variables in a research study examples

• Privacy Policy

Home » Variables in Research – Definition, Types and Examples

Variables in Research – Definition, Types and Examples

Table of Contents

Variables in Research

Definition:

In Research, Variables refer to characteristics or attributes that can be measured, manipulated, or controlled. They are the factors that researchers observe or manipulate to understand the relationship between them and the outcomes of interest.

Types of Variables in Research

Types of Variables in Research are as follows:

Independent Variable

This is the variable that is manipulated by the researcher. It is also known as the predictor variable, as it is used to predict changes in the dependent variable. Examples of independent variables include age, gender, dosage, and treatment type.

Dependent Variable

This is the variable that is measured or observed to determine the effects of the independent variable. It is also known as the outcome variable, as it is the variable that is affected by the independent variable. Examples of dependent variables include blood pressure, test scores, and reaction time.

Confounding Variable

This is a variable that can affect the relationship between the independent variable and the dependent variable. It is a variable that is not being studied but could impact the results of the study. For example, in a study on the effects of a new drug on a disease, a confounding variable could be the patient’s age, as older patients may have more severe symptoms.

Mediating Variable

This is a variable that explains the relationship between the independent variable and the dependent variable. It is a variable that comes in between the independent and dependent variables and is affected by the independent variable, which then affects the dependent variable. For example, in a study on the relationship between exercise and weight loss, the mediating variable could be metabolism, as exercise can increase metabolism, which can then lead to weight loss.

Moderator Variable

This is a variable that affects the strength or direction of the relationship between the independent variable and the dependent variable. It is a variable that influences the effect of the independent variable on the dependent variable. For example, in a study on the effects of caffeine on cognitive performance, the moderator variable could be age, as older adults may be more sensitive to the effects of caffeine than younger adults.

Control Variable

This is a variable that is held constant or controlled by the researcher to ensure that it does not affect the relationship between the independent variable and the dependent variable. Control variables are important to ensure that any observed effects are due to the independent variable and not to other factors. For example, in a study on the effects of a new teaching method on student performance, the control variables could include class size, teacher experience, and student demographics.

Continuous Variable

This is a variable that can take on any value within a certain range. Continuous variables can be measured on a scale and are often used in statistical analyses. Examples of continuous variables include height, weight, and temperature.

Categorical Variable

This is a variable that can take on a limited number of values or categories. Categorical variables can be nominal or ordinal. Nominal variables have no inherent order, while ordinal variables have a natural order. Examples of categorical variables include gender, race, and educational level.

Discrete Variable

This is a variable that can only take on specific values. Discrete variables are often used in counting or frequency analyses. Examples of discrete variables include the number of siblings a person has, the number of times a person exercises in a week, and the number of students in a classroom.

Dummy Variable

This is a variable that takes on only two values, typically 0 and 1, and is used to represent categorical variables in statistical analyses. Dummy variables are often used when a categorical variable cannot be used directly in an analysis. For example, in a study on the effects of gender on income, a dummy variable could be created, with 0 representing female and 1 representing male.

Extraneous Variable

This is a variable that has no relationship with the independent or dependent variable but can affect the outcome of the study. Extraneous variables can lead to erroneous conclusions and can be controlled through random assignment or statistical techniques.

Latent Variable

This is a variable that cannot be directly observed or measured, but is inferred from other variables. Latent variables are often used in psychological or social research to represent constructs such as personality traits, attitudes, or beliefs.

Moderator-mediator Variable

This is a variable that acts both as a moderator and a mediator. It can moderate the relationship between the independent and dependent variables and also mediate the relationship between the independent and dependent variables. Moderator-mediator variables are often used in complex statistical analyses.

Variables Analysis Methods

There are different methods to analyze variables in research, including:

• Descriptive statistics: This involves analyzing and summarizing data using measures such as mean, median, mode, range, standard deviation, and frequency distribution. Descriptive statistics are useful for understanding the basic characteristics of a data set.
• Inferential statistics : This involves making inferences about a population based on sample data. Inferential statistics use techniques such as hypothesis testing, confidence intervals, and regression analysis to draw conclusions from data.
• Correlation analysis: This involves examining the relationship between two or more variables. Correlation analysis can determine the strength and direction of the relationship between variables, and can be used to make predictions about future outcomes.
• Regression analysis: This involves examining the relationship between an independent variable and a dependent variable. Regression analysis can be used to predict the value of the dependent variable based on the value of the independent variable, and can also determine the significance of the relationship between the two variables.
• Factor analysis: This involves identifying patterns and relationships among a large number of variables. Factor analysis can be used to reduce the complexity of a data set and identify underlying factors or dimensions.
• Cluster analysis: This involves grouping data into clusters based on similarities between variables. Cluster analysis can be used to identify patterns or segments within a data set, and can be useful for market segmentation or customer profiling.
• Multivariate analysis : This involves analyzing multiple variables simultaneously. Multivariate analysis can be used to understand complex relationships between variables, and can be useful in fields such as social science, finance, and marketing.

Examples of Variables

• Age : This is a continuous variable that represents the age of an individual in years.
• Gender : This is a categorical variable that represents the biological sex of an individual and can take on values such as male and female.
• Education level: This is a categorical variable that represents the level of education completed by an individual and can take on values such as high school, college, and graduate school.
• Income : This is a continuous variable that represents the amount of money earned by an individual in a year.
• Weight : This is a continuous variable that represents the weight of an individual in kilograms or pounds.
• Ethnicity : This is a categorical variable that represents the ethnic background of an individual and can take on values such as Hispanic, African American, and Asian.
• Time spent on social media : This is a continuous variable that represents the amount of time an individual spends on social media in minutes or hours per day.
• Marital status: This is a categorical variable that represents the marital status of an individual and can take on values such as married, divorced, and single.
• Blood pressure : This is a continuous variable that represents the force of blood against the walls of arteries in millimeters of mercury.
• Job satisfaction : This is a continuous variable that represents an individual’s level of satisfaction with their job and can be measured using a Likert scale.

Applications of Variables

Variables are used in many different applications across various fields. Here are some examples:

• Scientific research: Variables are used in scientific research to understand the relationships between different factors and to make predictions about future outcomes. For example, scientists may study the effects of different variables on plant growth or the impact of environmental factors on animal behavior.
• Business and marketing: Variables are used in business and marketing to understand customer behavior and to make decisions about product development and marketing strategies. For example, businesses may study variables such as consumer preferences, spending habits, and market trends to identify opportunities for growth.
• Healthcare : Variables are used in healthcare to monitor patient health and to make treatment decisions. For example, doctors may use variables such as blood pressure, heart rate, and cholesterol levels to diagnose and treat cardiovascular disease.
• Education : Variables are used in education to measure student performance and to evaluate the effectiveness of teaching strategies. For example, teachers may use variables such as test scores, attendance, and class participation to assess student learning.
• Social sciences : Variables are used in social sciences to study human behavior and to understand the factors that influence social interactions. For example, sociologists may study variables such as income, education level, and family structure to examine patterns of social inequality.

Purpose of Variables

Variables serve several purposes in research, including:

• To provide a way of measuring and quantifying concepts: Variables help researchers measure and quantify abstract concepts such as attitudes, behaviors, and perceptions. By assigning numerical values to these concepts, researchers can analyze and compare data to draw meaningful conclusions.
• To help explain relationships between different factors: Variables help researchers identify and explain relationships between different factors. By analyzing how changes in one variable affect another variable, researchers can gain insight into the complex interplay between different factors.
• To make predictions about future outcomes : Variables help researchers make predictions about future outcomes based on past observations. By analyzing patterns and relationships between different variables, researchers can make informed predictions about how different factors may affect future outcomes.
• To test hypotheses: Variables help researchers test hypotheses and theories. By collecting and analyzing data on different variables, researchers can test whether their predictions are accurate and whether their hypotheses are supported by the evidence.

