the presumed effect.

Independent Variable The variable that is stable and unaffected by the other variables you are trying to measure. It refers to the condition of an experiment that is systematically manipulated by the investigator. It is the presumed cause.

Cramer, Duncan and Dennis Howitt. The SAGE Dictionary of Statistics . London: SAGE, 2004; Penslar, Robin Levin and Joan P. Porter. Institutional Review Board Guidebook: Introduction . Washington, DC: United States Department of Health and Human Services, 2010; "What are Dependent and Independent Variables?" Graphic Tutorial .

Identifying Dependent and Indepent Variables

Don't feel bad if you are confused about what is the dependent variable and what is the independent variable in social and behavioral sciences research . However, it's important that you learn the difference because framing a study using these variables is a common approach to organizing the elements of a social sciences research study in order to discover relevant and meaningful results. Specifically, it is important for these two reasons:

• You need to understand and be able to evaluate their application in other people's research.
• You need to apply them correctly in your own research.

A variable in research simply refers to a person, place, thing, or phenomenon that you are trying to measure in some way. The best way to understand the difference between a dependent and independent variable is that the meaning of each is implied by what the words tell us about the variable you are using. You can do this with a simple exercise from the website, Graphic Tutorial. Take the sentence, "The [independent variable] causes a change in [dependent variable] and it is not possible that [dependent variable] could cause a change in [independent variable]." Insert the names of variables you are using in the sentence in the way that makes the most sense. This will help you identify each type of variable. If you're still not sure, consult with your professor before you begin to write.

Fan, Shihe. "Independent Variable." In Encyclopedia of Research Design. Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 592-594; "What are Dependent and Independent Variables?" Graphic Tutorial ; Salkind, Neil J. "Dependent Variable." In Encyclopedia of Research Design , Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 348-349;

Structure and Writing Style

The process of examining a research problem in the social and behavioral sciences is often framed around methods of analysis that compare, contrast, correlate, average, or integrate relationships between or among variables . Techniques include associations, sampling, random selection, and blind selection. Designation of the dependent and independent variable involves unpacking the research problem in a way that identifies a general cause and effect and classifying these variables as either independent or dependent.

The variables should be outlined in the introduction of your paper and explained in more detail in the methods section . There are no rules about the structure and style for writing about independent or dependent variables but, as with any academic writing, clarity and being succinct is most important.

After you have described the research problem and its significance in relation to prior research, explain why you have chosen to examine the problem using a method of analysis that investigates the relationships between or among independent and dependent variables . State what it is about the research problem that lends itself to this type of analysis. For example, if you are investigating the relationship between corporate environmental sustainability efforts [the independent variable] and dependent variables associated with measuring employee satisfaction at work using a survey instrument, you would first identify each variable and then provide background information about the variables. What is meant by "environmental sustainability"? Are you looking at a particular company [e.g., General Motors] or are you investigating an industry [e.g., the meat packing industry]? Why is employee satisfaction in the workplace important? How does a company make their employees aware of sustainability efforts and why would a company even care that its employees know about these efforts?

Identify each variable for the reader and define each . In the introduction, this information can be presented in a paragraph or two when you describe how you are going to study the research problem. In the methods section, you build on the literature review of prior studies about the research problem to describe in detail background about each variable, breaking each down for measurement and analysis. For example, what activities do you examine that reflect a company's commitment to environmental sustainability? Levels of employee satisfaction can be measured by a survey that asks about things like volunteerism or a desire to stay at the company for a long time.

The structure and writing style of describing the variables and their application to analyzing the research problem should be stated and unpacked in such a way that the reader obtains a clear understanding of the relationships between the variables and why they are important. This is also important so that the study can be replicated in the future using the same variables but applied in a different way.

Fan, Shihe. "Independent Variable." In Encyclopedia of Research Design. Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 592-594; "What are Dependent and Independent Variables?" Graphic Tutorial ; “ Case Example for Independent and Dependent Variables .” ORI Curriculum Examples. U.S. Department of Health and Human Services, Office of Research Integrity; Salkind, Neil J. "Dependent Variable." In Encyclopedia of Research Design , Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 348-349; “ Independent Variables and Dependent Variables .” Karl L. Wuensch, Department of Psychology, East Carolina University [posted email exchange]; “ Variables .” Elements of Research. Dr. Camille Nebeker, San Diego State University.

• << Previous: Design Flaws to Avoid
• Next: Choosing a Research Problem >>
• Last Updated: Sep 8, 2023 12:19 PM
• URL: https://guides.library.txstate.edu/socialscienceresearch

Organizing Your Social Sciences Research Paper: Independent and Dependent Variables

• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Choosing a Title
• Making an Outline
• Paragraph Development
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Reading Research Effectively
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• What Is Scholarly vs. Popular?
• Qualitative Methods
• Quantitative Methods
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Annotated Bibliography
• Dealing with Nervousness
• Using Visual Aids
• Grading Someone Else's Paper
• Types of Structured Group Activities
• Group Project Survival Skills
• Multiple Book Review Essay
• Reviewing Collected Essays
• Writing a Case Study
• About Informed Consent
• Writing Field Notes
• Writing a Policy Memo
• Writing a Research Proposal
• Bibliography

Definitions

Dependent Variable The variable that depends on other factors that are measured. These variables are expected to change as a result of an experimental manipulation of the independent variable or variables. It is the presumed effect.

Independent Variable The variable that is stable and unaffected by the other variables you are trying to measure. It refers to the condition of an experiment that is systematically manipulated by the investigator. It is the presumed cause.

Cramer, Duncan and Dennis Howitt. The SAGE Dictionary of Statistics . London: SAGE, 2004; Penslar, Robin Levin and Joan P. Porter. Institutional Review Board Guidebook: Introduction . Washington, DC: United States Department of Health and Human Services, 2010; "What are Dependent and Independent Variables?" Graphic Tutorial .

Identifying Dependent and Indepent Variables

Don't feel bad if you are confused about what is the dependent variable and what is the independent variable in social and behavioral sciences research . However, it's important that you learn the difference because framing a study using these variables is a common approach to organizing the elements of a social sciences research study in order to discover relevant and meaningful results. Specifically, it is important for these two reasons:

• You need to understand and be able to evaluate their application in other people's research.
• You need to apply them correctly in your own research.

A variable in research simply refers to a person, place, thing, or phenomenon that you are trying to measure in some way. The best way to understand the difference between a dependent and independent variable is that the meaning of each is implied by what the words tell us about the variable you are using. You can do this with a simple exercise from the website, Graphic Tutorial. Take the sentence, "The [independent variable] causes a change in [dependent variable] and it is not possible that [dependent variable] could cause a change in [independent variable]." Insert the names of variables you are using in the sentence in the way that makes the most sense. This will help you identify each type of variable. If you're still not sure, consult with your professor before you begin to write.

Fan, Shihe. "Independent Variable." In Encyclopedia of Research Design. Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 592-594; "What are Dependent and Independent Variables?" Graphic Tutorial ; Salkind, Neil J. "Dependent Variable." In Encyclopedia of Research Design , Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 348-349;

Structure and Writing Style

The process of examining a research problem in the social and behavioral sciences is often framed around methods of analysis that compare, contrast, correlate, average, or integrate relationships between or among variables . Techniques include associations, sampling, random selection, and blind selection. Designation of the dependent and independent variable involves unpacking the research problem in a way that identifies a general cause and effect and classifying these variables as either independent or dependent.

The variables should be outlined in the introduction of your paper and explained in more detail in the methods section . There are no rules about the structure and style for writing about independent or dependent variables but, as with any academic writing, clarity and being succinct is most important.

After you have described the research problem and its significance in relation to prior research, explain why you have chosen to examine the problem using a method of analysis that investigates the relationships between or among independent and dependent variables . State what it is about the research problem that lends itself to this type of analysis. For example, if you are investigating the relationship between corporate environmental sustainability efforts [the independent variable] and dependent variables associated with measuring employee satisfaction at work using a survey instrument, you would first identify each variable and then provide background information about the variables. What is meant by "environmental sustainability"? Are you looking at a particular company [e.g., General Motors] or are you investigating an industry [e.g., the meat packing industry]? Why is employee satisfaction in the workplace important? How does a company make their employees aware of sustainability efforts and why would a company even care that its employees know about these efforts?

Identify each variable for the reader and define each . In the introduction, this information can be presented in a paragraph or two when you describe how you are going to study the research problem. In the methods section, you build on the literature review of prior studies about the research problem to describe in detail background about each variable, breaking each down for measurement and analysis. For example, what activities do you examine that reflect a company's commitment to environmental sustainability? Levels of employee satisfaction can be measured by a survey that asks about things like volunteerism or a desire to stay at the company for a long time.

The structure and writing style of describing the variables and their application to analyzing the research problem should be stated and unpacked in such a way that the reader obtains a clear understanding of the relationships between the variables and why they are important. This is also important so that the study can be replicated in the future using the same variables but applied in a different way.

Fan, Shihe. "Independent Variable." In Encyclopedia of Research Design. Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 592-594; "What are Dependent and Independent Variables?" Graphic Tutorial ; “ Case Example for Independent and Dependent Variables .” ORI Curriculum Examples. U.S. Department of Health and Human Services, Office of Research Integrity; Salkind, Neil J. "Dependent Variable." In Encyclopedia of Research Design , Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 348-349; “ Independent Variables and Dependent Variables .” Karl L. Wuensch, Department of Psychology, East Carolina University [posted email exchange]; “ Variables .” Elements of Research. Dr. Camille Nebeker, San Diego State University.

• << Previous: Design Flaws to Avoid
• Next: Glossary of Research Terms >>
• Last Updated: Jan 17, 2023 10:50 AM
• URL: https://libguides.pointloma.edu/ResearchPaper

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Open Access

Peer-reviewed

Research Article

Cutting consumption without diluting the experience: Preferences for different tactics for reducing alcohol consumption among increasing-and-higher-risk drinkers based on drinking context

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Validation, Writing – original draft

* E-mail: [email protected]

Affiliation Department of Behavioural Science and Health, University College London, United Kingdom

Roles Conceptualization, Formal analysis, Investigation, Methodology, Validation, Writing – review & editing

Roles Conceptualization, Funding acquisition, Methodology, Writing – review & editing

Affiliation School of Psychological Science, University of Bristol, United Kingdom

Roles Conceptualization, Writing – review & editing

Affiliation Population Health, School of Medicine and Population Health, University of Sheffield, Sheffield, United Kingdom

Affiliation School of Psychology, Liverpool John Moores University, Liverpool, United Kingdom

Roles Conceptualization, Funding acquisition, Methodology, Supervision, Writing – review & editing

• Melissa Oldham, 
• Tosan Okpako, 
• Corinna Leppin, 
• Claire Garnett, 
• Larisa-Maria Dina, 
• Abigail Stevely, 
• Andrew Jones, 
• John Holmes

• Published: August 21, 2024
• https://doi.org/10.1371/journal.pdig.0000523
• Peer Review
• Reader Comments

Contexts in which people drink vary. Certain drinking contexts may be more amenable to change than others and the effectiveness of alcohol reduction tactics may differ across contexts. This study aimed to explore how helpful context-specific tactics for alcohol reduction were perceived as being amongst increasing-and-higher-risk drinkers. Using the Behaviour Change Technique Taxonomy, context-specific tactics to reduce alcohol consumption were developed by the research team and revised following consultation with experts in behaviour change. In four focus groups (two online, two in-person), N = 20 adult increasing-and-higher-risk drinkers in the UK discussed how helpful tactics developed for four drinking contexts would be: drinking at home alone (19 tactics), drinking at home with partner or family (21 tactics), in the pub with friends (23 tactics), and a meal out of the home (20 tactics). Transcripts were analysed using constant comparison methods. Participants endorsed four broad approaches to reducing alcohol consumption which encompassed all the individual tactics developed by the research team: Diluting and substituting drinks for those containing less alcohol (e.g. switching to soft drinks or no- or low-alcohol drinks); Reducing external pressure to drink (e.g. setting expectations in advance); Creating barriers to drinking (e.g. not buying alcohol to keep at home or storing it in less visible places), and Setting new habits (e.g. breaking old patterns and taking up new hobbies). Three cross-cutting themes influenced how applicable these approaches were to different drinking contexts. These were: Situational pressure, Drinking motives, and Financial motivation. Diluting and substituting drinks which enabled covert reduction and Reducing external pressure to drink were favoured in social drinking contexts. Diluting and substituting drinks which enabled participants to feel that they were having ‘a treat’ or which facilitated relaxation and Creating barriers to drinking were preferred at home. Interventions to reduce alcohol consumption should offer tactics tailored to individuals’ drinking contexts and which account for context-specific individual and situational pressure to drink.

Author summary

Reducing alcohol consumption is a public health priority in the UK. The contexts in which people drink are highly variable. This has implications for intervention development as i) Certain drinking contexts may be more amenable to change than others, both in terms of whether people drink at all and how much they drink and ii) Tactics for alcohol reduction could be more or less applicable in different drinking contexts. In this study, increasing-and-higher-risk drinkers discussed alcohol reduction tactics developed by the research team for inclusion in an effective and popular alcohol reduction app, Drink Less. Twenty increasing-and-higher-risk drinkers participated in four focus groups (two online, two in-person). Participants endorsed four broad approaches to alcohol reduction which encompassed the alcohol reduction tactics developed by the research team; Diluting and substituting drinks, Reducing external pressure to drink, Creating barriers to drinking and Setting new habits in the context of an alcohol reduction app. Three cross-cutting themes, Drinking motives, Situational pressure and Financial motivation influenced how applicable these broad approaches, and individual tactics they encompass, were across drinking contexts. This work highlights the importance of accounting for drinking practices and offering tailored support within alcohol reduction interventions.

Citation: Oldham M, Okpako T, Leppin C, Garnett C, Dina L-M, Stevely A, et al. (2024) Cutting consumption without diluting the experience: Preferences for different tactics for reducing alcohol consumption among increasing-and-higher-risk drinkers based on drinking context. PLOS Digit Health 3(8): e0000523. https://doi.org/10.1371/journal.pdig.0000523

Editor: Haleh Ayatollahi, Iran University of Medical Sciences, ISLAMIC REPUBLIC OF IRAN

Received: May 13, 2024; Accepted: July 10, 2024; Published: August 21, 2024

Copyright: © 2024 Oldham et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The codebook underpinning analysis of the current study is available in the Supplementary Materials .

Funding: This study is funded by the Medical Research Council’s Public Health intervention Development scheme (MRC grant number MR/W026430/1 to MO). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: JH, AS, CL, TO, LMD declare no conflicts of interest. CG and MO have done paid consultancy work for the behaviour change and lifestyle organization, ‘One Year No Beer (OYNB)’, providing fact checking for blog posts. OYNB has no links to the alcohol industry or their affiliates. AJ has received funding from CAMARUS pharmaceuticals for unrelated research.

Alcohol is a dose-dependent [ 1 , 2 ], leading risk factor for preventable cases of cancer and other diseases [ 3 – 6 ] and contributes to health inequalities with the most deprived groups suffering the most harm from alcohol [ 7 ]. In the UK, the contexts in which people drink (e.g. socialising in the pub with friends or drinking at home with a partner) are highly variable [ 8 – 10 ]. Some drinking contexts may be more amenable to change than others in terms of whether people drink at all and how much they drink. Furthermore, the applicability of tactics for reducing alcohol consumption may be context dependent. In this study, increasing-and-higher-risk drinkers discussed alcohol reduction tactics developed by the research team and the relative suitability of these tactics in different drinking contexts.

When conceptualising alcohol consumption, researchers have applied theories such as Social Practice Theory, to emphasise the importance of viewing alcohol consumption as an event, occasion or practice-level phenomenon [ 8 , 11 ]. Through this lens what looks like one behaviour, such as drinking a glass of wine, can take on very different ‘meanings’ in different contexts (e.g. bonding with friends, unwinding after a hard day, or soothing nerves on a first date)[ 12 ]. Empirical studies have also identified the need to measure alcohol consumption at an occasion, rather than individual, level. A range of contextual factors are associated with drinking more alcohol within an occasion, including drinking within a large group [ 13 ], drinking at the weekend [ 14 ] and drinking stronger drinks such as spirits or wine [ 15 ]. Other research has identified the predominant types of drinking occasion in Great Britain and Finland (e.g. ‘big nights out’ and ‘drinking at home with family’) that account for most alcohol consumption [ 9 , 10 , 16 ].