Characteristics of Variables

Characteristics of Variables are as follows:

• Measurement : Variables can be measured using different scales, such as nominal, ordinal, interval, or ratio scales. The scale used to measure a variable can affect the type of statistical analysis that can be applied.
• Range : Variables have a range of values that they can take on. The range can be finite, such as the number of students in a class, or infinite, such as the range of possible values for a continuous variable like temperature.
• Variability : Variables can have different levels of variability, which refers to the degree to which the values of the variable differ from each other. Highly variable variables have a wide range of values, while low variability variables have values that are more similar to each other.
• Validity and reliability : Variables should be both valid and reliable to ensure accurate and consistent measurement. Validity refers to the extent to which a variable measures what it is intended to measure, while reliability refers to the consistency of the measurement over time.
• Directionality: Some variables have directionality, meaning that the relationship between the variables is not symmetrical. For example, in a study of the relationship between smoking and lung cancer, smoking is the independent variable and lung cancer is the dependent variable.

Advantages of Variables

Here are some of the advantages of using variables in research:

• Control : Variables allow researchers to control the effects of external factors that could influence the outcome of the study. By manipulating and controlling variables, researchers can isolate the effects of specific factors and measure their impact on the outcome.
• Replicability : Variables make it possible for other researchers to replicate the study and test its findings. By defining and measuring variables consistently, other researchers can conduct similar studies to validate the original findings.
• Accuracy : Variables make it possible to measure phenomena accurately and objectively. By defining and measuring variables precisely, researchers can reduce bias and increase the accuracy of their findings.
• Generalizability : Variables allow researchers to generalize their findings to larger populations. By selecting variables that are representative of the population, researchers can draw conclusions that are applicable to a broader range of individuals.
• Clarity : Variables help researchers to communicate their findings more clearly and effectively. By defining and categorizing variables, researchers can organize and present their findings in a way that is easily understandable to others.

Disadvantages of Variables

Here are some of the main disadvantages of using variables in research:

• Simplification : Variables may oversimplify the complexity of real-world phenomena. By breaking down a phenomenon into variables, researchers may lose important information and context, which can affect the accuracy and generalizability of their findings.
• Measurement error : Variables rely on accurate and precise measurement, and measurement error can affect the reliability and validity of research findings. The use of subjective or poorly defined variables can also introduce measurement error into the study.
• Confounding variables : Confounding variables are factors that are not measured but that affect the relationship between the variables of interest. If confounding variables are not accounted for, they can distort or obscure the relationship between the variables of interest.
• Limited scope: Variables are defined by the researcher, and the scope of the study is therefore limited by the researcher’s choice of variables. This can lead to a narrow focus that overlooks important aspects of the phenomenon being studied.
• Ethical concerns: The selection and measurement of variables may raise ethical concerns, especially in studies involving human subjects. For example, using variables that are related to sensitive topics, such as race or sexuality, may raise concerns about privacy and discrimination.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Ratio Variable – Definition, Purpose and Examples

Quantitative Variable – Definition, Types and...

Extraneous Variable – Types, Control and Examples

Control Variable – Definition, Types and Examples

Polytomous Variable – Definition, Purpose and...

Confounding Variable – Definition, Method and...

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 ...

Very informative, concise and helpful. Thank you

Helping information.Thanks

practical and well-demonstrated

Very helpful and insightful

Submit a Comment Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

• Print Friendly

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• Independent vs. Dependent Variables | Definition & Examples

Independent vs. Dependent Variables | Definition & Examples

Published on February 3, 2022 by Pritha Bhandari . Revised on June 22, 2023.

In research, variables are any characteristics that can take on different values, such as height, age, temperature, or test scores.

Researchers often manipulate or measure independent and dependent variables in studies to test cause-and-effect relationships.

• The independent variable is the cause. Its value is independent of other variables in your study.
• The dependent variable is the effect. Its value depends on changes in the independent variable.

Your independent variable is the temperature of the room. You vary the room temperature by making it cooler for half the participants, and warmer for the other half.

Table of contents

What is an independent variable, types of independent variables, what is a dependent variable, identifying independent vs. dependent variables, independent and dependent variables in research, visualizing independent and dependent variables, other interesting articles, frequently asked questions about independent and dependent variables.

An independent variable is the variable you manipulate 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).

These terms are especially used in statistics , where you estimate the extent to which an independent variable change can explain or predict changes in the dependent variable.

Prevent plagiarism. Run a free check.

There are two main types of independent variables.

• Experimental independent variables can be directly manipulated by researchers.
• Subject variables cannot be manipulated by researchers, but they can be used to group research subjects categorically.

Experimental variables

In experiments, you manipulate independent variables directly to see how they affect your dependent variable. The independent variable is usually applied at different levels to see how the outcomes differ.

You can apply just two levels in order to find out if an independent variable has an effect at all.

You can also apply multiple levels to find out how the independent variable affects the dependent variable.

You have three independent variable levels, and each group gets a different level of treatment.

You randomly assign your patients to one of the three groups:

• A low-dose experimental group
• A high-dose experimental group
• A placebo group (to research a possible placebo effect )

A true experiment requires you to randomly assign different levels of an independent variable to your participants.

Random assignment helps you control participant characteristics, so that they don’t affect your experimental results. This helps you to have confidence that your dependent variable results come solely from the independent variable manipulation.

Subject variables

Subject variables are characteristics that vary across participants, and they can’t be manipulated by researchers. For example, gender identity, ethnicity, race, income, and education are all important subject variables that social researchers treat as independent variables.

It’s not possible to randomly assign these to participants, since these are characteristics of already existing groups. Instead, you can create a research design where you compare the outcomes of groups of participants with characteristics. This is a quasi-experimental design because there’s no random assignment. Note that any research methods that use non-random assignment are at risk for research biases like selection bias and sampling bias .

Your independent variable is a subject variable, namely the gender identity of the participants. You have three groups: men, women and other.

Your dependent variable is the brain activity response to hearing infant cries. You record brain activity with fMRI scans when participants hear infant cries without their awareness.

A dependent variable is the variable that changes as a result of the independent variable manipulation. It’s the outcome 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)

The dependent variable is what you record after you’ve manipulated the independent variable. You use this measurement data to check whether and to what extent your independent variable influences the dependent variable by conducting statistical analyses.

Based on your findings, you can estimate the degree to which your independent variable variation drives changes in your dependent variable. You can also predict how much your dependent variable will change as a result of variation in the independent variable.

Distinguishing between independent and dependent variables can be tricky when designing a complex study or reading an academic research paper .

A dependent variable from one study can be the independent variable in another study, so it’s important to pay attention to research design .

Here are some tips for identifying each variable type.

Recognizing independent variables

Use this list of questions to check whether you’re dealing with an independent variable:

• Is the variable manipulated, controlled, or used as a subject grouping method by the researcher?
• Does this variable come before the other variable in time?
• Is the researcher trying to understand whether or how this variable affects another variable?

Recognizing dependent variables

Check whether you’re dealing with a dependent variable:

• Is this variable measured as an outcome of the study?
• Is this variable dependent on another variable in the study?
• Does this variable get measured only after other variables are altered?

Independent and dependent variables are generally used in experimental and quasi-experimental research.

Here are some examples of research questions and corresponding independent and dependent variables.

For experimental data, you analyze your results by generating descriptive statistics and visualizing your findings. Then, you select an appropriate statistical test to test your hypothesis .

The type of test is determined by:

• your variable types
• level of measurement
• number of independent variable levels.

You’ll often use t tests or ANOVAs to analyze your data and answer your research questions.

In quantitative research , it’s good practice to use charts or graphs to visualize the results of studies. Generally, the independent variable goes on the x -axis (horizontal) and the dependent variable on the y -axis (vertical).

The type of visualization you use depends on the variable types in your research questions:

• A bar chart is ideal when you have a categorical independent variable.
• A scatter plot or line graph is best when your independent and dependent variables are both quantitative.

To inspect your data, you place your independent variable of treatment level on the x -axis and the dependent variable of blood pressure on the y -axis.

You plot bars for each treatment group before and after the treatment to show the difference in blood pressure.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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.

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:

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.

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.

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!

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.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Bhandari, P. (2023, June 22). Independent vs. Dependent Variables | Definition & Examples. Scribbr. Retrieved September 18, 2024, from https://www.scribbr.com/methodology/independent-and-dependent-variables/

Is this article helpful?

Pritha Bhandari

Other students also liked, guide to experimental design | overview, steps, & examples, explanatory and response variables | definitions & examples, confounding variables | definition, examples & controls, "i thought ai proofreading was useless but..".

I've been using Scribbr for years now and I know it's a service that won't disappoint. It does a good job spotting mistakes”

27 Types of Variables in Research and Statistics

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

Learn about our Editorial Process

In research and statistics, a variable is a characteristic or attribute that can take on different values or categories. It represents data points or information that can be measured, observed, or manipulated within a study.

Statistical and experimental analysis aims to explore the relationships between variables. For example, researchers may hypothesize a connection between a particular variable and an outcome, like the association between physical activity levels (an independent variable) and heart health (a dependent variable).

Variables play a crucial role in data analysis . Data sets collected through research typically consist of multiple variables, and the analysis is driven by how these variables are related, how they influence each other, and what patterns emerge from these relationships.

Therefore, as a researcher, your understanding of variables and their manipulation forms the crux of your study.

To help with your understanding, I’ve presented 27 of the most common types of variables below.

Types of Variables

1. quantitative (numerical) variables.

Definition: Quantitative variables, also known as numerical variables, are quantifiable in nature and represented in numbers, allowing the data collected to be measured on a scale or range (Moodie & Johnson, 2021). These variables generally yield data that can be organized, ranked, measured, and subjected to mathematical operations.

Explanation: The values of quantitative variables can either be counted (referred to as discrete variables) or measured (continuous variables). Quantifying data in numerical form allows for a range of statistical analysis techniques to be applied, from calculating averages to finding correlations.

Quantitative Variable Example : Consider a marketing survey where you ask respondents to rate their satisfaction with your product on a scale of 1 to 10. The satisfaction score here represents a quantitative variable. The data can be quantified and used to calculate average satisfaction scores, identify the scope for product improvement, or compare satisfaction levels across different demographic groups.

2. Continuous Variables

Definition: Continuous variables are a subtype of quantitative variables that can have an infinite number of measurements within a specified range. They provide detailed insights based on precise measurements and are often representative on a continuous scale (Christmann & Badgett, 2009).

Explanation: The variable is “continuous” because there are an infinite number of possible values within the chosen range. For instance, variables like height, weight, or time are measured continuously.

Continuous Variable Example : The best real-world example of a continuous variable is time. For instance, the time it takes for a customer service representative to resolve a customer issue can range anywhere from few seconds to several hours, and can accurately be measured down to the second, providing an almost finite set of possible values.

3. Discrete Variables

Definition: Discrete variables are a form of quantitative variable that can only assume a finite number of values. They are typically count-based (Frankfort-Nachmias & Leon-Guerrero, 2006).

Explanation: Discrete variables are commonly used in situations where the “count” or “quantity” is distinctly separate. For instance, the number of children in a family is a common example – you can’t have 2.5 kids.

Discrete Variable Example : The number of times a customer contacts customer service within a month. This is a discrete variable because it can only take a whole number of values – you can’t call customer service 2.5 times.

4. Qualitative (Categorical) Variables

Definition: Qualitative, or categorical variables, are non-numerical data points that categorize or group data entities based on shared features or qualities (Moodie & Johnson, 2021).

Explanation: They are often used in research to classify particular traits, characteristics, or properties of subjects that are not easily quantifiable, such as colors, textures, tastes, or smells.

Qualitative Variable Example : Consider a survey that asks respondents to identify their favorite color from a list of choices. The color preference would be a qualitative variable as it categorizes data into different categories corresponding to different colors.

5. Nominal Variables

Definition: Nominal variables, a subtype of qualitative variables, represent categories without any inherent order or ranking (Norman & Streiner, 2008).

Explanation: Nominal variables are often used to label or categorize particular sets of items or individuals, with no intention of giving numerical value or order. For example, race, gender, or religion.

Nominal Variable Example : For instance, the type of car someone owns (sedan, SUV, truck, etc.) is a nominal variable. Each category is unique and one is not inherently higher, better, or larger than the others.

6. Ordinal Variables

Definition: Ordinal variables are a subtype of categorical (qualitative) variables with a key feature of having a clear, distinct, and meaningful order or ranking to the categories (De Vaus, 2001).

Explanation: Ordinal variables represent categories that can be logically arranged in a specific order or sequence but the difference between categories is unknown or doesn’t matter, such as satisfaction rating scale (unsatisfied, neutral, satisfied).

Ordinal Variable Example : A classic example is asking survey respondents how strongly they agree or disagree with a statement (strongly disagree, disagree, neither agree nor disagree, agree, strongly agree). The answers form an ordinal scale; they can be ranked, but the intervals between responses are not necessarily equal.

7. Dichotomous (Binary) Variables

Definition: Dichotomous or binary variables are a type of categorical variable that consist of only two opposing categories like true/false, yes/no, success/failure, and so on (Adams & McGuire, 2022).

Explanation: Dichotomous variables refer to situations where there can only be two, and just two, possible outcomes – there is no middle ground.

Dichotomous Variable Example : Whether a customer completed a transaction (Yes or No) is a binary variable. Either they completed the purchase (yes) or they did not (no).

8. Ratio Variables

Definition: Ratio variables are the highest level of quantitative variables that contain a zero point or absolute zero, which represents a complete absence of the quantity (Norman & Streiner, 2008).

Explanation: Besides being able to categorize and order units, ratio variables also allow for the relative degree of difference between them to be calculated. For example, income, height, weight, and temperature (in Kelvin) are ratio variables.

Ratio Variable Example : An individual’s annual income is a ratio variable. You can say someone earning $50,000 earns twice as much as someone making $25,000. The zero point in this case would be an income of $0, which indicates that no income is being earned.

9. Interval Variables

Definition: Interval variables are quantitative variables that have equal, predictable differences between values, but they do not have a true zero point (Norman & Streiner, 2008).

Explanation: Interval variables are similar to ratio variables; both provide a clear ordering of categories and have equal intervals between successive values. The primary difference is the absence of an absolute zero.

Interval Variable Example : The classic example of an interval variable is the temperature in Fahrenheit or Celsius. The difference between 20 degrees and 30 degrees is the same as the difference between 70 degrees and 80 degrees, but there isn’t a true zero because the scale doesn’t start from absolute nonexistence of the quantity being measured.

Related: Quantitative Reasoning Examples

10. Dependent Variables

Definition: The dependent variable is the outcome or effect that the researcher wants to study. Its value depends on or is influenced by one or more other variables known as independent variables.

Explanation: In a research study, the dependent variable is the phenomenon or behavior that may be affected by manipulations in the independent variable. It’s what you measure to see if your predictions about the effects of the independent variable are correct.

Dependent Variable Example: Suppose you want to study the impact of exercise frequency on weight loss. In this case, the dependent variable is weight loss, which changes based on how often the subject exercises (the independent variable).

11. Independent Variables

Definition: The independent variable, or the predictor variable, is what the researcher manipulates to test its effect on the dependent variable.