Most existing alcohol reduction interventions do not account for variability in drinking practices. Instead, interventions tend to focus on reducing alcohol consumed without attending to context. However, previous research suggests drinkers do not conceptualise their alcohol consumption in terms of a weekly total, but rather as individual drinking occasions that are differentially integrated, important and acceptable in drinkers’ daily lives [ 17 – 19 ]. Tailoring intervention tactics to individuals’ drinking contexts, and particularly those contexts in which individuals drink to harmful levels, may be more effective than a ‘one-size-fits-all’ approach. As such, studies exploring context-specific tactics for alcohol reduction are of value.

Digital interventions, such as software applications (‘apps’), offer substantial potential for delivering personalised intervention tactics, while addressing barriers associated with face-to-face interventions and reaching a significant proportion of the population [ 20 ]. The Drink Less app is a theory- and evidence-based app [ 21 , 22 ], which resulted in alcohol reduction amongst increasing-and-higher-risk drinkers in a large Randomised Control Trial [ 23 ]. For the present study, the research team used the Behaviour Change Technique (BCT) Taxonomy [ 24 , 25 ] to develop context-specific intervention messaging for two of the existing Drink Less components; Insights and Action Planning. The BCT Taxonomy offers a reliable, cross domain, method for specifying, interpreting and implementing the active ingredients of behaviour change interventions (24). For example, within the Action Planning component, the BCT “facilitate goal setting”[ 24 ] could be differentially applied to particular drinking contexts. Specifically, someone who consumes most of their alcohol in the pub with friends could be prompted to alternate alcoholic drinks with soft drinks. The two app components were selected as context-specific messaging could be integrated into them straightforwardly and they are regularly used by Drink Less users [ 26 ].

We are aware of no research to date which has examined increasing-and-higher-risk drinkers views on the applicability of tactics for alcohol reduction tailored to different drinking contexts. This study used a focus group design to examine these views.

If focus groups run as intended, a conversational dynamic is established between participants. This facilitates discussion of broad opinions, attitudes, and past experiences [ 27 , 28 ]. This process can lead to participants asking questions and exploring topics and ideas that a researcher in a one-to-one interview may not have broached [ 28 ]. Here, the aim of the focus groups was to explore a range of opinions reflecting the experiences of a diverse group of increasing-and-higher-risk drinkers who drink in a range of drinking contexts.

Materials and methods

The study was designed in line with guidance recommending holding 3–6 focus groups lasting 1–2 hours, each with 6–8 participants and two facilitators [ 27 , 28 ]. This study is reported in line with the Consolidated Criteria for Reporting Qualitative Studies (COREQ) 32-item checklist [ 29 ]. The protocol was pre-registered on the Open Science Framework https://osf.io/257t4 .

Ethical approval was granted by UCL’s Research Ethics Committee (ID: 255627.003). Participants provided informed, written consent prior to participation which was reiterated verbally at the start of focus groups. Identities were removed and data was stored securely.

Participants were recruited from an existing database made up of people who have previously taken part in alcohol reduction or smoking cessation studies and given permission for the research team to recontact them about research studies. Participants were emailed with study information and a link to the screening survey. Participants were also recruited via physical posters around the University campus which featured a QR link to the screening study and digital advertisements on social media accompanied by a link to the screening survey.

Eligibility was determined via screening survey. To be eligible for participation, participants had to be increasing-and-higher-risk drinkers (scoring ≥5 on the AUDIT-C [ 30 ]) and interested in using an alcohol reduction app now or in the future. Given the research aims, within each of the focus groups we selected a sample who reported drinking alcohol in a range of different contexts. To ensure the inclusion of diverse viewpoints, the study used a purposive sampling strategy to ensure a maximum variation sample, with representation of different ages and genders, and we aimed to recruit at least half the sample from more disadvantaged socioeconomic positions (SEP). This study aimed to recruit six participants for four focus groups (n = 24).

Participants were given the option of participating in-person or online. Previous studies have shown that data from online and in-person focus groups is comparable [ 31 ] and providing a choice of formats is more inclusive in terms of participants’ geographical and socio-economic position. Online participants took part via Microsoft Teams (2 groups) and in-person participants attended on campus at University College London (2 groups). The focus groups were conducted between December 2023-January 2024. MO facilitated discussion, and TO and CL co-facilitated discussion alongside taking observational notes and monitoring recording equipment.

Development of alcohol reduction tactics and topic guide

To develop context-specific alcohol reduction tactics, it was first necessary to identify the key drinking contexts the tactics should target. A recent typology of drinking occasions identified 15 predominant types of drinking occasion in the UK [ 32 ]. The research team simplified this typology to select eight key drinking contexts that could require different tactics to reduce alcohol consumption. These eight contexts and the labels we use to describe them underwent user testing in a previous study and were found to be acceptable and cover most drinking scenarios among increasing-and-higher-risk drinkers [ 33 ].

Next, the research team developed context-specific intervention messaging for two existing components of the Drink Less app, i) Insights and ii) Action Planning. This drew on theories (e.g. COM-B [ 34 ]) and the Behaviour Change Techniques (BCT) taxonomy [ 24 ].

The Insights component gives users weekly feedback on their progress towards meeting their goals. The research team developed messaging which could be delivered within the Insights component. This highlighted the types of contexts individuals were drinking in when they did not meet their goals (e.g. when you drink more than you want to, you tend to be in occasion X).

Within the Action Planning component, users make action plans to facilitate them reaching their goals. These take the form of implementation intentions, or “If… Then…” plans[ 35 ], and can be differentially applied to particular drinking contexts (e.g. someone who consumes most of their alcohol at home alone could be prompted to not buy alcohol to keep at home or buy smaller bottles of alcohol). The team therefore developed suggested action plans (described as alcohol reduction tactics throughout) that the app might prompt the user with, which were specific to eight different drinking contexts[ 33 ]: Alone at home, With partner or family at home, Social event in a home, Pub with friends, Pub alone, Big day or night out, Meal out and Out with Partner. There was some overlap in the tactics between different contexts (e.g. “I will only buy the alcohol I want to drink that day” was relevant for Alone at home and With partner or family at home). These action plans were developed with reference to the BCT taxonomy. For example, the BCT “facilitate goal setting” could be differentially applied to drinking contexts. Someone who consumes alcohol at home alone could be prompted to set goals such as to ‘use a measure when pouring spirits or wine’ or ‘buy smaller package sizes in the supermarket’. Alternatively, an individual more likely to consume multiple drinks in pubs with friends may set goals ‘to order soft drinks between alcoholic drinks’. The lead researcher initially developed suggested intervention content for both components, this was then extensively reviewed and edited by the full research team, and wider experts in behaviour change and intervention development in a workshop.

Interested participants consented to the study and completed a screening survey including questions on alcohol consumption (AUDIT-C[ 30 ]), willingness to use an alcohol reduction app now or in the future and types of occasions participants typically drank in. Eligible participants were invited to one of four 90-minute focus groups. Consent was reaffirmed at the start of the focus group.

After an icebreaker, there was a short presentation on the Drink Less app, the two relevant components and the plans for the context-specific updates. Participants then discussed how they would feel about receiving feedback on the types of drinking contexts they tended to be in when they did not achieve their goals. Then participants jointly completed a ranking task for two different drinking contexts, putting the alcohol reduction tactics developed by the research team for each context in order of least to most helpful, the aim of this task was to stimulate discussion of each strategy. Throughout the focus groups, the facilitators attempted to ensure that everyone shared views and attempted to draw out differences in opinion by asking whether any participants saw things in a different way. One example of this is following the group ranking task, participants were asked to select the tactics they thought would be personally more or less helpful, this was to draw out differences in opinion within groups. See S1 Appendix for the full topic guide. Each participant was then debriefed and paid a £30 Amazon voucher.

One facilitator took notes during each focus group and afterwards facilitators immediately discussed the topics that arose during the focus groups.

Transcriptions were pseudo-anonymised [gender, age, focus group number], where there were duplicates a, b were added after age. Constant comparison analysis [ 27 , 36 ] of transcripts was then undertaken. Constant comparison enables consecutive analysis of focus groups, to establish whether codes and themes present in earlier groups are seen in later groups.

This involved three stages of coding [ 36 ];

• Open coding–transcripts were read multiple times and codes were attached to chunks of text summarising the topic being discussed.
• Axial coding–these codes were then grouped into categories with other codes that expressed similar or related topics.
• Selective coding—themes were developed that expressed the content of the categories.

The analysis approach was a mix of deductive and thematic coding. The intervention content developed by the research team framed much of the discussion in the focus groups and therefore many of the open codes developed were deductively coded in relation to this. However, participants were also encouraged to discuss tactics they felt were missing or had not been included and new tactics raised by participants were inductively coded. Themes were inductively coded from the data; they were driven by the way participants grouped and talked about different tactics and the factors that were perceived as influencing the applicability of tactics to different settings. When interpreting the categories and formulating themes, the notes taken during focus groups, those documenting conversations directly after focus groups and notes taken during coding were reviewed and this helped inform the structure of the themes. MO undertook preliminary analysis of the first focus group, this was then reviewed by TO and CL with a high level of agreement. There were some suggestions where a code could be applied to new quotes (e.g. mention of self-control that had not been coded as will power). There were two suggestions for where code names should be tweaked to better represent the data (e.g. from “excuses to not drink” to “socially acceptable reasons to abstain”). Finally, there were two suggestions for new codes. One of these, familiarity, representing apparent preference for tactics participants had previously tried, was adopted. The other suggested code, individuality, was not included as after discussion, we felt this was represented by the coding of dissenting voices for each strategy. Following this, MO reviewed and revised the coding for focus group one, before coding each subsequent focus group in turn. Each stage of this coding was then reviewed and agreed upon by the full research team. Quotes presented in the results section have been edited to remove verbal ticks such as ‘umm’ and repeated words for clarity. While we recruited fewer participants than originally planned (n = 20 rather than the estimated n = 24, though the same number of focus groups was conducted), when analysing data from the last focus group, no new open codes were developed nor did the final focus group change the meaning of any existing codes or themes. As such and in line with previous definitions [ 27 ], the research team concluded that theoretical and meaning saturation had been achieved. See S2 Appendix for reflections from the researchers on the analysis.

Sample characteristics

49 individuals completed the screening survey, 39 were eligible and were emailed to schedule a focus group. 20 participants attended a focus group, with individual focus groups ranging from 4–6 participants due to cancellations on the day. Participant characteristics are shown in Table 1 . Focus Groups 2 and 4 took place in-person, whilst Focus Groups 1 and 3 took place online.

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https://doi.org/10.1371/journal.pdig.0000523.t001

There was a total of 60 codes, which were used to develop 19 categories and seven themes. Four themes focused on broad approaches to alcohol reduction and three themes moderate how applicable reduction approaches are to different contexts. Table 2 presents the themes alongside their categories and codes. A full description of each code can be found in S3 Appendix .

https://doi.org/10.1371/journal.pdig.0000523.t002

Overview of themes

Ranking and rating alcohol reduction tactics developed by the research team, resulted in four themes describing broad approaches to reducing alcohol consumption. These were Diluting and substituting drinks, Reducing external pressure to drink, Creating barriers to drinking and Setting new habits. These themes encompassed the alcohol reduction tactics developed by the research team. These four approaches were applied in different ways and were perceived as being differentially helpful across different drinking contexts.

This was in part due to the cross-cutting themes of Situational pressure, Financial motivation and Drinking motives, which differed across drinking contexts. The theme Situational pressure encompassed different forms of pressure to drink, alongside the perceived social costs of reducing alcohol consumption. This theme seemed to be more relevant to social settings, particularly in the context of being in the pub with friends, or situations where a bigger group was present. The Drinking motives theme encompassed different motivations for drinking. In a home context, drinking was often motivated by relaxation. Whereas in larger social contexts drinking was motivated more often by fun or belonging. Drinking for confidence was applied to different settings including pre-drinking at home before a social gathering and in work-related contexts. Finally, Financial motivation impacted on the acceptability of different tactics to reduce consumption across different contexts, being more likely to impact on-trade settings (e.g. bars and pubs).

Theme 1: Diluting and substituting drinks

Participants discussed different tactics for Diluting and substituting drinks within drinking contexts. This theme encompassed the most tactics; alternating alcoholic drinks with soft drinks or no and low alcohol (no-lo) drinks, drinking lower strength drinks and having smaller drinks or measures.

Alternating alcoholic and soft drinks was perceived as most useful for social occasions in a pub and buying rounds. Whereas having a soft drink or water alongside an alcoholic drink was seen as being more helpful in a home context.

“That is more phrased for drinking out, where you are out drinking… when you’re at home, you could have as many drinks in front of you that you want, of various kinds .” [MALE, 68, FG2]

There were mixed responses to tactics which included switching to soft or no-lo drinks. Some participants highlighted reasons they would avoid soft drinks, including sugar content and the volume of liquid. Other participants highlighted soft drinks they felt were a good replacement for an alcoholic drink, such as kombucha, that felt special and replicated the feeling of a having a treat or a reward at the end of the day.

“For me, it’s like a waste of a beverage having, like, a horrible sickly soft drink that I don’t want. Right? So I could have a drink that feels like I’m still experiencing having a beer or glass of wine, but without the consequences .” [ FEMALE, 31, FG4]

Reponses to drinking lower strength drinks, either through switching from a higher strength drink type such as wine, to a lower strength one such as beer, or by reducing the strength within a beverage category (e.g. from a 6% to a 3% beer or alcohol-free beer), were also mixed. Some felt this would be a helpful strategy, particularly if drinking during the day, whereas others reported not liking the taste of lower strength options.

“I actually look at what the strength is before I buy a bottle of wine. I don’t like the lower strength, that tends to be a bit sweet .” [FEMALE, 60, FG1]

Pouring smaller measures of spirits or wine was highlighted as being particularly helpful in a home context, given some participants felt they overpoured at home. Whereas buying smaller bottles was seen as being more helpful in on-trade contexts, partly due to limited availability of different sized packaging in supermarkets and shops.

“That doesn’t help me because of the type of things that I drink, it’s all one size bottles… it would [help] in the pub .” [FEMALE, 29, FG3]

Cross-cutting themes

The cross-cutting themes impacted on how applicable different dilution and substitution tactics were to different contexts. For example, they were perceived as being helpful for participants in social drinking contexts, in which drinking was more likely to be motivated by “drinking to belong” or “to have fun”. Diluting and substituting drinks enabled them to remain part of the social group whilst still limiting their alcohol consumption.

“I prefer the strategies where you do go to the pub however many times you want and you do go with the friends who like drinking, but you have a strategy to not over drink. [That’s] well, I would say would be the ideal, because then you’re still having your social life .” [FEMALE, 44, FG2]

Maintaining a presence at social events whilst having a strategy to reduce consumption may also relieve external pressure to drink. Participants did not feel they would miss out and some participants felt that dilution and substitution methods could be done covertly, allowing them to “present as a drinker” helping them avoid being perceived as judgemental and others becoming defensive. However, this was partly dependent on drink and occasion type, with participants feeling it would be easier to pass as a drinker in the pub or in a larger group.

“Especially if you’re in a bigger group.. and if you’re drinking things that present as alcohol, you’re drinking lower alcohol things, you can probably just sort of like glide through the evening harassment free in some respects, because you’re presenting as a drinker .” [FEMALE, 33, FG1] “There seems to be a bit of an attitude that if you’re somebody who’s trying to drink less or you’re completely sober , you’re very judgmental about people who do drink .” [FEMALE , 33 , FG1]

Financial motivations also interacted with dilution and substitution tactics due to a focus on value. This meant buying smaller bottles was often not popular due to the discounts available for larger purchases. Some participants also discussed the prohibitive cost of no-lo drinks, which put them off buying and trying them. Because people attached value to the alcohol content of drinks, they tended to feel that no-los should be cheaper than alcoholic drinks. For some participants, this was exacerbated by previously trying, but not enjoying no-lo drinks. However, many participants did highlight that the range and quality of no-lo drinks had improved in recent years.

“There are a few, especially if you like sort of craft beers and those sort of hipster beers, there are quite good alternatives now by some big-name brands like BrewDog and that kind of thing. It’s just a shame the price doesn’t always reflect the fact there isn’t any alcohol in them .” [FEMALE, 33, FG1]

Theme 2: Reducing external pressure to drink

Participants talked about the importance of setting expectations to reduce external pressure to drink alcohol. When drinking with friends this often involved warning people in advance that they would not be drinking or setting a drinking limit. Some participants felt this would be less disappointing to friends and less likely to be perceived as a personal slight or rejection. They also talked about feeling they needed to have socially acceptable reasons for not drinking alcohol, which included working the next day, driving or training for a sports event.

“I think we’ve all sort of identified that the social aspect of saying no to a drink can be quite difficult. I think maybe.. like if the app had certain prompts that you could use. I suppose different excuses that maybe would go down better with people? Like I have found just saying no thank you, I don’t wanna drink, leads to a lot of questions .” [FEMALE, 33, FG1]

More extreme versions of Reducing external pressure to drink included avoiding certain friends who would pressure them to drink or to leave early if pressured to drink. However, these were less popular options and were seen as a last resort.

Situational pressure was particularly relevant to Reducing external pressure to drink. Participants’ willingness to ask for social support in their reduction goals often depended on the drinking context or companions. Some participants preferred to ask for support from a partner, rather than friends, although informal ways of doing this were preferred to prevent this from feeling controlling.

“ I think when it’s one on one with a close friend, I’d feel a lot more comfortable saying it. But I also… don’t think it would be a booze up in the same way if it’s one on one, versus if you’re going to a big party with a big group ” [FEMALE, 33, FG1] “I would be made fun of , whereas with a partner who knows its a serious decision , that’d be fine . I don’t have many friends who’ve raised this with me , so I wouldn’t be comfortable raising it with them .” [MALE , 48 , FG3]

As this quote indicates, there were some social contexts where participants felt it was less acceptable to say they were not drinking. They felt pressure to be the ‘most fun’ version of themselves at special occasions and celebrations and felt they would be disappointing friends by being sober, some felt it was ‘rude’ not to drink at special occasions.

“It’s social situations that are causing me pressure, because I’m usually the life and soul of the party and I’ll be coming in with the wine or champagne or whatever, and I am now going to the other side thinking what conversations do I have and how do I go to a wedding… saying I’m not drinking ?” [FEMALE, 56, FG3]

These concerns can be understood as ‘social costs’ of alcohol reduction. Other examples included people feeling that asking for social support would result in negative impacts on friendships or might lead people to think they ‘had a problem’ with their drinking or were not in control of their drinking. This was often perceived as a severe consequence and something to be avoided. This is in line with the tendency amongst heavier drinkers to construct their drinking identity as positive and healthy, deliberately differentiating themselves from the stigmatised ‘alcoholic other’[ 37 ].

“You don’t wanna have to say to someone can you help me to control my drinking, because it’s something a little bit.. [there’s a] weird feeling about that.. is there a problem? Am I not in charge? Am I not in control of that myself ?” [MALE, 36B, FG1]

Theme 3: Creating barriers to drinking

The third theme focused on Creating barriers to drinking, such as making plans in advance to either limit the availability of alcohol in the moment they might want it or limit whether and how much they drink. This included introducing set start and/or stop times for drinking, or having set days for going to bars or pubs. Others introduced external cues such as pre-booking a taxi or telling people they would leave at a specific time to help them stick to their plans.

“If I wait for my first drink, that cuts down the number of hours drinking and therefore the number of drinks .” [FEMALE, 44, FG2] “I’ll usually order a taxi for nine o’clock , so that basically gives me a reason to stop and get back home without getting carried away .” [MALE , 61 , FG3]

In home drinking contexts, having less alcohol available in the house, by not buying alcohol, buying only the alcohol that they would drink that day or by storing alcohol in less visible places in the home to avoid temptation, were seen as good barriers to drinking.

“If you don’t have the beers waiting for you in the fridge when you come home, you’re less likely to be enticed by them .” [FEMALE, 44, FG2]

The cross-cutting theme “Financial motivation” was relevant in whether people created barriers to drinking, with perceived value again playing a role. Only buying alcohol for that day was perceived by some as reducing value for money, as they would not be able to take advantage of multi-pack offers. As above, tactics that were perceived as making alcohol more expensive, particularly in a home context, were generally unpopular.

“I wouldn’t want to commit to not buy multipacks, only buying what I wanted to drink that day or only buying a specific amount because you can save money on bulk purchases and I wouldn’t want to stop doing that .” [FEMALE, 44, FG2]

Situational pressure was also relevant. Participants typically reported Creating barriers to drinking in relation to home-drinking or lone-drinking, which were less subject to external social pressure. However, when discussing Creating barriers to drinking in relation to social contexts, participants discussed planning activities which did not centre on alcohol to reduce expectations and pressure to drink. These included going to board game cafes, gyms, and museums. However, participants highlighted that over time alcohol had become more available and had encroached into more activities, such as going to the cinema. This made it harder to identify places where alcohol was not available, and they would experience no pressure to drink. This was also relevant to the Drinking Motives cross-cutting theme as participants talked about finding ways of socialising and having fun with friends without drinking.

“There’s actually so much overlap with alcohol and different settings… There used to be the separation of pubs where you went to drink and everywhere else where you went to not drink, or not drink as much. So you would drink with your meal or you would go to the cinema and there wouldn’t be any alcohol there .” [FEMALE, 44, FG2]

Theme 4: Setting new habits

Fewer participants discussed Setting new habits relative to the other themes, and where they did, this tended to be in generalities about the importance of will power in facilitating different tactics. Participants talked about the difficulty of breaking old patterns and routines, the importance of will power in creating new habits and having clear, easy behaviours to implement.

“[If] I’m stressed I’m gonna pour a glass of wine. That’s the point, that’s the moment when I probably would need the support .” [FEMALE, 61, FG2] “it’s OK to say ohh yeah , I will do that . It’s the doing .. it’s the willpower bit . So it’s trying to figure out which is easier , which takes the least willpower or whatever to actually implement .” [MALE , 36B , FG1]

When thinking about how to make new habits stick, participants highlighted the importance of behaviour repetition, having visual prompts to new behaviours and tying new habits to existing behaviours and contexts.

“If it’s somebody who really wants to come in and have another alcoholic drink, if there was no… I have got my Horlicks and I’m gonna stick it out on the top.. again, suggestions for what other people have may have done to to break that bit of their habit .” [FEMALE, 61, FG2] “setting specific days you know on Mondays I go round mums I won’t drink there , easy like just to sort of attach it to another part of your routine .” [FEMALE , 33 , FG1]

Drinking motives seemed to be related to this theme. Participants spoke about the importance of understanding what was driving their drinking to develop appropriate new habits that would help them cut down whilst still achieving their desired outcome, whether this was having fun or relaxing.

“The lower strength alcohol, personally, is useful because I like to unwind on the weekends with a beer. I know I’m gonna want it, but having a lower strength means that it’s better for me and I don’t feel like I’m missing out .” [FEMALE, 33, FG1]

Financial motivation was also relevant, with some participants discussing redirecting money from alcohol to a new hobby. Though others felt in practice this would be difficult to implement and keep track of.

“The money I used to spend on alcohol is now in open water swimming and sauna-ing and things like that. So, I made a conscious effort to use my money differently. ” [FEMALE, 56, FG3]

See Table 3 for a summary of how the cross-cutting themes impacted on the broader approaches to alcohol reduction.

https://doi.org/10.1371/journal.pdig.0000523.t003

The research team developed context-specific tactics for alcohol reduction to consider within an alcohol reduction app. Increasing-and-higher-risk drinkers interested in using an alcohol reduction app now or in the future, rated these tactics and endorsed four broad approaches for cutting down. These broad approaches encompassed the alcohol reduction tactics, and were: Diluting and substituting drinks (e.g. through lower strength drinks, smaller drinks or no-lo drinks), Reducing external pressure to drink (e.g. by reducing expectations around drinking or asking for support from friends), Creating barriers to drinking (e.g. by avoiding having alcohol in the house or by setting time limits on drinking) and Setting new habits (e.g. breaking old patterns and taking up new hobbies). Three cross-cutting themes influenced how applicable these approaches were in different types of drinking context; Drinking motives, Situational pressure and Financial motivation.

Understanding the Drinking motives of specific drinking practices can inform tailored tactics which enable behaviour change whilst still facilitating the desired motivation, in healthier ways[ 12 ]. Drinking at home seemed to be more associated with drinking to relax whereas social events, particularly in the on-trade, seemed to be more related to drinking for fun or to belong. As such, in the home approaches that allowed participants to ‘have a treat’ or to take part in familiar routines, such as no-lo alternatives or adult soft drinks which felt ‘special’ or ‘different’ were favoured. For on-trade social events, dilution and substitution tactics, which enabled participants to remain part of the group whilst achieving their reduction goals were favoured. The broad endorsement of Diluting and substituting drinks in different contexts has favourable implications for the role of adult soft drink and no-lo drinks in alcohol harm reduction. No-lo’s could potentially be a broadly positive tool to achieve alcohol harm reduction. They may play a similar role to that of vapes in smoking cessation, although there are important distinctions between the two products (e.g. vapes retain the addictive component of smoking while no/lo products remove most or all of the addictive component). However, some expressed concerns about the pricing of no-lo options which served as a barrier for some participants. There are some concerns about no-lo drinks amongst those working in public health, in terms of them sharing marketing and branding with alcohol products[ 38 ] and leading to cravings amongst dependent drinkers[ 39 ]. Future research examining the role of no-lo’s in alcohol harm reduction is required.

The Drinking motives raised in this study draw parallels with an established drinking motives questionnaire[ 40 ]. Participants discussed drinking to have fun which mapped on to both ‘social motives’ and ‘enhancement motives’. There was also the drinking motive to belong, which mapped well onto the ‘conformity’ motivation. Coping motivations were mentioned less frequently. Participants did talk about drinking to relax and for confidence, and three participants mentioned this in the context of feeling stressed, socially anxious or nervous in networking contexts. Previous qualitative research has found that participants tend to blur the line between drinking to relax and drinking to cope [ 41 ]. As such, this study may broaden our understanding of how people think and talk about their own drinking. It could be that drinking to relax feels more palatable than drinking to cope, or that coping motivations may be perceived by others as less socially acceptable and might be indicative that they are not in control of their drinking.

Alongside Drinking motives, Situational pressures to drink also differed by drinking context which impacted on the suitability of different reduction approaches. In social, on-trade contexts, dilution and substitution tactics which enabled people to remain part of the ‘in-group’ of drinkers were preferred to tactics that marked them as a non-drinking other. Particularly with bigger events, participants felt this approach allowed them to engage in covert reduction, where they could ‘present as a drinker’ and therefore avoid pressure to drink alcohol. Participants differentiated between social expectations to drink, where drinking alcohol was perceived as the default or expected behaviour, and social pressure, more explicit peer pressure from friends. Both contributed to participants feeling that there were social costs to reducing their alcohol consumption within social settings. This was particularly the case in the context of special occasions or celebrations such as birthdays or weddings. This suggests that some drinking contexts, such as special occasions may be less malleable and may require greater levels of intervention than others. Participants experienced less external Situational pressure to drink when drinking at home or alone, where they seemed to be more influenced by habitual patterns of behaviour. This is in line with a previous study, that found that users of an alcohol reduction app, Drink Less, reported they found the app less helpful in controlling their social drinking relative to more habitual home drinking [ 26 ]. Some participants liked the idea of teaming up with a partner to help with accountability and reducing temptation. Informal ways of doing this were preferred to prevent this from feeling controlling.

Financial motivation was more likely to facilitate approaches to reduction in on-trade settings and negatively impact tactics for alcohol reduction which reduced value for money in off-trade contexts. Participants felt that they were ‘saving money’ by drinking in off-trade or home contexts and did not endorse approaches that reduced their value for money. This meant that some tactics falling within the broader approach of Creating barriers to drinking, such as avoiding multipack offers in supermarkets, or dilution or substitution tactics focused on smaller packaging were less favoured by some participants. As mentioned above, the value placed upon the alcohol contained in drinks, also meant that most participants felt that no-lo drinks should be cheaper than their alcoholic counterparts to be considered good value for money. These findings support the need for pricing policy changes which ensure price differentials between no-lo and standard alcohol drinks and remove pricing structures that disincentivise smaller purchases.

An important aspect of this study was that the focus groups took place in December and January. Christmas was described by some participants as being one of the special occasions which presents unique barriers to reduction and many people see January as a time to cut back on drinking through approaches such as Dry January [ 42 ]. This could have resulted in participants being more conscious of, or being in the process of trying to reduce drinking, which may have led to a more fruitful discussion. However, it may also be atypical for their usual drinking. We took an inclusive approach by giving participants the option to participate online or in-person. This approach resulted in a geographically varied sample as well as achieving a varied sample in terms of gender and age. However, we had planned to recruit half of participants of a more disadvantaged socioeconomic position. Just under half of our sample reported living comfortably, 40% reported meeting their needs with a little left and 15% were just meeting basic expectations. Unfortunately, we did not recruit participants who were not meeting basic needs. This limits the findings, particularly as one of the themes indicated that financial motivations influenced perceived utility of different approaches and tactics to reduce consumption. As such, those not currently able to meet their needs would likely have had a different perspective. This is something which should be unpicked in future research. It is possible that individuals in this category had less time or availability to take part in research. The more disadvantaged participants that were captured in our sample opted to attend online focus groups, this highlights the importance of providing participants with a choice in future studies. Another limitation of this approach is that the focus groups were made up of increasing-and-higher-risk drinkers who were willing to talk in a group about their alcohol consumption and who were interested in using an alcohol reduction app now or in the future. Therefore, it is likely that these participants are not representative of all increasing-and-higher-risk drinkers both in terms of those experiencing digital exclusion [ 43 ], and those not interested in making a future alcohol reduction attempt. Whilst the aim of focus groups is not to be representative it is important to include diverse voices in intervention development research to ensure person-focused and fit-for-purpose interventions, as such alternative methods or recruitment (e.g. by leafleting in less advantaged areas) or data collection (e.g. offering alternative formats or holding focus groups or interviews in more convenient locations) could be explored in future studies. This study centres user voices and highlights broad approaches to alcohol reduction which were deemed as appropriate for different drinking contexts. However, the developed tactics have not undergone efficacy testing, this is now a priority for future research.

Increasing-and-higher-risk drinkers endorsed four broad approaches to alcohol reduction which encompassed the alcohol reduction tactics developed by the research team; Diluting and substituting drinks, Reducing external pressure to drink, Creating barriers to drinking and Setting new habits in the context of an alcohol reduction app. Three cross-cutting themes, Drinking motives, Situational pressure and Financial motivation influenced how applicable these broad approaches, and individual tactics they encompass, were across drinking contexts. Dilution and substitution approaches which enabled covert reduction alongside tactics which Reduced external pressure, such as setting expectations in advance, were favoured in social contexts such as in the pub with friends and meals out. Tactics which enabled the broader approach of Creating barriers to drinking, such as limiting alcohol kept in the home and storing alcohol in less visible places, alongside Dilution or substitution tactics such as no-lo alcohol drinks and adult soft drinks which enabled participants to feel that they were having ‘a treat’ to facilitate relaxation, were preferred in the home.

Supporting information

S1 appendix. topic guide..

https://doi.org/10.1371/journal.pdig.0000523.s001

S2 Appendix. Reflexivity.

https://doi.org/10.1371/journal.pdig.0000523.s002

S3 Appendix. Qualitative codebook.

https://doi.org/10.1371/journal.pdig.0000523.s003

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An improved digital soil mapping approach to predict total N by combining machine learning algorithms and open environmental data

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• Published: 20 August 2024

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• Alessandro Auzzas 1 ,
• Gian Franco Capra 1 ,
• Arun Dilipkumar Jani 2 &
• Antonio Ganga   ORCID: orcid.org/0000-0001-7929-5160 1  

Digital Soil Mapping (DSM) is fundamental for soil monitoring, as it is limited and strategic for human activities. The availability of high temporal and spatial resolution data and robust algorithms is essential to map and predict soil properties and characteristics with adequate accuracy, especially at a time when the scientific community, legislators and land managers are increasingly interested in the protection and rational management of soil.