Explanation: The independent variable is presumed to have some effect on the dependent variable in a study. It can often be thought of as the cause in a cause-and-effect relationship.

Independent Variable Example: In a study looking at how different dosages of a medication affect the severity of symptoms, the medication dosage is an independent variable. Researchers will adjust the dosage to see what effect it has on the symptoms (the dependent variable).

See Also: Independent and Dependent Variable Examples

12. Confounding Variables

Definition: Confounding variables—also known as confounders—are variables that might distort, confuse or interfere with the relationship between an independent variable and a dependent variable, leading to a false correlation (Boniface, 2019).

Explanation: Confounders are typically related in some way to both the independent and dependent variables. Because of this, they can create or hide relationships, leading researchers to make inaccurate conclusions about causality.

Confounding Variable Example : If you’re studying the relationship between physical activity and heart health, diet could potentially act as a confounding variable. People who are physically active often also eat healthier diets, which could independently improve heart health [National Heart, Lung, and Blood Institute].

13. Control Variables

Definition: Control variables are variables in a research study that the researcher keeps constant to prevent them from interfering with the relationship between the independent and dependent variables (Sproull, 2002).

Explanation: Control variables allow researchers to isolate the effects of the independent variable on the dependent variable, ensuring that any changes observed are solely due to the manipulation of the independent variable and not an external factor.

Control Variable Example : In a study evaluating the impact of a tutoring program on student performance, some control variables could include the teacher’s experience, the type of test used to measure performance, and the student’s previous grades.

14. Latent Variables

Definition: Latent variables—also referred to as hidden or unobserved variables—are variables that are not directly observed or measured but are inferred from other variables that are observed (measured directly).

Explanation: Latent variables can represent abstract concepts like intelligence, socioeconomic status, or even happiness. They are often used in psychological and sociological research, where certain concepts can’t be measured directly.

Latent Variable Example: In a study on job satisfaction, factors like job stress, financial reward, work-life balance, or relationship with colleagues can be measured directly. However, “job satisfaction” itself is a latent variable as it is inferred from these observed variables.

15. Derived Variables

Definition: Derived variables are variables that are created or developed based on existing variables in a dataset. They involve applying certain calculations or manipulations to one or more variables to create a new one.

Explanation: Derived variables can be created by either transforming a single variable (like taking the square root) or combining multiple variables (computing the ratio of two variables).

Derived Variable Example: In a dataset containing a person’s height and weight, a derived variable could be the Body Mass Index (BMI). The BMI is calculated by dividing weight (in kilograms) by the square of height (in meters).

16. Time-series Variables

Definition: Time-series variables are a set of data points ordered or indexed in time order. They provide a sequence of data points, each associated with a specific instance in time.

Explanation: Time-series variables are often used in statistical models to study trends, analyze patterns over time, make forecasts, and understand underlying causes and characteristics of the trend.

Time-series Variable Example : The quarterly GDP (Gross Domestic Product) data over a period of several years would be an example of a time series variable. Economists use such data to examine economic trends over time.

17. Cross-sectional Variables

Definition: Cross-sectional variables are data collected from many subjects at the same point in time or without regard to differences in time.

Explanation: This type of data provides a “snapshot” of the variables at a specific time. They’re often used in research to compare different population groups at a single point in time.

Cross-sectional Variable Example: A basic example of a set of cross-sectional data could be a national survey that asks respondents about their current employment status. The data captured represents a single point in time and does not track changes in employment over time.

18. Predictor Variables

Definition: A predictor variable—also known as independent or explanatory variable—is a variable that is being manipulated in an experiment or study to see how it influences the dependent or response variable.

Explanation: In a cause-and-effect relationship, the predictor variable is the cause. Its modification allows the researcher to study its effect on the response variable.

Predictor Variable Example : In a study evaluating the impact of studying hours on exam score, the number of studying hours is a predictor variable. Researchers alter the study duration to see its impact on the exam results (response variable).

19. Response Variables

Definition: A response variable—also known as the dependent or outcome variable—is what the researcher observes for any changes in an experiment or study. Its value depends on the predictor or independent variable.

Explanation: The response variable is the “effect” in a cause-and-effect scenario. Any changes occurring to this variable due to the predictor variable are observed and recorded.

Response Variable Example: Continuing from the previous example, the exam score is the response variable. It changes based on the manipulation of the predictor variable, i.e., the number of studying hours.

20. Exogenous Variables

Definition: Exogenous variables are variables that are not affected by other variables in the system but can affect other variables within the same system.

Explanation: In a model, an exogenous variable is considered to be an input, it’s determined outside the model, and its value is simply imposed on the system.

Exogenous Variable Example: In an economic model, the government’s taxation rate may be considered an exogenous variable. The rate is set externally (not determined within the economic model) but impacts variables within the model, such as business profitability.

21. Endogenous Variables

Definition: In contrast, endogenous variables are variables whose value is determined by the functional relationships within the system in an economic or statistical model. They depend on the values of other variables in the model.

Explanation: These are the “output” variables of a system, determined through cause-and-effect relationships within the system.

Endogenous Variable Example: To continue the previous example, business profitability in an economic model may be considered an endogenous variable. It is influenced by several other variables within the model, including the exogenous taxation rate set by the government.

22. Causal Variables

Definition: Causal variables are variables which can directly cause an effect on the outcome or dependent variable. Their value or level determines the value or level of other variables.

Explanation: In a cause-and-effect relationship, a causal variable is the cause. The understanding of causal relationships is the basis of scientific enquiry, allowing researchers to manipulate variables to see the effect.

Causal Variable Example: In a study examining the effect of fertilizer on plant growth, the type or amount of fertilizer used is the causal variable. Changing its type or amount should directly affect the outcome—plant growth.

23. Moderator Variables

Definition: Moderator variables are variables that can affect the strength or direction of the association between the predictor (independent) and response (dependent) variable. They specify when or under what conditions a relationship holds.

Explanation: The role of a moderator is to illustrate “how” or “when” an independent variable’s effect on a dependent variable changes.

Moderator Variable Example: If you are studying the effect of a training program on job performance, a potential moderator variable could be the employee’s education level. The influence of the training program on job performance could depend on the employee’s initial level of education.

24. Mediator Variables

Definition: Mediator variables are variables that account for, or explain, the relationship between an independent variable and a dependent variable, providing an understanding of “why” or “how” an effect occurs.

Explanation: Often, the relationship between an independent and a dependent variable isn’t direct—it’s through a third, intervening, variable known as a mediator variable.

Mediator Variable Example: In a study looking at the relationship between socioeconomic status and academic performance, a mediator variable might be the access to educational resources. Socioeconomic status may influence access to educational resources, which in turn affects academic performance. The relationship between socioeconomic status and academic performance isn’t direct but through access to resources.

25. Extraneous Variables

Definition: Extraneous variables are variables that are not of primary interest to a researcher but might influence the outcome of a study. They can add “noise” to the research data if not controlled.

Explanation: An extraneous variable is anything else that has the potential to influence our dependent variable or confound our results if not kept in check, other than our independent variable.

Extraneous Variable Example : Consider an experiment to test whether temperature influences the rate of a chemical reaction. Potential extraneous variables could include the light level, humidity, or impurities in the chemicals used—each could affect the reaction rate and, thus, should be controlled to ensure valid results.

26. Dummy Variables

Definition: Dummy variables, often used in regression analysis, are artificial variables created to represent an attribute with two or more distinct categories or levels.

Explanation: They are used to turn a qualitative variable into a quantitative one to facilitate mathematical processing. Typically, dummy variables are binary – taking a value of either 0 or 1.

Dummy Variable Example: Consider a dataset that includes a variable “Gender” with categories “male” and “female”. A corresponding dummy variable “IsMale” could be introduced, where males get classified as 1 and females as 0.

27. Composite Variables

Definition: Composite variables are new variables created by combining or grouping two or more variables.

Explanation: Depending upon their complexity, composite variables can help assess concepts that are explicit (e.g., “total score”) or relatively abstract (e.g., “life quality index”).

Composite Variable Example: A “Healthy Living Index” might be created as a composite of multiple variables such as eating habits, physical activity level, sleep quality, and stress level. Each of these variables contributes to the overall “Healthy Living Index”.

Knowing your variables will make you a better researcher. Some you need to keep an eye out for: confounding variables , for instance, always need to be in the backs of our minds. Others you need to think about during study design, matching the research design to the research objectives.

Adams, K. A., & McGuire, E. K. (2022). Research Methods, Statistics, and Applications . SAGE Publications.

Allen, M. (2017). The SAGE Encyclopedia of Communication Research Methods (Vol. 1). New York: SAGE Publications.

Babbie, E., Halley, F., & Zaino, J. (2007).  Adventures in Social Research: Data Analysis Using SPSS 14.0 and 15.0 for Windows  (6th ed.). New York: SAGE Publications.

Boniface, D. R. (2019). Experiment Design and Statistical Methods For Behavioural and Social Research . CRC Press. ISBN: 9781351449298.

Christmann, E. P., & Badgett, J. L. (2009). Interpreting Assessment Data: Statistical Techniques You Can Use. New York: NSTA Press.

Coolidge, F. L. (2012). Statistics: A Gentle Introduction (3rd ed.). SAGE Publications.

Creswell, J. W., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . New York: SAGE Publications.

De Vaus, D. A. (2001). Research Design in Social Research . New York: SAGE Publications.

Katz, M. (2006) . Study Design and Statistical Analysis: A Practical Guide for Clinicians . Cambridge: Cambridge University Press.

Knapp, H. (2017). Intermediate Statistics Using SPSS. SAGE Publications.

Moodie, P. F., & Johnson, D. E. (2021). Applied Regression and ANOVA Using SAS. CRC Press.

Norman, G. R., & Streiner, D. L. (2008). Biostatistics: The Bare Essentials . New York: B.C. Decker.

Privitera, G. J. (2022). Research Methods for the Behavioral Sciences . New Jersey: SAGE Publications.

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 10 Reasons you’re Perpetually Single
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 20 Montessori Toddler Bedrooms (Design Inspiration)
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 21 Montessori Homeschool Setups
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 101 Hidden Talents Examples

Leave a Comment Cancel Reply

Your email address will not be published. Required fields are marked *

Variables in Research | Types, Definiton & Examples

Introduction

What is a variable, what are the 5 types of variables in research, other variables in research.

Variables are fundamental components of research that allow for the measurement and analysis of data. They can be defined as characteristics or properties that can take on different values. In research design , understanding the types of variables and their roles is crucial for developing hypotheses , designing methods , and interpreting results .

This article outlines the the types of variables in research, including their definitions and examples, to provide a clear understanding of their use and significance in research studies. By categorizing variables into distinct groups based on their roles in research, their types of data, and their relationships with other variables, researchers can more effectively structure their studies and achieve more accurate conclusions.

A variable represents any characteristic, number, or quantity that can be measured or quantified. The term encompasses anything that can vary or change, ranging from simple concepts like age and height to more complex ones like satisfaction levels or economic status. Variables are essential in research as they are the foundational elements that researchers manipulate, measure, or control to gain insights into relationships, causes, and effects within their studies. They enable the framing of research questions, the formulation of hypotheses, and the interpretation of results.

Variables can be categorized based on their role in the study (such as independent and dependent variables ), the type of data they represent (quantitative or categorical), and their relationship to other variables (like confounding or control variables). Understanding what constitutes a variable and the various variable types available is a critical step in designing robust and meaningful research.

ATLAS.ti makes complex data easy to understand

Turn to our powerful data analysis tools to make the most of your research. Get started with a free trial.

Variables are crucial components in research, serving as the foundation for data collection , analysis , and interpretation . They are attributes or characteristics that can vary among subjects or over time, and understanding their types is essential for any study. Variables can be broadly classified into five main types, each with its distinct characteristics and roles within research.

This classification helps researchers in designing their studies, choosing appropriate measurement techniques, and analyzing their results accurately. The five types of variables include independent variables, dependent variables, categorical variables, continuous variables, and confounding variables. These categories not only facilitate a clearer understanding of the data but also guide the formulation of hypotheses and research methodologies.

Independent variables

Independent variables are foundational to the structure of research, serving as the factors or conditions that researchers manipulate or vary to observe their effects on dependent variables. These variables are considered "independent" because their variation does not depend on other variables within the study. Instead, they are the cause or stimulus that directly influences the outcomes being measured. For example, in an experiment to assess the effectiveness of a new teaching method on student performance, the teaching method applied (traditional vs. innovative) would be the independent variable.

The selection of an independent variable is a critical step in research design, as it directly correlates with the study's objective to determine causality or association. Researchers must clearly define and control these variables to ensure that observed changes in the dependent variable can be attributed to variations in the independent variable, thereby affirming the reliability of the results. In experimental research, the independent variable is what differentiates the control group from the experimental group, thereby setting the stage for meaningful comparison and analysis.

Dependent variables

Dependent variables are the outcomes or effects that researchers aim to explore and understand in their studies. These variables are called "dependent" because their values depend on the changes or variations of the independent variables.

Essentially, they are the responses or results that are measured to assess the impact of the independent variable's manipulation. For instance, in a study investigating the effect of exercise on weight loss, the amount of weight lost would be considered the dependent variable, as it depends on the exercise regimen (the independent variable).

The identification and measurement of the dependent variable are crucial for testing the hypothesis and drawing conclusions from the research. It allows researchers to quantify the effect of the independent variable , providing evidence for causal relationships or associations. In experimental settings, the dependent variable is what is being tested and measured across different groups or conditions, enabling researchers to assess the efficacy or impact of the independent variable's variation.

To ensure accuracy and reliability, the dependent variable must be defined clearly and measured consistently across all participants or observations. This consistency helps in reducing measurement errors and increases the validity of the research findings. By carefully analyzing the dependent variables, researchers can derive meaningful insights from their studies, contributing to the broader knowledge in their field.

Categorical variables

Categorical variables, also known as qualitative variables, represent types or categories that are used to group observations. These variables divide data into distinct groups or categories that lack a numerical value but hold significant meaning in research. Examples of categorical variables include gender (male, female, other), type of vehicle (car, truck, motorcycle), or marital status (single, married, divorced). These categories help researchers organize data into groups for comparison and analysis.

Categorical variables can be further classified into two subtypes: nominal and ordinal. Nominal variables are categories without any inherent order or ranking among them, such as blood type or ethnicity. Ordinal variables, on the other hand, imply a sort of ranking or order among the categories, like levels of satisfaction (high, medium, low) or education level (high school, bachelor's, master's, doctorate).

Understanding and identifying categorical variables is crucial in research as it influences the choice of statistical analysis methods. Since these variables represent categories without numerical significance, researchers employ specific statistical tests designed for a nominal or ordinal variable to draw meaningful conclusions. Properly classifying and analyzing categorical variables allow for the exploration of relationships between different groups within the study, shedding light on patterns and trends that might not be evident with numerical data alone.

Continuous variables

Continuous variables are quantitative variables that can take an infinite number of values within a given range. These variables are measured along a continuum and can represent very precise measurements. Examples of continuous variables include height, weight, temperature, and time. Because they can assume any value within a range, continuous variables allow for detailed analysis and a high degree of accuracy in research findings.

The ability to measure continuous variables at very fine scales makes them invaluable for many types of research, particularly in the natural and social sciences. For instance, in a study examining the effect of temperature on plant growth, temperature would be considered a continuous variable since it can vary across a wide spectrum and be measured to several decimal places.