Proximity and remote sensing, efficient data sampling and open public environmental data allow the use of innovative tools to create spatial databases and digital soil maps with high spatial and temporal accuracy. Applying machine learning (ML) to soil data prediction can improve the accuracy of maps, especially at scales where geostatistics may be inefficient. The aim of this research was to map the nitrogen (N) levels in the soils of the Nurra sub-region (north-western Sardinia, Italy), testing the performance of the Ranger, Random Forest Regression (RFR) and Support Vector Regression (SVR) models, using only open source and open access data. According to the literature, the models include soil chemical-physical characteristics, environmental and topographic parameters as independent variables. Our results showed that predictive models are reliable tools for mapping N in soils, with an accuracy in line with the literature. The average accuracy of the models is high (R 2  = 0.76) and the highest accuracy in predicting N content in surface horizons was obtained with RFR (R 2  = 0.79; RMSE = 0.32; MAE = 0.18). Among the predictors, SOM has the highest importance. Our results show that predictive models are reliable tools in mapping N in soils, with an accuracy in line with the literature. The results obtained could encourage the integration of this type of approach in the policy and decision-making process carried out at regional scale for land management.

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Introduction

Digital Soil Mapping (DSM) has been the main spatial information practice in soil science for many years. This sub-discipline of soil science received international recognition in 2005 with the establishment of a dedicated working group led by IUSS (Arrouays et al. 2017 ). Today, the main processes of DSM are based on geostatistical methods, machine learning (ML) models, and algorithms (Heung et al. 2016 ; Khaledian and Miller 2020 ; Padarian et al. 2019 ; Wadoux et al. 2020 ). Geostatistics refers to methods of studying environmental phenomena based on their spatial variability, starting from real data collected in the field (Hoffimann et al. 2021 ). These tools are widely used for drafting prediction maps, especially through different Kriging algorithms (Keskin and Grunwald 2018 ; Santra et al. 2017 ; Zhang et al. 2020 ). Alongside them, however, ML (i.e., tools obtaining comparable results), is increasingly being used (Taghizadeh-Mehrjardi et al. 2021 ; Wadoux et al. 2020 ).

Indeed, ML is applied in several fields, such as monitoring of hydrogeological risk (Jain et al. 2020 ; Ma et al. 2021 ), wildfire prevention (Elia et al. 2020 ), the prediction of soil physical–chemical parameters (Li et al. 2023a , b ; Li et al. 2022 ; Wang et al. 2021 , 2022 ; Xu et al. 2021 ), and human health (Aghazadeh et al. 2019 ; Piunti 2019 ). Consequently, the number of algorithms to reference is as numerous as the fields of application. Depending on the objective, the sampling characteristics and the dataset, it is necessary to choose one algorithm over another (Li et al. 2023a , b ; Wadoux et al. 2020 ). A relevant aspect in the application of ML is the abundance and quality of databases (Chen et al. 2022 ). In environmental science, the application of ML requires extensive and costly surveying campaigns, which can be supported by existing databases, often shared by institutions and governmental bodies according to the logic of open data (Hengl et al. 2017 ) . It is precisely in the environmental field that we are witnessing in recent years the proliferation of open databases, especially by public institutions (Worthy 2015 ), and in the field of soil science (Orgiazzi et al. 2018 ). Furthermore, the increased use of open data in digital soil mapping is recent and strictly related to the use of new spatial analysis tools, such as Google Earth Engine (GEE), and the availability of large datasets of remote sensing data acquired by satellite missions (Copernicus, Landsat) (Poppiel et al. 2021 ). National and international agencies are developing policies and tools to share soil data, also for scientific purposes, such as the LUCAS soil project implemented by the EU Environment Agency (Orgiazzi et al. 2018 ). Indeed, today almost all medium/large scale studies focused on digital soil mapping integrate field data with updated, publicly managed, high-resolution open data (Radočaj et al. 2024 ; Searle et al. 2021 ). This type of data, coupled by a ML algorithm, appears to be more efficient, also in terms of cost–benefit, than the traditional approach using a geostatistical algorithm (Radočaj et al. 2022a ).

Soil mapping can have two main purposes: i) assignment of a class associated with observed soil, or ii) identification of one or more soil features (Zhang et al. 2017 ). Among these, physical–chemical parameters were extensively investigated to create regional (Brungard et al. 2021 ; Maleki et al. 2023 ), local and field scale distribution maps (Chlingaryan et al. 2018 ; Söderström et al. 2016 ; Zhou et al. 2023 ). Among the chemical parameters, the map elaboration for soil macronutrients (N, P, and K) represents a pivotal step, for environmental and agricultural development agencies, farmers, etc., to understand their spatial distribution and consequently improve nutrient input management while avoiding soil water pollution. Nitrogen is a fundamental macronutrient for the development of plant species, not the least because of the quantities that plants require for sustenance (Högberg et al. 2017 ). In fact, plant species accumulate N in different forms and through different modalities, throughout their life cycle and predominantly during the growth phases (Das et al. 2022 ). The continuous input of N needed by crops has a significant impact on production cycles and markets (Dimkpa et al. 2020 ). Use of N fertilizers has a significant economic weight; this entails careful and constant monitoring over time, to highlight the spatial distribution dynamics of N deficits and surpluses (Singh 2018 ; Wang et al. 2019 ).

The Nurra subregion (northwestern Sardinia) provides an excellent paradigmatic case to explore previously reported questions. Indeed, it encompasses several environmental conditions, passing from natural areas (Parks protected and ruled by laws) to highly productive enterprises, mainly located in plains, and represented by: the production of famous, high-quality wines that are exported around the world; from intensive to semi-intensive agricultural activities; cattle and sheep farming for meat and milk-derived products. Additionally, the area has undergone extensive urbanization due to the presence of extended urban areas (Sassari and Alghero) and famous tourist locations (Arru et al. 2019 ).

However, the objectives of this research were to: i) assess the effectiveness and performance of some ML models using only open access environmental databases; ii) predict N values in soil surface horizons of the Nurra sub-region (Sardinia, Italy) and iii) based on the predicted values, draw up a sub-regional scale map. Only open-access data were used, provided, and implemented by different bodies and organizations at different hierarchical levels. Variables under investigation have been selected through data exploration, i.e., an in-depth analysis of the dataset to study its distribution and main characteristics from a statistical point of view. Random tree models were used since they are in common use and integrated, as algorithms, in several statistical software packages, such as “CART”, “RF” and “Ranger,” of Rstudio (RStudio Team 2011 ). Furthermore, this approach has three important characteristics. It is: i) easy to reproduce with open-source software; ii) powered by public open data; iii) oriented to produce outputs that can be easily integrated into decision-making processes (Fig.  1 ).

Workflow Diagram

Materials and methods

The study area, which covers 1,330 km 2 , is located in NW Sardinia (Italy, Fig.  2 ), in the Nurra sub-region (40°48′28.8″N 8°15′14.4″E). Different geological substrates are featured in the area. The most extensive is the limestone formation, followed by pyroclastic flow deposits (south), aeolian sandstones, and gravel (Carmignani et al. 2015 ). The study area is characterized by high pedodiversity, (Aru e Baldaccini 1983) with Alfisols (Rhodoxeralfs, Palexeralfs, Haploxeralf), Inceptisols (Xerochrepts) and Entisols (Fluvents—Xerofluvents, Aquents—Fluvaquents, Psamments—Xeropsamments, ( Keys to Soil Taxonomy, 13th Edition 2022 )) dominating. The main land uses are: agriculture (65%), urban settlements (5%), and natural areas (30%, CORINE Land Cover Copernicus Land Monitoring Service). The vegetative cover is mainly divided into forest vegetation (30%), such as hardwood and coniferous trees, and arable crops (40%), as described by Corine Land Cover (CLC). A part of the forest is located on the coastline of Asinara’s Bay. These are relatively recent conifer plantations placed behind the dunes. Approximately 10% of the surface is occupied by olive trees. The central part of the study area is characterized by irrigated, arable lands.

Study area framework

Data collection

The construction, implementation, and validation of the dataset is a pivotal part of the mapping process; the predictive results of the model depend on its characteristics and composition. The availability of quality data determines the accuracy of the model; therefore, it is necessary to build a general dataset that includes a carefully selected range of variables that, as a whole, influence the values of the variable we want to predict (Wadoux et al. 2020 ). Only open sources have been used in this work. The use of open sources increases the level of replicability of this research, thus providing the possibility to compare results. Furthermore, as shown by several authors (Ferreira et al. 2022 ; Nussbaum et al. 2018 ; Wadoux et al. 2020 ), the availability of data, especially those related to soil characteristics, stimulates research regarding the conditions of this resource. At the same time, the existence and availability of freely accessible data increases society’s awareness of soil resource issues (Gorelick et al. 2017 ; Orgiazzi et al. 2018 ). In this work, chemical, physical, topographical, and land-use-related predictors are used. In Table  1 , the main characteristics of the predictors are reported (type, source and resolution).

Soil chemical-physical features

Soil data used in the study are available on the official website of the Sardinian Soil Survey. Footnote 1 These data are provided in ESRI shapefile format with a geometric punctual structure. Each one of these points represents a sample collected by different institutions involved in several projects: Regional Agency (AGRIS, LAORE), University of Sassari and Cagliari. There are 1511 samplings in the study area, each point is associated with the prosaic card’s code and the relative link that contains the profile description and chemical and physical parameters. Unfortunately, 981 of the 1511 maps contained only physical property data, reducing the number of observations available to apply the models. Further data will be added by LUCAS. Footnote 2

Topography a directly and indirectly affects the dynamics of soil N concentrations (Weintraub et al. 2017 ). In this research, we studied the spatial variation of the Topographic Position Index (TPI), which expresses the shape of the space making up the landscape. We demonstrated the relationship between topographic index and N concentration in soil, especially in forest watersheds (Dai et al. 2022 ; Li et al. 2020 ). The data relating to the topography were explored using the Digital Terrain Model (DTM), developed by the cartographic office of the Sardinian Region, available on the Regional Geoportal (Regione Autonoma della Sardegna 2023 ) at the resolution 10 × 10 m. The TPI values were calculated through the SAGAGIS tool (Conrad et al. 2015 ).

Erosion by water and distance to waterbody

Nitrogen is one of the essential macronutrients in vegetation. The color and vigour of the plant depend on the soil N concentration. Soil N is susceptible to runoff due to water-induced soil erosion (Sequi et al. 2017 ). A covariate related to the hydrography of the study area consisted of an estimation of soil water erosion. This estimate was made available by the European Soil Data Centre (ESDAC) and was achieved using the Revised Universal Soil Loss Equation (RUSLE) model. This empirical model is defined by the following equation:

K = Soil Erodibility, (Panagos et al. 2014 );

R = Erosivity, (Panagos et al. 2015a , b , c , d );

C = Vegetation Cover, (Panagos et al. 2015a , b , c , d );

l = Slope length, s = Steepness (Panagos et al. 2015b );

P = Support Practices, (Panagos et al. 2015c ).

This model estimates soil loss per year (t/ha −1 ). Another important dataset, related to hydrography, is the Euclidean distance between the cell and the waterbodies. The presence of water affects N concentration in the surface horizons of soil (Amicabile 2016 ) and is, therefore, included in the data set. Our aim was to assess the influence of these and other predictors to improve the accuracy of the predictions.

Soil N concentrations in the surface horizons are intrinsically linked to vegetation cover conditions (Chen et al. 2014 ), so vegetation data contribute to assessing land degradation processes (Ridwan et al. 2024 ). Therefore, the vegetation index could help to detect and describe soil conditions. The vegetation spectral indices were obtained by combining several satellite images (Chlingaryan et al. 2018 ). One of the covariates selected to represent the vegetation cover was the Normalized Difference Vegetations Index (NDVI), which represents the vigour of the vegetation with a range of values from [−1; +1], interpreted by the color of the leaves (Antognelli 2018 ). This index estimates the vigour of the vegetation by photosynthesis and is found by the satellite image combination, product by Landsat 8, Footnote 3 through the elaborations of the following band:

n o 4 Red (0.64–0.67 µm).

n o 5 Near-Infrared (0.85–0.88 µm).

the band is elaborated through the following equation:

NIR corresponds to the band 5;

VIS corresponds to band 4.

The final NDVI reading is the average of the values and the image detected in the summer and winter seasons in the years from 2016 to 2020. Data of the images are as follows (Table  2 ):

Exploratory data and spatial analysis

The Exploratory Data and Spatial Analysis (EDA) was implemented using R software. In this study, EDA consisted of analysing the distribution and composition of any predictors, through use of descriptive statistics. It was articulated in five parts: i) data collection, ii) data cleaning, iii) univariate statistics, iv) multivariate statistics, and v) spatial distribution analysis.

Once collected, all data selected in a vectorial dataset in the QGIS workspace (QGIS Development Team 2023 ) covered a wide study area with 100 × 100-m cell grids. The matrix associated with the vectorial grid showed the cell as the row and the variable as the column. The raster dataset was appropriately re-scaled and transformed into a vector dataset using the QGIS raster statistics procedure. The Raster dataset was re-scaled and incorporated into a vectorial dataset using the QGIS raster statistics procedure (QGIS Development Team 2023 ).

In the final dataset, a general check was carried out to identify and remove the null values (NA) and outliers.

Univariate statistics were used to describe the distribution of the values of the predictor and dependent variable.

To detect multicollinearity, we created a correlation matrix. Multicollinearity is a phenomenon that arises during regression analysis when multiple variables exhibit significant correlations not only with the dependent variable but also with each other (Shrestha 2020 ). If two covariates are correlated, it increases the absolute error of the predictions (Daoud 2017 ). Therefore, this analysis helped identify variables that had no impact on prediction quality or, worse, adversely affected it. According to the literature (Chan et al. 2022 ; Lindner et al. 2022 ), we removed the covariates with a correlation coefficient >0.80, because if the value of Pearson correlation coefficient is close to 0.8, collinearity is probable (Shrestha 2020 ).

Another analysis that we conducted on the N value point dataset was the study of spatial autocorrelations, which is the phenomenon associated with the presence of a systematic spatial variation in a variable. A positive spatial autocorrelation is the trend of a site or nearby space to have similar values (Chlingaryan et al. 2018 ; Li et al. 2016 ; Nguyen and Vu 2019 ). The Moran index (Moran 1948 ) enables an estimation of the grade of global spatial autocorrelation. The index is given by:

N is the number of the events;

\({X}_{i}\) and \({X}_{i}\) are the values taken from the intensity at the points i and j with \(i\ne j\) ;

X is the average of the covariate considered;

\({w}_{ij}\) is an element of the matrix containing arbitrary event weights.

The weights are determined according to the contiguity of the events. The range values of the index I are [−1;+1] (Tybl 2016 ). The values closest to 1 and −1 indicate the presence of clustering. While values close to zero indicate a random spatial distribution. This approach could be useful for strengthening model selection. In the absence of high spatial correlation, it is preferable to use multivariate statistical methods rather than geostatistical methods.

Machine learning algorithms

This type of model has been used widely in both classification and regression problems. (Wadoux et al. 2020 ) analysed a large amount of peer-reviewed literature and found that, in the case of classification, 80% of the articles contained the application of at least one random tree model. More than one model was chosen in this research, as it is common to use several models of different types to compare results (Wadoux et al. 2020 ; Zhou et al. 2023 ).

The selection of algorithms was based on the results of previous applications in this field. As described by several authors (Wadoux et al. 2020 ), ML tools have not previously considered soil mechanics, phenomena, and properties, but rather learn from the data on which they are trained. For this reason, it can be useful to understand the results of the model applications in similar situations. In this case, to select the models, we search for a similar case study, where the goal is to predict the values of chemical components in the soil (Dai et al. 2022 ; Flynn et al. 2023 ; Forkuor et al. 2017 ; Hengl et al. 2017 ; Li et al. 2023a , b ; Li et al. 2022 ; Prado Osco et al. 2019 ; van der Westhuizen et al. 2023 ; Wadoux et al. 2020 ; Wang et al. 2022 ; Xiaorui et al. 2023 ; Xu et al. 2021 ; Zhou et al. 2023 ). Following the bibliography analysis, the algorithms selected were Random Forest Regression (RFR), Ranger, and Support Vector Machine Regression (SVR).