When dealing with continuous variables, researchers often use methods incorporating a particular statistical test to accommodate a wide range of data points and the potential for infinite divisibility. This includes various forms of regression analysis, correlation, and other techniques suited for modeling and analyzing nuanced relationships between variables. The precision of continuous variables enhances the researcher's ability to detect patterns, trends, and causal relationships within the data, contributing to more robust and detailed conclusions.

Confounding variables

Confounding variables are those that can cause a false association between the independent and dependent variables, potentially leading to incorrect conclusions about the relationship being studied. These are extraneous variables that were not considered in the study design but can influence both the supposed cause and effect, creating a misleading correlation.

Identifying and controlling for a confounding variable is crucial in research to ensure the validity of the findings. This can be achieved through various methods, including randomization, stratification, and statistical control. Randomization helps to evenly distribute confounding variables across study groups, reducing their potential impact. Stratification involves analyzing the data within strata or layers that share common characteristics of the confounder. Statistical control allows researchers to adjust for the effects of confounders in the analysis phase.

Properly addressing confounding variables strengthens the credibility of research outcomes by clarifying the direct relationship between the dependent and independent variables, thus providing more accurate and reliable results.

Beyond the primary categories of variables commonly discussed in research methodology , there exists a diverse range of other variables that play significant roles in the design and analysis of studies. Below is an overview of some of these variables, highlighting their definitions and roles within research studies:

• Discrete variables : A discrete variable is a quantitative variable that represents quantitative data , such as the number of children in a family or the number of cars in a parking lot. Discrete variables can only take on specific values.
• Categorical variables : A categorical variable categorizes subjects or items into groups that do not have a natural numerical order. Categorical data includes nominal variables, like country of origin, and ordinal variables, such as education level.
• Predictor variables : Often used in statistical models, a predictor variable is used to forecast or predict the outcomes of other variables, not necessarily with a causal implication.
• Outcome variables : These variables represent the results or outcomes that researchers aim to explain or predict through their studies. An outcome variable is central to understanding the effects of predictor variables.
• Latent variables : Not directly observable, latent variables are inferred from other, directly measured variables. Examples include psychological constructs like intelligence or socioeconomic status.
• Composite variables : Created by combining multiple variables, composite variables can measure a concept more reliably or simplify the analysis. An example would be a composite happiness index derived from several survey questions .
• Preceding variables : These variables come before other variables in time or sequence, potentially influencing subsequent outcomes. A preceding variable is crucial in longitudinal studies to determine causality or sequences of events.

Master qualitative research with ATLAS.ti

Turn data into critical insights with our data analysis platform. Try out a free trial today.

Independent and Dependent Variables

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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.

• Science Notes Posts
• Contact Science Notes
• Todd Helmenstine Biography
• Anne Helmenstine Biography
• Free Printable Periodic Tables (PDF and PNG)
• Periodic Table Wallpapers
• Interactive Periodic Table
• Periodic Table Posters
• Science Experiments for Kids
• How to Grow Crystals
• Chemistry Projects
• Fire and Flames Projects
• Holiday Science
• Chemistry Problems With Answers
• Physics Problems
• Unit Conversion Example Problems
• Chemistry Worksheets
• Biology Worksheets
• Periodic Table Worksheets
• Physical Science Worksheets
• Science Lab Worksheets
• My Amazon Books

Independent and Dependent Variables Examples

The independent and dependent variables are key to any scientific experiment, but how do you tell them apart? Here are the definitions of independent and dependent variables, examples of each type, and tips for telling them apart and graphing them.

Independent Variable

The independent variable is the factor the researcher changes or controls in an experiment. It is called independent because it does not depend on any other variable. The independent variable may be called the “controlled variable” because it is the one that is changed or controlled. This is different from the “ control variable ,” which is variable that is held constant so it won’t influence the outcome of the experiment.

Dependent Variable

The dependent variable is the factor that changes in response to the independent variable. It is the variable that you measure in an experiment. The dependent variable may be called the “responding variable.”

Examples of Independent and Dependent Variables

Here are several examples of independent and dependent variables in experiments:

• In a study to determine whether how long a student sleeps affects test scores, the independent variable is the length of time spent sleeping while the dependent variable is the test score.
• You want to know which brand of fertilizer is best for your plants. The brand of fertilizer is the independent variable. The health of the plants (height, amount and size of flowers and fruit, color) is the dependent variable.
• You want to compare brands of paper towels, to see which holds the most liquid. The independent variable is the brand of paper towel. The dependent variable is the volume of liquid absorbed by the paper towel.
• You suspect the amount of television a person watches is related to their age. Age is the independent variable. How many minutes or hours of television a person watches is the dependent variable.
• You think rising sea temperatures might affect the amount of algae in the water. The water temperature is the independent variable. The mass of algae is the dependent variable.
• In an experiment to determine how far people can see into the infrared part of the spectrum, the wavelength of light is the independent variable and whether the light is observed is the dependent variable.
• If you want to know whether caffeine affects your appetite, the presence/absence or amount of caffeine is the independent variable. Appetite is the dependent variable.
• You want to know which brand of microwave popcorn pops the best. The brand of popcorn is the independent variable. The number of popped kernels is the dependent variable. Of course, you could also measure the number of unpopped kernels instead.
• You want to determine whether a chemical is essential for rat nutrition, so you design an experiment. The presence/absence of the chemical is the independent variable. The health of the rat (whether it lives and reproduces) is the dependent variable. A follow-up experiment might determine how much of the chemical is needed. Here, the amount of chemical is the independent variable and the rat health is the dependent variable.

How to Tell the Independent and Dependent Variable Apart

If you’re having trouble identifying the independent and dependent variable, here are a few ways to tell them apart. First, remember the dependent variable depends on the independent variable. It helps to write out the variables as an if-then or cause-and-effect sentence that shows the independent variable causes an effect on the dependent variable. If you mix up the variables, the sentence won’t make sense. Example : The amount of eat (independent variable) affects how much you weigh (dependent variable).

This makes sense, but if you write the sentence the other way, you can tell it’s incorrect: Example : How much you weigh affects how much you eat. (Well, it could make sense, but you can see it’s an entirely different experiment.) If-then statements also work: Example : If you change the color of light (independent variable), then it affects plant growth (dependent variable). Switching the variables makes no sense: Example : If plant growth rate changes, then it affects the color of light. Sometimes you don’t control either variable, like when you gather data to see if there is a relationship between two factors. This can make identifying the variables a bit trickier, but establishing a logical cause and effect relationship helps: Example : If you increase age (independent variable), then average salary increases (dependent variable). If you switch them, the statement doesn’t make sense: Example : If you increase salary, then age increases.

How to Graph Independent and Dependent Variables

Plot or graph independent and dependent variables using the standard method. The independent variable is the x-axis, while the dependent variable is the y-axis. Remember the acronym DRY MIX to keep the variables straight: D = Dependent variable R = Responding variable/ Y = Graph on the y-axis or vertical axis M = Manipulated variable I = Independent variable X = Graph on the x-axis or horizontal axis

• Babbie, Earl R. (2009). The Practice of Social Research (12th ed.) Wadsworth Publishing. ISBN 0-495-59841-0.
• di Francia, G. Toraldo (1981). The Investigation of the Physical World . Cambridge University Press. ISBN 978-0-521-29925-1.
• Gauch, Hugh G. Jr. (2003). Scientific Method in Practice . Cambridge University Press. ISBN 978-0-521-01708-4.
• Popper, Karl R. (2003). Conjectures and Refutations: The Growth of Scientific Knowledge . Routledge. ISBN 0-415-28594-1.

Related Posts

What are Examples of Variables in Research?