Random forest regression and ranger

While the RF and model is often used in fields, such as medicine (Sarica et al. 2017 ), it is also widely used in soil mapping (Wadoux et al. 2020 ).

This method is based on the creation of forests of decision trees to improve the accuracy of predictions, and is, therefore, classified as an ensemble algorithm, i.e. one that includes a number of other models (Zhou et al. 2023 ). Unlike other ML models, RF randomly selects the subset of independent variables to subdivide the nodes (leaves), making it more accurate and further minimising the instability of the trees (Forkuor et al. 2017 ; van der Westhuizen et al. 2023 ). It is possible to choose the number of trees that make up the forest (Tree Number = 500), each of which is created independently using a single sample of the training data.

Ranger is a fast implementation of RF mostly used for large datasets (Wright and Ziegler 2017 ). Both belong to the class of tree models. The Ranger package, implemented in the R workspace, enables managing some other aspects in the model realisation phase.

Specifically, the parameters to be handled in the function are different from those of RF and allow the implementation of model management and refinement. The main ones used in the model training phase are:

Quantreg, if enabled it performs a quantile prediction through a regression forest;

Num.trees, which adjusts the quantity of trees in the forest;

Write.forest, to store the results of the model;

Min.node.size, which is the minimum size of the leaves, the value 5 is recommended for this parameter if a regression is performed .

Importance, which makes a ranking of the importance of the independent variables in the prediction, for regression the importance is based on the value of the variance of the results and is coded with the terminology “ impurity” (Xu et al. 2016 ).

This makes this phase more refined compared to other models. We demonstrated the computational and memory efficiency of a ranger in the implementation done in R software, the algorithm manages many more values and variables in less time than RF, making it very effective and fast compared to other models (Wright and Ziegler 2017 ).

Algorithm 1 RFR Program Code

Algorithm 2 Ranger Program Code

Support vector regression

SVR, an extension of Support Vector Machine for Regression issues (Lee et al. 2020 ; Ramedani et al. 2014 ) is not a widely used model in this field, but there are some examples of its application in regression issues to predict the values of different soil properties (Li et al. 2023a , b ; Wang et al. 2021 ; Xu et al. 2021 ; Zhou et al. 2023 ). This algorithm implements a function whose purpose is to predict the dependent variable. One of the reasons we chose this algorithm is the difference in the inner workings of the tree models. SVR formulations are analogous to common linear regression, but there are some differences concerning it (Ramedani et al. 2014 ). This algorithm projects the data into a high-dimension space, through the Kernel function (the choice of kernel depends on the characteristics of the data and can have a significant impact on the performance of the model (Forkuor et al. 2017 )), to identify a separation hyperplane due to the support vector. Into the limit of the vector, managed by the cost parameter (C), the prediction occurs, i.e., the value predicted is located in this range (Adwad and Khanna 2015 ).

Algorithm 3 Ranger Program Code

Validation and assessment models

Two different techniques were used to validate the models. The first divided the model into two parts, in random mode. The larger part of the dataset was used to train the models (training dataset). The second part was used to test the performance of the model on unknown data (test dataset). The split of the dataset was 75% for the training dataset and the rest for the test dataset. The cross-validation, or k-fold cross-validation (CV), is a statistical technique that consists of dividing the training dataset into k parts to limit the overfitting phenomenon. The overfitting problems are essential when one wants to use ML tools, both in the case of classification and regression issues (Berrar 2019 ; Wang et al. 2021 ). According to the bibliography (Aghazadeh et al. 2019 ; Berrar 2019 ; Dharumarajan 2019 ; Hounkpatin et al. 2022 ; Khaledian and Miller 2020 ; Li et al. 2023a , b ; Liu et al. 2022 ; Maleki et al. 2023 ; Mashaba-Munghemezulu et al. 2021 ; Nolan et al. 2018 ; Radočaj et al. 2022b ; Rahman et al. 2020 ; Uddameri et al. 2020 ; Van Der Westhuizen et al. 2022 , 2023 ; Wadoux et al. 2020 ; Wang et al. 2021 ; Xu et al. 2021 ; Zhang et al. 2021 ; Zhou et al. 2023 ), the most widely used and efficient CVs are those with K = 5 and K = 10. In this paper, we have chosen a CV of K = 10.

The metrics used to assess the accuracy of the performance can be different according to the issue at hand. In this paper, we use the metrics that assess the residual of the prediction, i.e., the difference between actual and predicted values. The most common are the coefficient of determination (R 2 ), the root-mean-square error (RMSE), and the mean absolute error (MAE). These metrics are used in several soil mapping cases to compare the performance of the different models chosen (Chlingaryan et al. 2018 ; Dai et al. 2022 ; Lee et al. 2020 ; Liang et al. 2018 ; Prado Osco et al. 2019 ; Wadoux et al. 2020 ; Zhang et al. 2019 ). The formulas are as follows:

\(O\) is the real value of N;

\(P\) is the prediction.

Results and discussion

The following table shows the results of the descriptive statistical analysis (Table  3 ):

The final dataset consisted of 300 observations and 18 predictors.

The correlation matrix (Fig.  3 ) did not indicate a high association between the predictors, so we excluded the potential presence of the phenomenon of multicollinearity. Results from the spatial autocorrelations (Fig.  4 ) indicated a value of 0.108. These relationships were, therefore, like random spatial phenomena; in these cases, it might be more appropriate to apply a multivariate statistical algorithm to study the distribution of variables, rather than using a ‘traditional’ geostatistical approach.

Correlation matrix

Moran I scatterplot

Covariates importance

In the tree models, it is possible to verify the importance of the variables in the predictions (Figs. 5 and 6 ). The importance of the variables is defined in models such as RFR and Ranger; that is why the evaluation of the importance is based on the deep mechanics of the model when it creates the tree that will compose the random forest in the regression process. The statistics analysed by the function are InNodePurity (Increase in Node Purity), which assesses how the purity of the node (detected by a metric such as the Gini index or the entropy) increases when a node is split based on a specific variable. High values in this case indicate a greater influence of the variable in the node splitting, in this case, process.

Plot of covariates importance in RFR model (RFR2 = RFR standard run; RFR*2 = RFR with tenfold CV )

Plot of covariates importance in Ranger model (Ranger2 = RFR standard run; Ranger*2 = RFR with tenfold CV )

The SOM represents the principal source of organic N in the soil, which amounts to approximately 97–98%. Vegetation accumulated N in the ammoniacal and nitrate forms and returned it to the soil as organic N after death (Sequi et al. 2017 ).

For this reason, we justified the high relevance of SOM. It is important to verify, in a subsequent phase, if there is a spatial relationship between the distribution of the prediction and the values of SO. The class of variables that had the most influence in the prediction of N values were the same in both models (Table  4 ). It was possible to say that the predictors with more influence belonged to the class of chemical characteristics of the soil. The topography, especially altitude, also is important.

Residual analysis

Residual analysis was performed on the predictions made in the test phase to assess the performance of the models and their accuracy when working with unknown data. SOM contributes approximately 98% of organic nitrogen in the soil. Most plant accumulate N directly from the soil as ammonium and nitrate. After death, plant N is returned to the soil in organic form (Sequi et al. 2017 ). For this reason and because of the importance of the variable in the prediction, we chose to relate the residuals of the results and the value of the SOM.

The greater density of values in the Ranger prediction corresponded to fewer residual values (Fig.  7 ). This shows that the model generated relatively accurate results, with less deviation from the real value. Most of the results are located in the negative component of the plots, i.e., the model tends to underestimate the prediction relative to the real value. The values that were aligned in the first row of the graph were instead overlapped in the second row, corresponding to the zero value of the y-axis. The model was, therefore, able to predict these specific values without error.

Plot of residual in Ranger model (first row: application without CV tenfold ; second row: application with CV tenfold )

In the model without CV validation, there is an inherent tendency to overestimate values in the range from 0 to 0.5. As can be seen in Fig.  7 , this tendency is eliminated in the model to which tenfold CV validation has been applied. The values that are aligned in the first line in the graph are superimposed at the zero value of the y-axis in the second. The model was therefore able to predict these certain values without committing any errors. The statistics on the residuals of the two applications of the model are shown in Table  5 .

The residual from the RFR model was very similar to the Ranger result. Again, the model showed the previously observed trend, but with greater moderation compared to the Ranger results. Contrary to Ranger, the application of the model with CV did not eliminate all the trends, resulting in an overestimated prediction corresponding to a real value of 0. The density of the predicted values was concentrated near the zero value in both RFR applications with and without CV (Fig.  8 ). The model with CV had a higher accuracy in the density curve, indicating a lower residual between the prediction and the real values.

Plot of residual in RFR model (first row: application without CV tenfold ; second row: application with CV tenfold )

The statistics in Tables 5 and 6 show the affinity between the tree models in this application. Both the mean and the variance were similar. Additionally, in the complex, RFR performance was aligned with the width of the residual distribution. We can say that, even if it is short, RFR residuals assumed a high precision compared to Ranger residuals.

The SVR was influenced by the tendency to overestimate the lowest N, both with and without CV. While in the previous models, the CV limited this type of problem, in this case, the opposite was true. From the plots (Fig.  9 ) we can see an increase in the overestimated values, although the trend observed in the plot showing the relationships with SOM concentration was decreasing.

Plot of residual in SVR model (first row: application without CV tenfold ; second row: application with CV tenfold )

The residual statistics in Table  7 indicated that the model had a wider bound than the other models. This suggests that the predictions had a higher error. The density, although more balanced, was less concentrated near the value of 0 on the x-axis, indicating an increase in the dispersion of the residuals and, therefore, a general increase in the error.

This analysis shows that CV has a positive significant influence on the model performance, regarding the tree algorithms, reducing some negative systematic trends. This does not happen in the case of the SVR algorithm and there have been some difficulties related to the overestimations of the lowest N values.

Accuracy assessment

This analysis demonstrated the reliability of the models in a regression prediction. The results near the real values produced a more solid DSM that was typical of the landscape characteristics. Part of the potential of these tools lies in providing a measure of the error that underlies the process of producing spatial information.

Table 8 shows the metrics related to the quality of the predictions in the training phase. These metrics are used to assess the quality of the model in predicting the training values.

From the values in Table  8 , it is possible to state that the better model performance, in the training phase was obtained by SVR since the algorithm had higher R 2 values and the lowest error metrics. RFR had better quality compared to Ranger. RFR had an R 2 of 0.86 while Ranger has 0.85. Additionally, the RMSE value was lower than Ranger which had a 0.29 for the RMSE, while RFR had 0.27. For the MAE, the opposite occurred as RFR and Ranger had 0.17 and 0.16, respectively.

Table 9 shows the metric values that represent the performance quality of the prediction in the test phase.

In the test phase, the situation was reversed as SVR had the lowest performance quality in terms of the selected metrics. RFR had the highest performance quality, with a prediction that approximated the real value. The values were slightly lower than in the training phase, in fact, the highest R 2 value was obtained by RFR at 0.79. Our results align with findings in other similar works. The R 2 of the RFR model predictions was higher than that obtained by Maleki et al. ( 2023 ), even if the metric error values were worse in this case. The R 2 of RFR and SVR were comparable to those obtained by other researchers (Lee et al. 2020 ; Liang et al. 2018 ), while the RMSE values showed higher precision in respect to those obtained by (Liu et al. 2023 ; Prado Osco et al. 2019 ). SVR resulted in RMSE and R 2 values better than those found by (Xiaorui et al. 2023 ) for the same model application. The MAE values were more moderate than those obtained by Prado Osco et al. ( 2019 ).

The graphs in Fig.  10 show the quality of the predictions for each model. In an optimal state, the predictions (red) should agree with the real values (black dots). In this case, all models had difficulty in predicting the highest values of N. RFR can accurately predict the value of N close to 0. Ranger and SVR cannot accurately predict the value around 0 g Kg −1 of N in the soil, in particular SVR which predicts a negative value.

Graphs of the prediction value

Figure  11 shows the graphs comparing the real N values and the predictions. In an optimal state, the predictions would appear as a perfect diagonal, indicating that the prediction matches the real values. We have used a color scale for the prediction point to show the error: red indicates a high error, orange and yellow indicate a medium error, while the green point indicates a prediction close to the real values. The points in the RFR graph are more aligned along the diagonal, which, when compared to the other graphs, shows the higher quality of its prediction.

Graphs of the distance between the prediction values and the actual values

As the previous graphs show, SVR and Ranger tended to overestimate N values close to or equal to 0, which did not happen in the case of RFR applications. Finally, it is possible to observe how SVR, in some states, obtains negative values in its prediction, in correspondence with a real value equal to 0.

Prediction maps

The models were used to produce prediction maps (Fig.  12 ). They showed the distribution of N concentration over the study area and the influence of some critical patterns:

In the western part, where there was a wooded vegetation cover (with a predominance of deciduous trees), the N concentration was higher than in the area occupied by agricultural activity, due to the absence of vegetation with a long-life cycle. Even if there was a contribution of N synthesis in the fertilization phase, the N was subject to different types of losses (e.g., denitrification and leaching).

The same scenario characterized the arable crops and pastures that occupy the central part, while the opposite was true for the area occupied by shrub and tree vegetation.

The hinterland of the city of Sassari (east-central sector) was one of the areas with the higher predicted N values, which was why the area was mostly occupied by olive groves along the city limits.

Predictions Maps

The presence of a large area cultivated almost exclusively with olive trees ensures, in this condition, an adequate soil N concentration, partly due to the fertilizer applied. The low level was concentrated along the coast, where the highest level of urbanization was found. According to Amicabile ( 2016 ), all models showed an increased concentration of N, corresponding to the high levels of SOM. The predictions showed an accumulation of N along the course of the rivers, due to leaching, which manifested itself with a storage towards the lowest part. In the map product of the SVR predictions, this phenomenon was more evident. It was possible to observe high values close to the hydrographic network of the main river (Riu Mannu), localized in the eastern part.

The relationship between N concentrations in the surface horizons clearly shows that in soils of the investigated areas, the N concentrations increased as the ecosystem’s conservation status increased. It clearly shows how in areas with a forest cover (with a prevalence of broad-leaved trees), N concentration is higher than in the same areas occupied by agricultural activities, due to the lack of long-cycle, high-coverage vegetation in the latter. Even if there is an input of synthetic N, due to fertilisation actions in the field, it should be remembered that N in soils is subject to various types of loss (mainly through leaching and denitrification (Amicabile 2016 )). This is true for agricultural areas affected by arable crops or pastures for sheep breeding, while on soils with tree-type vegetation the opposite phenomenon occurs. Evidence of this can be seen in the fact that the models have, in all three cases, identified the maximum content in the areas bordering the city of Sassari, attributable to the massive presence of olive groves.

Concerning the difference between the model predictions, the main difference between the maps predicted by the tree models and the SVR was the localization of the higher values. In the tree models, the higher values of N were localized in the boundaries of the city of Sassari, while the SVR predicted higher values along the western coast of the municipality of Sassari. The RFR and Ranger map products showed a high N value on the surface of the municipality of Sorso (northeast of Sassari) compared to the SVR map. This behaviour could be explained by the difference in performance in the presence of low-density sampling points.

Conclusions

This research was conducted to evaluate the effectiveness and performance of some ML models using only open environmental databases. The use of open-source data will be pivotal in the future, especially due to the large datasets acquired by remote sensing or proximity sensors. However, great importance assumes the possibilities of the use of most effective algorithm. The results showed that the RFR performed strongly. The main outcomes also revealed that by using ML algorithms, it was possible to predict N values at a medium scale coupling large open environmental databases to obtain a reliable performance. More specifically, the applied models showed approximately the same performance, with the RFR showing the highest R 2 while the RSME showed the lowest. The spatial visualization of the results demonstrated the distribution of the N value in a middle-scale map, where it was possible to detect potential critical areas that could require specific actions in the environmental policy framework. Our next steps with this research are to improve the models by incorporating additional data sources to improve the spatio-temporal scale, taking into account the quality of the data, assessed on the basis of a deep exploratory data analysis. Indeed, the high spatio-temporal resolution is crucial for the implementation of effective soil management policies in areas of high human activity density.

Data availability

The data used to support this study are available by contacting the corresponding author.

Available on: http://www.sardegnaportalesuolo.it/opendata , redacted by Agris Sardegna.

Available on: https://esdac.jrc.ec.europa.eu/projects/lucas

Available on: https://earthexplorer.usgs.gov/

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What predicts hurricane evacuation decisions? The importance of efficacy beliefs, risk perceptions, and other factors

• Rebecca E. Morss 1 ,
• Cara L. Cuite 2 &
• Julie L. Demuth 1  

npj Natural Hazards volume  1 , Article number:  24 ( 2024 ) Cite this article

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Risk theories and empirical research indicate that a variety of factors can influence people’s protective decisions for natural hazards. Using data from an online survey that presented coastal U.S. residents with a hypothetical hurricane scenario, this study investigates the relative importance of cognitive risk perceptions, negative affect, efficacy beliefs, and other factors in explaining people’s anticipated evacuation decisions. The analysis finds that multiple factors, including individual and household characteristics, previous experiences, cognitive and affective risk perceptions, and efficacy beliefs, can help predict hurricane evacuation intentions. However, the largest amount of variance in survey participants’ evacuation intentions is explained by their evacuation-related response efficacy (coping appraisals) and their perceived likelihood of getting hurt if they stay home during the storm. Additional analysis explores how risk perceptions and efficacy beliefs interact to influence people’s responses to risk information. Although further investigation in additional situations is needed, these results suggest that persuading people at high risk that evacuating is likely to reduce harm can serve as an important risk communication lever for motivating hurricane evacuation.

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Introduction.

With increasing risk of hurricane impacts along U.S. coastlines, motivating populations at high risk to evacuate when needed remains both important and challenging. In areas where an approaching hurricane poses a threat to life and safety, public officials typically recommend that people move to a safer location before the arrival of hazardous weather. Yet some people in areas at high risk do not or cannot leave, which can have devastating consequences. In 2021 during Hurricane Ian, for example, more than 40 people drowned due to storm surge flooding, and many more experienced physical injuries or traumatic life-threatening situations 1 , 2 , 3 .

Many previous studies have examined why some people evacuate when a hurricane threatens, while other people do not. This body of research finds that multiple factors, including messages received, individual and household characteristics, past experiences, evacuation capacity and barriers, social influences, and risk perceptions, can influence people’s hurricane evacuation decisions (see, e.g., reviews in refs. 4 , 5 , 6 , 7 ). However, which factors are most influential varies among studies. This variation arises in part because studies examine a variety of real and hypothetical hurricane situations, and people vary in their hurricane-related experiences, vulnerabilities, capacities, and responses to risks 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 . In addition, different studies include different sets of possible influences on evacuations and operationalize variables differently. For example, as discussed further below, researchers measure concepts such as hurricane experience and risk perceptions in a variety of ways 4 , 5 , 6 , 7 , 16 , which further complicates understanding the primary factors driving hurricane evacuation decisions.

Along with empirical studies, there are multiple theories of how people respond to information about threats and what determines protective behaviors. These include Protection Motivation Theory (PMT 17 , 18 ), the Extended Parallel Process Model (EPPM 19 , 20 ), and the Protective Action Decision Model (PADM 21 , 22 ). Although the specifics vary, each of these theories suggests that protective decision making is influenced by people’s risk perceptions (also called threat appraisals or threat perceptions) and their efficacy beliefs (also called coping appraisals or perceptions of protective actions), as illustrated in Fig. 1 . In the PMT and EPPM, risk perceptions include cognitive risk perceptions , conceptualized as perceived likelihood and severity of the threat, and fear, a type of affective risk perceptions (also called affective responses or emotional appraisals). Efficacy beliefs include beliefs about how effective an action is in protecting against a threat ( response efficacy ) and about one’s ability to perform a protective action ( self-efficacy ). (See, e.g., refs. 12 , 15 , 16 , 17 , 19 , 23 , 24 ) These theories also suggest that people facing risks can engage in protective responses, such as deciding to evacuate, and other (non-protective) responses, such as defensive reactions (Fig. 1 ; see Results).

The top-left box indicates the hurricane risk information that survey respondents received in the experimental module studied here. Solid arrows indicate relationships explored in this article; dashed arrows indicate relationships anticipated based on theory and prior empirical research but not directly investigated here.

The concepts and relationships posited by these theories have been tested extensively in the context of health messaging and behaviors. Meta-analyses of health studies find that both risk and efficacy messaging, as well as the risk perceptions and efficacy beliefs that such messages can elicit, influence people’s protective decisions and other responses to risks 25 , 26 , 27 , 28 , 29 . The roles of risk perceptions and efficacy beliefs in decisions have also been investigated in other contexts, including climate adaptation 30 , 31 , 32 , 33 and long-term protection from natural, technological, and human-caused hazards 34 , 35 , 36 , 37 , 38 , 39 . Although many of these studies find that risk perceptions are important, some find that efficacy beliefs can have stronger effects on protective decisions 25 , 26 , 30 , 31 , 34 , 38 , 39 , 40 , 41 .

While similar to hurricane evacuation in some respects, the types of decision contexts discussed above involve different considerations and dynamics than near-term decisions for approaching hazards 42 , 43 . A number of hurricane studies have examined risk perceptions, with most finding that they are related to people’s evacuation decisions 4 , 5 , 6 , 7 , 8 , 16 , 44 , 45 . This has important practical implications because risk perceptions can be influenced by risk communication, which forecasters and public officials can shape. However, because risk perception is a broad construct that includes multiple interrelated attitudes, beliefs, and feelings 46 , 47 , 48 , 49 , researchers use a variety of hurricane risk perception measures. These include perceptions of storm characteristics and associated wind and flood hazards, perceived likelihood and severity of a hurricane event, perceived impacts and safety, and affective responses such as concern and fear 5 , 7 , 12 , 50 , 51 , 52 , 53 , 54 , 55 .

Research also shows that people’s capacity to take protective actions—including barriers or impediments such as lack of available shelter or transportation, limited funds, work, pets, or household members with disabilities—influences their protective action decisions 7 , 9 , 52 , 53 , 54 , 56 , 57 . A few studies have investigated the role of efficacy beliefs in the context of near-term hazard threats 16 , 58 , 59 , but less is known about how different types of efficacy influence hurricane evacuation decisions. Thus, improving understanding about how different aspects of risk perceptions, efficacy beliefs, and other factors influence evacuation decisions can help improve hurricane risk communication.

To help address these knowledge gaps, this study investigates the roles of different situation-specific and general hurricane-related factors in explaining evacuation decisions, using data from a survey that presented respondents with information about a hypothetical scenario of an approaching hurricane (see Methods; Fig. 1 ). The situation-specific variables investigated here measured multiple aspects of respondents’ perceptions, beliefs, and anticipated behaviors related to the specific hurricane situation presented (Table 1 ). This includes respondents’ cognitive risk perceptions related to the overall hurricane threat (measured here as their perceived likelihood of their home being affected, severity of the hurricane at their home, and likelihood of getting hurt if they stay home) along with their perceptions of whether the hurricane poses a threat to their home from three hurricane-related geophysical hazards (strong winds, storm surge flooding, and rain flooding). It also includes their affective risk perceptions (measured as fear and worry) and efficacy beliefs (measured as evacuation-related self-efficacy and response efficacy). The general hurricane-related variables investigated here measured respondents’ perceptions, preparations, and experiences related to hurricane risks in general, outside the context of the specific hypothetical hurricane situation presented. This includes factors that previous research has found to influence hurricane evacuation decisions, such as perceived residence in a flood or evacuation zone, prior evacuation planning, prior hurricane evacuation, and other related hurricane-related experiences 5 , 6 , 7 , 16 , 45 , 53 .

This article analyzes these data to investigate three research questions:

To what extent do different types of situation-specific risk perceptions and efficacy beliefs predict evacuation intentions?

How do situation-specific risk perceptions and efficacy beliefs interact in influencing evacuation intentions?

How do situation-specific perceptions and beliefs compare with general hurricane-related factors as predictors of evacuation intentions?

Related to RQ2, we also explore whether high hurricane risk perceptions are a prerequisite to high response efficacy and protective responses, and whether these data suggest that people with high risk perceptions and low efficacy tend to engage in non-protective rather than protective responses. In addition, we explore what might underlie respondents’ response efficacy as measured here. Figure 1 depicts a simplified version of the conceptual model that informs our analysis, synthesized from the theories and prior research results discussed above.

By investigating the extent to which different situation-specific and other factors directly influence evacuation intentions, we aim to improve understanding about what can help motivate people at risk from hurricanes to take protective actions. More broadly, by investigating different types of risk perceptions along with efficacy beliefs, we aim to develop new knowledge about how different dimensions of these concepts interact with natural hazard decision making. Our goal is to explore these topics within the scope of the hypothetical hurricane situation and measures used in this survey. The findings presented here can inform future empirical research by illustrating the importance of studying multiple dimensions of risk perceptions along with situation-specific efficacy beliefs. They also help advance understanding about how existing risk theories apply in the context of near-term, approaching natural hazards.

Situation-specific perceptions and beliefs as predictors of hurricane evacuation intentions

To investigate RQ1, Table 2 shows the results from a series of regression analyses investigating which of the situation-specific perceptions and beliefs investigated here help predict evacuation intentions, and how much. As shown in the top-left box of Fig. 1 and described in Methods, all respondents received three types of information about the hurricane risk: (1) an introduction to the scenario, (2) experimentally manipulated message conditions, in which different respondents received different combinations of hurricane risk messages, and (3) information about evacuation. Model 1 shows that, as found in ref. 12 , the individual/household characteristics and the three experimentally manipulated message conditions explain only a small amount (2%) of the variance in evacuation intentions in these data. Thus, while we include the experimental message conditions as controls in subsequent regression analyses, we focus on the other variables investigated, and we interpret survey participants’ responses to the hurricane scenario in terms of the information that was received by all respondents.

Models 2–5 test adding different sets of situation-specific perceptions and beliefs as predictors in Model 1. In Model 2, adding the three cognitive risk perception variables related to the overall (cross-hazard) hurricane threat explains an additional 58% of the variance in evacuation intentions—considerable explanatory power for this type of data. Model 5 shows that adding the two efficacy variables explains even more of the variance in evacuation intentions: 67%. Models 3 and 4 indicate that respondents’ perceptions of which hurricane hazards are a threat to their home in this situation can also help explain evacuation intentions, as can their affective risk perceptions (worry and fear). However, these latter two sets of variables have less explanatory power than the other three cognitive risk perceptions or the efficacy beliefs.

Model 6 includes all of the situation-specific variables in Models 2–5 as predictors of evacuation intentions, in the same regression model. In this analysis, the three cognitive risk perception variables related to the overall threat and the two efficacy variables remain statistically significant predictors, and the hurricane hazard and affective risk perception variables are not ( p  = 0.07–0.94). Respondents’ evacuation-related response efficacy is the strongest predictor of their evacuation intentions, followed by their perceived likelihood of getting hurt, perceived severity of the hurricane at their home, and evacuation-related self-efficacy (see Supplementary Table S1 for standardized regression coefficients). Model 6 explains 73% of the variance in evacuation intentions, a small increase from Model 5, which included the two efficacy variables but none of the risk perception variables.

Together, these results indicate that, consistent with some of the research discussed in the introduction, respondents’ perceptions of what geophysical hazards are a threat to their home from an approaching hurricane can help predict their evacuation intentions, as can their worry and fear about the situation. However, these variables are no longer predictors when overall cognitive risk perceptions and efficacy beliefs are included in the same regression analysis. This suggests shared variance, or possibly mediated relationships, in which respondents’ hazard perceptions and negative affect influence evacuation intentions indirectly through their influence on other cognitive risk perceptions and efficacy beliefs. Given the goals of this article, we do not test these types of more complex relationships explicitly; however, they have been found in other studies 54 , 60 .

Interactions among situation-specific risk perceptions and efficacy beliefs

The three risk theories summarized in the introduction also posit more specific relationships among risk perceptions, efficacy beliefs, and decisions. According to the PMT, EPPM, and PADM, when a person receives information about a potential threat, they first appraise the perceived risk. If perceived risk is sufficiently low, then no protective action needs to be considered. If perceived risk is sufficiently high, then the person initiates a second appraisal, this time of protective actions that may alleviate the risk. Although the specifics differ, in all three theories this second appraisal includes constructs related to response efficacy and self-efficacy. The two appraisals then combine to influence if and how the individual responds to the threat.

If a person’s perceived risk and efficacy are both sufficiently high, theory predicts that they will be motivated to take a protective response, which may then lead to engaging in protective behaviors such as evacuation. Some empirical studies in other contexts have found these predicted interactions between risk perceptions and efficacy beliefs in predicting protective responses, while other studies find no or alternative interactions 27 , 28 , 29 , 30 , 34 , 38 , 61 , 62 , 63 , 64 .

In this section, we investigate how situation-specific risk perceptions and efficacy beliefs interact in influencing evacuation intentions—RQ2—using two approaches. Building on the results in the previous section, the first approach examines these interactions statistically using multiple linear regression models (Table 3 ). We also examine several interactions in greater detail by analyzing how evacuation intentions vary across a range of combinations of situation-specific variables (Fig. 2 ). These more in-depth analyses investigate whether these data exhibit the types of non-linear or threshold effects posited by the EPPM, as discussed above. Other (e.g., non-protective) responses to the hurricane risk information presented are discussed in a later section.

Left panels: Matrices depicting mean evacuation intentions for respondents with different combinations of situation-related response efficacy and ( a ) self-efficacy, ( c ) perceived likelihood of getting hurt, ( e ) perceived severity at home, or ( g ) perceived likelihood of home affected. The background of each cell is colored on a yellow (low) to green (high) scale based on the value of mean evacuation intentions. The font for the numbers in each cell is colored gray (low) to black (high) based on the number of respondents with the variable combination represented by that cell, in other words, based on the N used to calculate that cell’s mean evacuation intentions; cells with N  < 5 are left blank. Right panels: Box and whisker plots depicting the same data as in the left panels, to illustrate variability across respondents. Mean and median evacuation intentions, along with the interquartile range (IQR), whiskers representing 1.5 * IQR, and inner and outlier points, are shown for respondents with different combinations of situation-related response efficacy and ( b ) self-efficacy, ( d ) perceived likelihood of getting hurt, ( f ) perceived severity at home, or ( h ) perceived likelihood of home affected. For clarity, risk perceptions and efficacy beliefs are compacted from 7 to 4 categories.

To investigate interactions statistically, we use a more parsimonious version of Model 6, Model 6a, which is described in Methods. When two-way interactions among the three cognitive risk perception variables are added to Model 6a, along with interactions between the two efficacy variables, two of the four interactions are statistically significant: Severity at home * Likelihood of getting hurt (−0.049, p  < 0.001) and Self-efficacy * Response efficacy (0.041, p  < 0.001). When two-way interactions between each of the three cognitive risk perception variables and each of the two efficacy variables are added to Model 6a, only one of the six interactions is statistically significant: Likelihood of getting hurt * Response efficacy (−0.061, p  < 0.001). When these three interactions are added to Model 6a together, Severity at home * Likelihood of getting hurt is no longer statistically significant (−0.019, p  = 0.09). Thus, our final model with interactions is Model 6b, with two interactions as shown in Table 3 . Including these interactions does not change the variance explained by the model; however, along with the additional analyses depicted in Fig. 2 , it does help elucidate several of the variables’ effects.

The Self-efficacy * Response efficacy interaction in Table 3 is positive. This indicates that the influence of self-efficacy on evacuation intentions tends to be greater for respondents with higher response efficacy, and vice versa. More specifically, Fig. 2 a, b shows that among respondents with high to very high response efficacy, higher self-efficacy is associated with higher evacuation intentions. For those with low to moderate response efficacy, however, self-efficacy has limited influence on evacuation intentions. In other words, the analysis in Fig. 2 indicates that if respondents do not believe that evacuating is effective, they are unlikely to evacuate regardless of their belief in their ability to evacuate. In contrast, although few respondents reported low self-efficacy, higher response efficacy is associated with higher evacuation intentions across all levels of self-efficacy.

The Likelihood of getting hurt * Response efficacy interaction in Table 3 is negative. This indicates that the influence of response efficacy on evacuation intentions tends to be smaller for respondents with higher perceived likelihood of getting hurt, and vice versa. More specifically, Fig. 2 c, d shows that respondents have low evacuation intentions if their response efficacy and perceived likelihood of getting hurt are both low, and moderate to high evacuation intentions if either is high. Note, however, that few respondents reported high perceived likelihood of getting hurt and low response efficacy.

The analyses in Fig. 2c–h also show an additional result that is not evident in the regression models. For each of the three cognitive risk perception variables related to the overall hurricane threat, the results on the left-hand side of Fig. 2 c–d, e–f, and g–h illustrate that even respondents with low risk perceptions can have high response efficacy and high evacuation intentions. This is counter to the predictions of the risk theories discussed above, which suggest that if risk perceptions are low, people will not consider taking protective action. Instead, even among respondents with low risk perceptions, higher response efficacy is associated with higher likelihood of evacuating.

More generally, the yellow to green gradation from bottom to top within Fig. 2a, c, e , and g again illustrates the strong effect of respondents’ evacuation-related response efficacy on their evacuation intentions. Figure 2b, d, f , and h show that some respondents with high response efficacy have low evacuation intentions, but most do not. This dominant role of efficacy across levels of risk perceptions differs from discussions in much of the literature described above, although it has been observed in other contexts 30 , 41 .

Comparison of situation-specific with general hurricane-related variables as predictors of evacuation intentions

Next, we investigate RQ3, using Models 7 and 8 in Table 4 . Model 7 tests adding to Model 1 six of the general hurricane-related variables that the survey measured outside the context of a specific approaching hurricane scenario. The results show that, as found in ref. 12 , these factors—respondents’ perceptions of whether their home is in a flood or evacuation zone, prior evacuation planning, and experiences with home flooding and Hurricane Sandy—can help explain their evacuation intentions in the hurricane scenario studied here. This is consistent with other studies’ findings that these types of perceptions, preparations, and experiences can help predict people’s protective behaviors during natural hazard threats 5 , 6 , 7 , 65 . Note, however, that the adjusted R 2 for Model 7 is 0.14, compared to, e.g., 0.73 in Model 6a in Table 3 . In other words, these general hurricane-related factors explain much less of the variance in evacuation intentions than the situation-specific cognitive risk perception and efficacy belief variables investigated above.

To compare the explanatory power of these general hurricane-related factors with situation-specific perceptions and beliefs more directly, Model 8 includes the variables in Models 7 and 6a in the same regression analysis. As in Table 2 , adding the situation-specific variables as predictors results in a large increase in the variance explained by the regression model. Of the six general hurricane-related variables that were predictors in Model 7, only two remain statistically significant ( p  < 0.05): whether respondents thought they were in a flood zone and whether they said they evacuated for Hurricane Sandy before landfall. The other four general hurricane-related variables are no longer direct predictors of evacuation intentions. Again, this suggests that these factors may influence evacuation intentions in other ways, such as indirectly through their influence on situation-specific risk perceptions and efficacy beliefs. Although we do not test these mediated paths explicitly, they are consistent with some other research 16 , 35 , 39 , 54 .

Note that the two general hurricane-related variables that remain direct predictors in Model 8 are both dichotomous (0 or 1), whereas the cognitive risk perception and efficacy belief variables are on a 1–7 scale. This, along with the standardized coefficients for Model 8 shown in Supplementary Table S2 , provides further evidence that the situation-specific cognitive risk perceptions and efficacy beliefs are stronger direct predictors of respondents’ evacuation intentions than the general hurricane-related perceptions, preparations, and experiences measured in this survey.

Other responses to hurricane risks

Along with protective responses such as evacuation, the three theories discussed earlier in this article include the potential for people to engage in other, non-protective responses to risks (Fig. 1 ). These non-protective responses—which are also referred to as maladaptive responses or emotion-focused coping—include defensive avoidance, denial, or negative reactance, e.g., minimizing the information as “overblown” or perceiving manipulation through “misleading” information 60 , 66 . The EPPM posits more specifically that when perceived risk elicits sufficient fear, but efficacy is low, a person will engage in non-protective responses to control their fear—and there may even be a boomerang effect, where they react to the information by engaging in more risky behavior rather than taking a recommended protective action (e.g., refs. 19 , 67 , 68 ).

Although some studies have concluded that people with high risk perceptions and low efficacy tend to exhibit non-protective responses instead of protective responses 27 , 37 , others have not 28 , 60 , 63 , 64 . Following on from previous research investigating the effects of fear-arousing hazardous weather risk information 10 , 69 , 70 , we explore this topic in these survey data using the information perception variables in Table 1 . These questions were included in the survey to measure negative reactance to information, a type of non-protective response.

As shown in Table 5 , respondents’ perceptions that the information provided about the hazardous weather situation is misleading or overblown are negatively correlated with their evacuation intentions. These information perceptions are also negatively correlated with respondents’ self and response efficacy. Both results are consistent with the EPPM predictions that people with lower efficacy will engage in non-protective rather than protective responses. However, all of these correlations are weak—much weaker than most of the other correlations with evacuation intentions in Table 5 .

To investigate these relationships further, we conducted regression analyses similar to those in Table 2 , with respondents’ information perceptions as the dependent variable. Self and response efficacy were statistically significant predictors ( p  < 0.01), but the model’s adjusted R 2 was only 0.04. In other words, variables other than risk perceptions and efficacy beliefs explain most of the variability in information perceptions. We also examined how respondents’ information perceptions vary as both risk perceptions and efficacy beliefs change, similar to Fig. 2c–g , and it did not appear that respondents with high risk perceptions but low efficacy tend to perceive the risk information as overblown or misleading. Further investigation revealed that many of the respondents who agreed that the information was misleading and/or overblown reported high evacuation intentions. All of these results are counter to EPPM predictions.

Overall, these results suggest that while a few respondents with low efficacy may be engaging in negative reactance rather than intending to take protective action, such behavior is not common in these data. Instead, respondents’ perceptions that the information is overblown or misleading may be functioning primarily as negative attitudes toward hurricane risk information and not necessarily as emotion-focused coping in lieu of protective responses 15 . However, these types of information perceptions are only a subset of possible emotion-focused coping responses discussed in the risk literature, and certain subpopulations may be more likely to engage in these types of responses 15 . Moreover, although Table 1 indicates that the information provided about the approaching hurricane did induce fear and worry among many recipients, only a subset of respondents received high-impact or fear-appeal messages in the experimentally manipulated message conditions 12 . Thus, further investigation of emotion-focused coping is needed in the context of hurricane risk communication.

Further exploration of efficacy

Across the analyses discussed above, evacuation-related response efficacy is a consistently strong predictor, which suggests that it is a significant driver of respondents’ evacuation intentions in the scenario presented. In addition, as discussed in the introduction, hurricane-related efficacy has been less extensively studied than hurricane risk perceptions. Thus, we close the analysis by further exploring what might underlie the efficacy measures used here and why response efficacy, in particular, might offer so much explanatory power for evacuation decisions in these survey data.

Looking at the response efficacy measure in Table 1 , we see that it relates to this specific hazardous weather situation and the specific protective action of interest: evacuating one’s home. The measure also refers to the hurricane’s possible negative impacts that the protective action may help reduce: harm to oneself or one’s family. In addition, as described in Methods, all respondents lived in areas that had recently experienced Hurricane Sandy, and the scenario presented in the survey said that a strong hurricane was approaching, with wind speeds up to 130 miles per hour. The information that all respondents received further stated that people living in evacuation zones should evacuate, and it briefly described options for evacuating. All of these may contribute to the explanatory power of response efficacy in this study.

As another approach to understanding this measure of response efficacy, we examined what other variables measured in the survey are associated with higher or lower response efficacy. As shown in Table 5 , of the situation-specific perceptions and beliefs, the three cognitive risk perceptions related to the overall threat have the strongest correlations with response efficacy. Similar to the response efficacy measure, all three of these risk perception measures relate to the personal risks that the hurricane poses to the respondent or their home—and the strongest correlation is with the likelihood of getting hurt measure. This suggests that an important component of this response efficacy measure is the wording related to reducing personal harm.

Table 6 shows that response efficacy is correlated with all of the six general hurricane-related perceptions, preparations, and experiences analyzed in this study, but not as strongly as with the situation-specific measures. Response efficacy is not meaningfully correlated with any of the four individual/household variables included as controls in the analysis. Correlations are also insignificant for other individual/household characteristics measured in the survey, including income, housing type, home ownership, head of household or employment status, household size, and presence of children in home (|r | <0.06, p  > 0.10). Together, these results suggest that the response efficacy measure used here is more closely related to the personal risk that respondents perceive in this specific hurricane situation—and their beliefs about the extent to which evacuation can reduce this personal risk—than to the types of general hurricane-related perceptions and experiences measured here or generally available demographic data.

Although the response efficacy variable used in this analysis was measured in the context of the specific hurricane situation presented, it could be partly (or largely) associated with respondents’ general evacuation-related response efficacy, across different hurricane situations. Supporting this idea, of the six general hurricane-related perceptions, preparations, and experiences investigated here, evacuation for Hurricane Sandy has the strongest correlation with response efficacy (Table 6 ), and it is the strongest predictor of evacuation intentions (Model 7 in Table 4 ; see also ref. 12 ). This is also consistent with other research, which finds that prior hurricane evacuation is a predictor of future evacuation, or more generally, that some people tend to be “evacuators” who believe in general that evacuation is likely to reduce harm, while others are “non-evacuators” who will not evacuate in most circumstances 6 , 8 , 58 , 71 , 72 , 73 . Unfortunately, we cannot fully investigate this hypothesis using these data, because the survey did not measure respondents’ general evacuation-related response efficacy. The survey also did not measure response efficacy in the other three experimental modules that were part of the survey. However, the survey did measure respondents’ evacuation intentions in the other three experimental modules, each of which presented a different hurricane scenario (see Methods). Thus, as our best available proxy for respondents’ propensity to evacuate across multiple hurricane situations, we use their average evacuation intentions in the other three experimental modules that were part of the survey.

As shown in Table 6 , this average evacuation intention variable is strongly correlated with response efficacy. When this variable is added to the regression analysis in Model 8, Model 9 in Table 4 shows that it is a statistically significant predictor of situation-specific evacuation intentions. In Model 9, the regression coefficient for response efficacy is somewhat smaller than in Model 8, but response efficacy remains a strong predictor of evacuation intentions. This suggests the response efficacy measure used here is partly associated with respondents’ general beliefs that evacuation is effective at reducing personal harm from hurricanes, and partly associated with their beliefs that evacuation is effective in the specific hurricane scenario presented. Further work is needed with additional measures of general and situation-specific efficacy, along with other measures such as prior evacuation experience, to further explicate these relationships.

As discussed earlier, our analyses find that the two efficacy measures used in this survey interact, with self-efficacy influencing evacuation intentions primarily among respondents with moderate to high levels of response efficacy. Note, however, that we cannot infer causality from these data; respondents’ self-efficacy may influence their appraisals of response efficacy, rather than vice versa. Moreover, our ability to investigate the role of self-efficacy using these data is limited by the small number of respondents reporting low self-efficacy: only 6% of the sample reported 1, 2, or 3 on the 7-point self-efficacy scale. We also did not measure evacuation costs and impediments, which are likely related to self-efficacy 33 , 74 and are important components of both the PMT and PADM. Such barriers to evacuation are likely to be more important for actual evacuation behaviors, compared to the evacuation intentions studied here, especially for populations that are likely to experience the most harm. Thus, while this analysis is a step towards understanding the importance of response and self-efficacy for hurricane evacuation decision making, additional research is needed to understand what underlies different types of efficacy and how these influence people’s responses to approaching hazard risks.

This article uses data from a hypothetical hurricane situation presented in a survey to examine the roles of different factors in influencing evacuation decisions. Our analysis finds that the strongest predictors of respondents’ evacuation intentions are their beliefs about the effectiveness of evacuation for reducing personal harm (response efficacy) and their perceptions that they could get hurt if they stay home during the hurricane. These types of situation-specific cognitive risk perceptions and response efficacy beliefs explain a much larger amount of the variance in evacuation intentions than respondents’ worry, fear, or perceptions of the hurricane’s wind, storm surge, or rain flooding hazards. Respondents’ beliefs about their ability to evacuate (self-efficacy) are also influential, but primarily for those with moderate to high response efficacy.

Similar to the prior research discussed in the introduction (e.g., refs. 5 , 6 , 7 , 8 , 16 ), we also find that variables measured outside the context of the specific hurricane situation, including individual/household characteristics, perceived hurricane-related exposure, and past experiences, can help predict evacuation intentions. However, the situation-specific risk perception and efficacy belief variables explain a larger amount of the variance in evacuation intentions. Together, these findings illustrate the value of including people’s situational perceptions of personal risk and protective action beliefs in studies of natural hazard decision making.

Aspects of these results may be influenced by the hypothetical nature of the situation posed in the survey. For example, affect is likely to be more important when people are facing a real hurricane threat, and evacuation impediments and associated self-efficacy are likely more important when people must actually evacuate. However, even in this hypothetical situation, many respondents reported feeling worry and fear. In addition, our results on the importance of respondents’ perceived likelihood of getting hurt and their beliefs about evacuation reducing harm are consistent with prior analyses that people stay at home as a hurricane approaches when they feel safe in their home, and evacuate when they do not 4 , 8 , 45 , 50 , 51 , 71 , 72 . Moreover, the variance explained by several of the regression models investigated here is 60% or greater, which is high for analyses with these types of data (see, e.g., refs. 36 , 37 , 38 , 39 , 47 , 48 , 53 , 54 , 54 , 58 , 72 , 75 ). This suggests that some of these results are likely to extend to real hurricane situations, although they may be attenuated.

What might account for the large explanatory power of response efficacy in this study, along with the cognitive risk perceptions related to the overall hurricane threat? One possibility is that the response efficacy measure used here asks about the risk to the respondent in this specific hazardous weather scenario, which is likely to be a strong motivator. The response efficacy measure used here also asks about a specific protective action that can reduce harm in this scenario, evacuating one’s home. These characteristics differ from the risk perception or efficacy measures used in some other studies, which are more general or ask about risk targets or actions that are less directly connected to the respondent and the situation 46 , 52 , 75 . In addition, since the hurricane scenario is hypothetical rather than real, the response efficacy and evacuation intention measures have a similar hypothetical phrasing; however, the cognitive risk perception measures are phrased differently, asking more directly about the threat (Table 1 ), and still have substantial explanatory power.

Other possible contributors to the large explanatory power of response efficacy include the nature of the hurricane scenario, risk information presented, and survey sample. The survey presented all respondents with a scenario of a strong hurricane, equivalent to a Category 4 storm, approaching their area. For many members of the coastal population sampled here, who lived in areas that experienced Hurricane Sandy several years prior to the survey, this may have been a highly salient risk, prompting concerns about harm and awareness of the need for protective action. The efficacy information presented to all respondents, which said that people in evacuation zones should evacuate and briefly described options for evacuation, may have also influenced the role of response efficacy. These two components of the information about the hurricane scenario were not experimentally manipulated, and so additional research is needed to understand the effects of the type of efficacy information included. Research with other data sets is also needed to further understand what underlies the types of response efficacy measure used in this survey, as well as the extent to which these results generalize to other situations and populations.

Our investigation of how survey respondents’ risk perceptions interacted with efficacy to influence their responses to risks found several results counter to expectations from the risk communication theories discussed above. For example, rather than a positive interaction, we found negative or non-significant interactions between risk perceptions and efficacy in influencing evacuation intentions. Moreover, our more in-depth analysis of interactions found that some respondents with low risk perceptions said they were likely to evacuate, if their response efficacy was high. This suggests that risk perceptions may not be antecedent to response efficacy, at least in this context. One possible explanation is that members of this population are reporting beliefs about the effectiveness of evacuation based on their hurricane experience, even if they perceive low risk in this situation. Or, given the dynamic nature of hurricane threats, respondents may be aware that even if they do not perceive high risk based on the current forecast, their risk may increase as the hurricane approaches and evolves 43 . These findings can inform applications of existing risk theories for understanding protective decision making and improving risk communication in near-term, approaching hazard situations, including hurricanes. If further research finds that these results extend to other situations, they can also help advance theory by informing models of how risk perceptions, efficacy, and other constructs influence people’s responses to risks.

One limitation of this study, noted above, is that people’s responses in hypothetical situations can differ from their responses during actual hazard threats. Research has found a correspondence between people’s intended and actual behaviors 25 , 62 , 71 , 76 , 77 , 78 , and the hypothetical situation enabled us to explore the concepts and relationships of interest here in a simplified, more controlled context. At the same time, this simplified context focuses on individual decision making, without considering the social processes in evacuation decision making (e.g., refs. 4 , 5 , 10 , 54 , 56 , 57 , 75 , 79 ) or more participatory approaches to risk management (e.g., ref. 80 ). In addition, people’s decision-making processes and responses can also vary across regions and populations with different characteristics and experiences, and across hazard situations. Thus, it is important to investigate the topics studied here in other populations and hazards contexts, to understand the generalizability and potential implications of these results.

Along with the areas mentioned above, our study suggests several additional topics for further research, using data sets with additional measures. One is understanding what influences and explains the type of response efficacy measure used here, to build further understanding about the underlying drivers of evacuation decision making. This includes investigating how much the type of response efficacy measure used here is determined by people’s general beliefs about the effectiveness of a protective action, and how much it is influenced by the specific situation. Another topic for further research is improving understanding about how risk perceptions and efficacy beliefs interact in hazard decision making. More specifically, what are the pathways from risk information to protective and other responses, and what are the roles of different types of pre-existing and situation-specific characteristics, perceptions and beliefs? Although these topics can be investigated using cross-sectional studies, a limitation of such work (including the study discussed here) is that one cannot explicitly test causal relationships. Thus, longitudinal studies that measure how individuals’ information use, perceptions, beliefs, and behaviors evolve over time may be especially valuable 81 , 82 . The analysis presented in this article provides valuable insight for informing these types of follow-on work.

Finally, in each of these areas, it is important to investigate these topics for different populations. For example, most of our respondents reported moderate to high self-efficacy, but capacities and constraints are key factors limiting protective behaviors for some populations. Thus, although response efficacy may be highly influential for many people, removing evacuation barriers or otherwise enabling capacity (both generally and in specific situations) can be critical for others. As another example, our results together with other research suggest that some people would typically evacuate or not across a variety of hurricane threats, while others decide based on the situation. Which factors most influence evacuation decisions may vary across these populations, leading to different strategies for effective risk communication.

Even with these potential limitations and future research needs in mind, this study suggests that persuading people at high risk that they or their families may be harmed if they stay home during a hurricane—and that evacuating can reduce the risk of personal harm—may be important levers for using risk communication to motivate hurricane evacuation. Our results further suggest that, as discussed by ref. 28 , arousing fear may not be as important as effectively conveying possible negative consequences and strategies for reducing them (see also, e.g., refs. 25 , 34 , 37 , 40 , 64 , 83 ). A corresponding implication is the need for testing communication strategies that can increase response efficacy, during specific hurricane situations and more generally over time. Since believing that evacuation will reduce harm will not enable evacuation for people who do not have the ability to evacuate, or who do not believe that they can, it is also important to advance interventions that increase capacity and self-efficacy for diverse populations. Additional research on these topics can inform the design of hazard risk communications that help a variety of people at risk reduce harm when natural hazards threaten.

Survey data collection and sample

This study builds on previous research that used data from the same survey to investigate other topics 12 , 13 , 14 . Here we provide an overview of the survey data collection and sample; additional details are provided in refs. 12 , 13 , 14 .

The survey data were collected online in 2015 from 1716 residents of coastal areas in three U.S. states (Connecticut, New York, and New Jersey) that were affected by Hurricane Sandy in 2012. Near the time of landfall, Sandy transitioned from a hurricane to a post-tropical cyclone, and so the storm is also called Post-Tropical Cyclone Sandy 84 or Superstorm Sandy 85 . Thus, the survey referred to the storm as “Sandy”. For simplicity, however, in this article we refer to the storm as “Hurricane Sandy”.

Survey data collection and sampling was managed by GfK Custom Research, with financial incentives provided to participants. The research protocol was approved by the Institutional Review Board at Rutgers University, and written informed consent was obtained from all participants. The sample was recruited using GfK’s probability-based online panel, along with non-probability opt-in online recruitment to obtain additional respondents in the targeted geographic areas. Respondents were recruited using the ZIP code of their primary residence, with ZIP codes selected for sampling based on U.S. National Weather Service (NWS) risk assessments of hurricane storm surge flooding 86 . More specifically, using NWS MOM [Maximum of MEOW (Maximum Envelopes of Water)] data to represent areas with potential for storm surge flooding from a category 2 hurricane, respondents were recruited from ZIP codes with 40% or more of the landmass in those areas in New Jersey and New York, and 1% or more of the landmass in those areas in Connecticut. Although the sample was limited to people who lived in these areas at the time of the survey, 9.3% reported living in a different home during Hurricane Sandy.

The survey sample included respondents with a mix of sociodemographic characteristics such as age, gender, education, income, and race/ethnicity 13 . At the time of the survey, 61.7% of respondents lived in areas at risk from hurricane storm surge inundation, and 19.5% of respondents lived in an officially designated 100-year floodplain 13 ; 30.9% lived outside both risk areas (percentages do not sum to 100% due to overlap in the risk areas). Across the sample, 12.9% of respondents said they had evacuated prior to Hurricane Sandy, and 58.6% reported preparing their residence 12 . When asked how much property damage and emotional distress they experienced due to Hurricane Sandy, respondents reported a median value of 2 for each, on a 1–4 scale 12 . In other words, even though we sampled respondents from areas that were at risk during Hurricane Sandy, respondents lived in areas with varying levels of hurricane risk, and they had a range of experiences related to Hurricane Sandy.

Survey instrument and measures

The survey began with a set of questions to screen participants, provide data for fields used later in the survey, and measure potentially relevant variables outside the context of the specific hazardous weather situations presented. This included questions about whether respondents thought they lived in an officially designated flood zone or hurricane evacuation zone, referred to as their perceived residence in a flood or evacuation zone (response options: Yes, No, Don’t Know). It also included questions about whether respondents had an evacuation plan (response options: Yes, No), whether their home had previously flooded (response options: Yes, No, Don’t Know), whether they had evacuated for Hurricane Sandy before landfall (Yes or No, coded as in ref. 12 ), and how much emotional distress they had experienced due to Hurricane Sandy (response options: 1 = None to 4 = A lot). Measures for these variables and their summary statistics are provided in Table 1 in ref. 12 .

This set of questions was followed by a series of four separate experimental modules, each of which presented information about a different hypothetical scenario of an approaching hurricane or other coastal storm and then asked a set of questions related to that scenario. All respondents received all four modules, presented in random order. This article focuses on one of the four experimental modules that were part of the survey.

In the survey module studied here, all respondents were presented with the same introduction to the scenario: “Imagine that a hurricane is approaching [insert state]. You receive the following information from the National Weather Service: A hurricane is predicted to make landfall two days from now, with wind speeds of up to 130 miles per hour.” Each respondent then received additional information about the threat, which included an embedded experimental design that randomly assigned respondents to receive different combinations of three message conditions: Hazard, Impact, and Fear (shown in Figure 3 of ref. 12 ). All respondents were then presented the same information about recommended behavioral responses: “You should evacuate if you live in an evacuation zone. Options for evacuation include a hotel or the home of family or friends located outside the evacuation area, or an emergency evacuation shelter.” This information about evacuation was included based on prior work indicating that effective hazard risk communications convey information about recommended protective actions along with threat information (e.g., refs. 27 , 64 , 87 ).

After receiving this combination of information within the survey module, each respondent was asked a set of questions related to the hurricane situation, including those shown in Table 1 . These measures of intended protective behaviors, cognitive and affective risk perceptions, efficacy beliefs, and perceptions of the information presented were developed based on the previous research discussed in the introduction. Three of the cognitive risk perception measures asked about respondents’ perceptions of the overall threat posed by the hurricane: perceived likelihood and severity at their home, and, given prior research suggesting that people take protective actions if they feel personally vulnerable or unsafe 4 , 8 , 28 , 45 , 50 , 65 , 72 , perceived likelihood of getting hurt if they stay home. The other three cognitive risk perception measures asked whether or not respondents thought that each of three major hurricane-related hazards—strong winds, storm surge flooding, and rain flooding—were a threat to their home from the hurricane. The two affective risk perception measures asked about respondents’ fear and worry, and the two efficacy belief measures asked about respondents’ evacuation-related self and response efficacy. Respondents were also asked whether they thought the information presented was overblown or misleading as measures of reactance, a type of non-protective response discussed in the fear appeals literature 10 , 53 , 60 , 66 .

In addition to the situation-specific variables measured in this experimental module, this article also uses data on respondents’ evacuation intentions from the other three experimental modules that were part of the survey. These data represent respondents’ evacuation intentions in the scenarios presented in each of those modules, which were measured using the same survey question shown in Table 1 in this article. We calculated the average of each respondents’ evacuation intentions across the other three modules and used this variable (mean = 4.66, SD = 1.64) in the analyses as an indication of respondents’ propensity to evacuate or not across a variety of hurricane scenarios.

Tables 5 and 6 present Pearson correlations for the variables investigated in this paper, with 2-tailed significance tests. Spearman correlations were also calculated, with similar results. As shown in Table 5 , many of the situation-specific perceptions and beliefs examined here are correlated, several with moderate to strong correlations. However, given our interest in investigating the relative importance of different predictors of evacuation intention, we decided to leave each of these variables separate, rather than conducting factor analysis or forming scales related to the broader concepts. This approach is consistent with several studies that have emphasized the value of investigating the distinct roles of different components of risk perceptions and efficacy beliefs 34 , 38 , 62 . It also allows us to interpret our results in the context of other work that examines these variables separately, as components of or responses to risk messages.

Data analysis

We conducted the statistical analyses in this article using IBM SPSS Statistics for Windows, version 28.0.1.1. The primary analyses are multiple linear regression models with evacuation intentions as the dependent variable. The number of respondents, N , varies slightly across analyses due to missing data. Although the conceptual model in Fig. 1 and other work suggests that some of the variables investigated here may have more complex relationships (e.g., refs. 16 , 35 , 36 , 39 , 40 , 54 , 79 ), here we focus on testing which variables are the strongest predictors of evacuation intentions. Thus, we test direct effects, which also provides a starting point for investigating more complex relationships in future work.

All of the regression models include four individual/household characteristics as predictors: age, gender (coded as 0 = male, 1 = female), race/ethnicity (recoded into two categories: 0 = white non-Hispanic, 1 = other), and education (recoded into three categories: high school graduate or less, some college, Bachelor’s degree or higher). All of the regressions also include as predictors the three experimentally manipulated message conditions embedded within the survey module examined here. The Impact and Fear message conditions are dichotomous, and the four Hazard message conditions were recoded into a dichotomous variable (0 = wind only, 1 = any added flood message) for this analysis. As a starting point for comparing subsequent results, Model 1 (Table 2 ) includes only these variables as predictors of evacuation intentions.

To investigate RQ1, Models 2–6 (Table 2 ) compare different types of situation-specific perceptions and beliefs as predictors of evacuation intentions. Models 2–5 add the situation-specific predictor variables to Model 1 in four conceptual sets: cognitive risk perceptions related to the overall (cross-hazard) hurricane threat and specific hurricane hazards, affective risk perceptions, and efficacy beliefs. Model 6 includes all of the predictors in Models 2–5 in the same regression analysis.

For use in subsequent analyses, we developed a more parsimonious regression model, Model 6a (Table 3 ), by removing the five situation-specific variables that were not statistically significant predictors of evacuation intentions in Model 6; this leaves three cognitive risk perception and two efficacy variables. Comparing Table 3 with Table 2 shows that the regression coefficients and adjusted R 2 in Model 6a and Model 6 are nearly identical. As part of investigating RQ2, we then tested interactions among the five situation-specific variables in Model 6a, as described in Results. All of the models with interactions had similar adjusted R 2 (0.73).

To investigate RQ3, Models 7–8 (Table 4 ) compare respondents’ general hurricane-related perceptions, preparations, and experiences to their situation-specific perceptions and beliefs as predictors of evacuation intentions. Starting with Model 1, Model 7 adds as predictors six general hurricane-related factors measured in the first part of the survey, outside the context of the hurricane scenarios. We selected these variables based on previous findings that they are predictors of evacuation intentions, using the same survey data set analyzed here 12 . Model 8 adds to Model 7 the five situation-specific perceptions and beliefs that were predictors of evacuation intentions in Model 6a.

The last regression model, Model 9 (Table 4 ), is included as part of our additional exploration of response efficacy. It adds to Model 8 respondents’ average evacuation intentions across the other three experimental modules that were part of the survey, as a proxy for their general propensity to evacuate.

Results from the regression analyses are presented in tables using unstandardized coefficients, along with standard errors. To support results on the relative importance of different predictors of evacuation intentions, standardized regression coefficients for Models 1–9 are presented in supplementary information. Because the primary independent variables of interest are measured on the same 7-point scale and have similar standard deviations (Table 1 ), comparing unstandardized and standardized coefficients yields similar interpretations.

To test for collinearity in the regression analyses, we examined Variance Inflation Factors (VIFs). In Model 6 (Table 1 ), the largest VIFs are 3.0–3.1, for fear and worry, and most of the other VIFs are less than 2.5. In Models 8 and 9, the largest VIFs are 2.8–3.0 and most other VIFs are less than 2.5. Removing the variables with the highest VIFs produces little change in the results. This, along with the similarity in coefficients for key variables across Models 6, 6a, 8, and 9, indicates that our analysis approach is robust.

To investigate additional aspects of RQ2, Fig. 2 analyzes in greater depth how several of the situation-specific perceptions and beliefs interact to influence evacuation intentions. These results allow us to explore the more complex, non-linear relationships between risk perceptions and efficacy beliefs posited in the PMT and EPPM. Many published PMT and EPPM analyses compare low vs. high risk perceptions and efficacy by manipulating these aspects of messages, analyzing risk perception and efficacy data using median splits, or segmenting risk perception * efficacy interactions into four quadrants 27 , 29 , 60 , 61 . Here, we use an approach that enables us to examine interactions between risk perception and efficacy across a broader range of both constructs.

Data availability

The datasets used during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors thank our collaborators on the survey design and data collection, William Hallman and Rachael Shwom. This material is based upon work supported by the National Center for Atmospheric Research, which is a major facility sponsored by the National Science Foundation under Cooperative Agreement 1852977. This material is also based upon work supported by (while the lead author was serving at) the National Science Foundation. Data collection for this study was funded by NJ Sea Grant Coastal Storm Awareness Program, Grant R/CSAP-1-NJ. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. The funders played no role in study design, data collection, analysis and interpretation of data, or the writing of this manuscript.

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R.M. contributed to data collection, performed the data analyses shown in this article, and led interpretation of results and writing the manuscript. C.C. led data collection and contributed to the design of data analyses, interpretation of results, and writing. J.D. contributed to data collection, design of data analyses, interpretation of results, and writing. All authors read and approved the final manuscript.

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Morss, R.E., Cuite, C.L. & Demuth, J.L. What predicts hurricane evacuation decisions? The importance of efficacy beliefs, risk perceptions, and other factors. npj Nat. Hazards 1 , 24 (2024). https://doi.org/10.1038/s44304-024-00025-8

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Received : 22 December 2023

Accepted : 18 July 2024

Published : 21 August 2024

DOI : https://doi.org/10.1038/s44304-024-00025-8

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