Table of contents, introduction.

In writing your thesis, one of the first terms that you encounter is the word variable. Failure to understand the meaning and the usefulness of variables in your study will prevent you from doing excellent research. What are variables, and how do you use variables in your research?

You may find it challenging to understand just what variables are in research, especially those that deal with quantitative data analysis. This initial difficulty about variables becomes much more confusing when you encounter the phrases “dependent variable” and “independent variable” as you go deeper in studying this vital concept of research, as well as statistics.

Therefore, it is a must that you should be able to grasp thoroughly the meaning of variables and ways on how to measure them. Yes, the variables should be measurable so that you will use your data for statistical analysis.

I will strengthen your understanding by providing examples of phenomena and their corresponding variables below.

Definition of Variable

Variables are those simplified portions of the complex phenomena that you intend to study. The word variable is derived from the root word “vary,” meaning, changing in amount, volume, number, form, nature, or type. These variables should be measurable, i.e., they can be counted or subjected to a scale.

Examples of Variables in Research: 6 Phenomena

The following are examples of phenomena from a global to a local perspective. The corresponding list of variables is given to illustrate how complex phenomena can be broken down into manageable pieces for better understanding and to subject the phenomena to research.

Phenomenon 1: Climate change

Examples of variables related to climate change :

Phenomenon 2: Crime and violence in the streets

Phenomenon 3: poor performance of students in college entrance exams.

Examples of variables related to poor academic performance :

Phenomenon 4: Fish kill

Phenomenon 5: poor crop growth.

Examples of variables related to poor crop growth :

Phenomenon 6:  How Content Goes Viral

Notice in the above variable examples that all the factors listed under the phenomena can be counted or measured using an ordinal, ratio, or interval scale, except for the last one. The factors that influence how content goes viral are essentially subjective.

Thus, the variables in the last phenomenon represent the  nominal scale of measuring variables .

The expected values derived from these variables will be in terms of numbers, amount, category, or type. Quantified variables allow statistical analysis . Variable descriptions, correlations, or differences are then determined.

Difference Between Independent and Dependent Variables

Independent variables.

For example, in the second phenomenon, i.e., crime and violence in the streets, the independent variables are the number of law enforcers. If there are more law enforcers, it is expected that it will reduce the following:

The five variables listed under crime and violence in the streets as the theme of a study are all dependent variables.

Dependent Variables

For example, in the first phenomenon on climate change, temperature as the independent variable influences sea level rise, the dependent variable. Increased temperature will cause the expansion of water in the sea. Thus, sea-level rise on a global scale will occur.

I will leave the classification of the other variables to you. Find out whether those are independent or dependent variables. Note, however, that some variables can be both independent or dependent variables, as the context of the study dictates.

Finding the relationship between variables

How will you know that one variable may cause the other to behave in a certain way?

Finding the relationship between variables requires a thorough  review of the literature . Through a review of the relevant and reliable literature, you will find out which variables influence the other variable. You do not just guess relationships between variables. The entire process is the essence of research.

At this point, I believe that the concept of the variable is now clear to you. Share this information with your peers, who may have difficulty in understanding what the variables are in research.

Related Posts

Writing the research objectives: 5 straightforward examples, using a matrix to write your research proposal, running at an even pace works better than negative or positive splits, about the author, patrick regoniel, 128 comments.

Your question is unclear to me Biyaminu. What do you mean? If you want to cite this, see the citation box after the article.

I salute your work, before I was have no enough knowledge about variable I think I was claimed from my lecturers, but the real meaning I was in the mid night. thanks

thanks for the explanation a bout variables. keep on posting information a bout reseach on my email.

You can see in the last part of the above article an explanation about dependent and independent variables.

I am requested to write 50 variables in my research as per my topic which is about street vending. I am really clueless.

Dear Alhaji, just be clear about what you want to do. Your research question must be clearly stated before you build your conceptual framework.

Can you please give me what are the possible variables in terms of installation of street lights along barangay roads of calauan, laguna: an assessment?

SimplyEducate.Me Privacy Policy

• How it works

Types of Variables – A Comprehensive Guide

Published by Carmen Troy at August 14th, 2021 , Revised On October 26, 2023

A variable is any qualitative or quantitative characteristic that can change and have more than one value, such as age, height, weight, gender, etc.

Before conducting research, it’s essential to know what needs to be measured or analysed and choose a suitable statistical test to present your study’s findings. 

In most cases, you can do it by identifying the key issues/variables related to your research’s main topic.

Example:  If you want to test whether the hybridisation of plants harms the health of people. You can use the key variables like agricultural techniques, type of soil, environmental factors, types of pesticides used, the process of hybridisation, type of yield obtained after hybridisation, type of yield without hybridisation, etc.

Variables are broadly categorised into:

• Independent variables
• Dependent variable
• Control variable

Independent Vs. Dependent Vs. Control Variable

Type of variable	Definition	Example
Independent Variable (Stimulus)	It is the variable that influences other variables.
Dependent variable (Response)	The dependent variable is the outcome of the influence of the independent variable.	You want to identify “How refined carbohydrates affect the health of human beings?” : refined carbohydrates : the health of human beings You can manipulate the consumption of refined carbs in your human participants and measure how those levels of consuming processed carbohydrates influence human health.
Control Variables	Control variables are variables that are not changed and kept constant throughout the experiment. The research includes finding ways: To change the independent variables. To prevent the controlled variables from changing. To measure the dependent variables. Note:  The term dependent and independent is not applicable in  correlational research  as this is not a  controlled experiment.  A researcher doesn’t have control over the variables. The association and between two or more variables are measured. If one variable affects another one, then it’s called the predictor variable and outcome variable. Example:  Correlation between investment (predictor variable) and profit (outcome variable) What data collection best suits your research? Find out by hiring an expert from ResearchProspect today! Despite how challenging the subject may be, we are here to help you. Types of Variables Based on the Types of Data A data is referred to as the information and statistics gathered for analysis of a research topic. Data is broadly divided into two categories, such as: Quantitative/Numerical data  is associated with the aspects of measurement, quantity, and extent.  Categorial data  is associated with groupings. A qualitative variable consists of qualitative data, and a quantitative variable consists of a quantitative variable. Quantitative Variable The quantitative variable is associated with measurement, quantity, and extent, like how many . It follows the statistical, mathematical, and computational techniques in numerical data such as percentages and statistics. The research is conducted on a large group of population. Example:  Find out the weight of students of the fifth standard studying in government schools. The quantitative variable can be further categorised into continuous and discrete. Type of variable Definition Example Continuous Variable A continuous variable is a quantitative variable that can take a value between two specific values. Discrete Variable A discrete variable is a quantitative variable whose attributes are separated from each other.  Literacy rate, gender, and nationality. Scale: Nominal and ordinal. Categorial Variable The categorical variable includes measurements that vary in categories such as names but not in terms of rank or degree. It means one level of a categorical variable cannot be considered better or greater than another level.  Example: Gender, brands, colors, zip codes The categorical variable is further categorised into three types: Type of variable Definition Example Dichotomous (Binary) Variable This is the categorical variable with two possible results (Yes/No) Alcoholic (Yes/No) Nominal Variable Nominal Variable can take the value that is not organised in terms of groups, degree, or rank. Ordinal Variable Ordinal Variable can take the value that can be logically ordered or ranked. Note:  Sometimes, an ordinal variable also acts as a quantitative variable. Ordinal data has an order, but the intervals between scale points may be uneven. Example: Numbers on a rating scale represent the reviews’ rank or range from below average to above average. However, it also represents a quantitative variable showing how many stars and how much rating is given. Not sure which statistical tests to use for your data? Let the experts at researchprospect do the daunting work for you.. Using our approach, we illustrate how to collect data, sample sizes, validity, reliability, credibility, and ethics, so you won’t have to do it all by yourself! Other Types of Variables It’s important to understand the difference between dependent and independent variables and know whether they are quantitative or categorical to choose the appropriate statistical test. There are many other types of variables to help you differentiate and understand them. Also, read a comprehensive guide written about inductive and deductive reasoning . Type of variable Definition Example Confounding variables The confounding variable is a hidden variable that produces an association between two unrelated variables because the hidden variable affects both of them. There is an association between water consumption and cold drink sales. The confounding variable could be the   and compels people to drink a lot of water and a cold drink to reduce heat and thirst caused due to the heat. Latent Variable These are the variables that cannot be observed or measured directly. Self-confidence and motivation cannot be measured directly. Still, they can be interpreted through other variables such as habits, achievements, perception, and lifestyle. Composite variables A composite variable is a combination of multiple variables. It is used to measure multidimensional aspects that are difficult to observe. Entertainment Online education Database management, storage, and retrieval Frequently Asked Questions What are the 10 types of variables in research. The 10 types of variables in research are: Independent Confounding Categorical Extraneous. What is an independent variable? An independent variable, often termed the predictor or explanatory variable, is the variable manipulated or categorized in an experiment to observe its effect on another variable, called the dependent variable. It’s the presumed cause in a cause-and-effect relationship, determining if changes in it produce changes in the observed outcome. What is a variable? In research, a variable is any attribute, quantity, or characteristic that can be measured or counted. It can take on various values, making it “variable.” Variables can be classified as independent (manipulated), dependent (observed outcome), or control (kept constant). They form the foundation for hypotheses, observations, and data analysis in studies. What is a dependent variable? A dependent variable is the outcome or response being studied in an experiment or investigation. It’s what researchers measure to determine the effect of changes in the independent variable. In a cause-and-effect relationship, the dependent variable is presumed to be influenced or caused by the independent variable. What is a variable in programming? In programming, a variable is a symbolic name for a storage location that holds data or values. It allows data storage and retrieval for computational operations. Variables have types, like integer or string, determining the nature of data they can hold. They’re fundamental in manipulating and processing information in software. What is a control variable? A control variable in research is a factor that’s kept constant to ensure that it doesn’t influence the outcome. By controlling these variables, researchers can isolate the effects of the independent variable on the dependent variable, ensuring that other factors don’t skew the results or introduce bias into the experiment. What is a controlled variable in science? In science, a controlled variable is a factor that remains constant throughout an experiment. It ensures that any observed changes in the dependent variable are solely due to the independent variable, not other factors. By keeping controlled variables consistent, researchers can maintain experiment validity and accurately assess cause-and-effect relationships. How many independent variables should an investigation have? Ideally, an investigation should have one independent variable to clearly establish cause-and-effect relationships. Manipulating multiple independent variables simultaneously can complicate data interpretation. However, in advanced research, experiments with multiple independent variables (factorial designs) are used, but they require careful planning to understand interactions between variables. You May Also Like Action research for my dissertation?, A brief overview of action research as a responsive, action-oriented, participative and reflective research technique. Struggling to figure out “whether I should choose primary research or secondary research in my dissertation?” Here are some tips to help you decide. A hypothesis is a research question that has to be proved correct or incorrect through hypothesis testing – a scientific approach to test a hypothesis. USEFUL LINKS LEARNING RESOURCES COMPANY DETAILS How It Works Explore Jobs Jobs Near Me Remote Jobs Full Time Jobs Part Time Jobs Entry Level Jobs Work From Home Jobs Find Specific Jobs $15 Per Hour Jobs $20 Per Hour Jobs Hiring Immediately Jobs High School Jobs H1b Visa Jobs Explore Careers Business And Financial Architecture And Engineering Computer And Mathematical Explore Professions What They Do Certifications Demographics Best Companies Health Care Fortune 500 Explore Companies CEO And Executies Resume Builder Career Advice Explore Majors Questions And Answers Interview Questions The Different Types Of Variables Used In Research And Statistics APR Formula Total Variable Cost Formula How to Calculate Probability How To Find A Percentile How To Calculate Weighted Average What Is The Sample Mean? Hot To Calculate Growth Rate Hot To Calculate Inflation Rate How To Calculate Marginal Utility How To Average Percentages Calculate Debt To Asset Ratio How To Calculate Percent Yield Fixed Cost Formula How To Calculate Interest How To Calculate Earnings Per Share How To Calculate Retained Earnings How To Calculate Adjusted Gross Income How To Calculate Consumer Price Index How To Calculate Cost Of Goods Sold How To Calculate Correlation How To Calculate Confidence Interval How To Calculate Consumer Surplus How To Calculate Debt To Income Ratio How To Calculate Depreciation How To Calculate Elasticity Of Demand How To Calculate Equity How To Calculate Full Time Equivalent How To Calculate Gross Profit Percentage How To Calculate Margin Of Error How To Calculate Opportunity Cost How To Calculate Operating Cash Flow How To Calculate Operating Income How To Calculate Odds How To Calculate Percent Change How To Calculate Z Score Cost Of Capital Formula How To Calculate Time And A Half Types Of Variables Find a Job You Really Want In Scientists and statisticians conduct experiments on a regular basis. Scientists use these experiments to identify cause and effect, while Statisticians use variables to represent the unknown or varied data in their experiments. Determining which variables to use is vital to the experiment. Also, choosing the right variables will lead to clearer analyses and more accurate results. What Is a Variable? A variable is something you can control, manipulate, or measure when conducting research or experiments. They are characteristics, numbers, or quantities and can represent specific items, people, places, or an idea. The variables may be referred to as data items. The variables in an experiment will vary depending on the desired outcome. All scientific experiments and statistical studies will analyze a variable. They are referred to as variables because the values can vary. A variable’s value can change within a single experiment. Whether there is a change between the groups being studied or the value changes over time, it may not necessarily be a constant. Designing Experiments As noted, all scientific experiments and statistical studies will control, manipulate, or measure a variable. In fact, the experiments are usually designed to determine the effect one variable has on another variable, cause, and effect. When designing experiments, it is extremely important to choose the right variables. Choosing incorrectly can skew the results and derail the experiment or study completely. Choosing right can help an experiment or study run much more smoothly and produce more accurate results. It is not just the specific variable within the experiment that needs to be determined, but the variable type as well. Knowing the variable type will allow you to interpret the results of the experiment or study. It should be noted, though, that categorizing variables is a little subjective. Scientists and statisticians have some wiggle room when they categorize their experiment variables. Generally, you will need to know what data the variable represents and what part of the experiment the variable represents in order to determine the variable type. Independent, Dependent, and Control Variables Typically, there will be an independent variable, dependent variable, and control variable in every experiment or study conducted. Independent variables. Independent variables are the variables in your experiment that are being manipulated. They are referred to as independent variables due to the fact that their value is independent of other variables, which means that the other variables cannot change the independent variable. Dependent variables. Dependent variables are the variables in your experiment that rely on other variables and can be changed or manipulated by the other variables being measured. Control variables. Control variables are the variables in your experiment that are constant. They do not change over the course of the experiment or study and will have no direct effect on the other variables being measured. Qualitative Versus Quantitative Variables Every single variable you include in your experiment will need to be categorized as either a qualitative variable or a quantitative variable. Qualitative variables. Also referred to as categorical variables, qualitative variables are any variables that hold no numerical value. They are nominal labels. For example, eye color would be a qualitative variable. The data being recorded is not a number but a color. These variables don’t necessarily measure, but they describe a characteristic of the data set. They can be broken down further as either ordinal variables or nominal variables (see below for definitions). Quantitative variables. Quantitative variables, or numeric variables, are the variables in your experiment that hold a numerical value. Unsurprisingly, they will represent a measurable quantity and will be recorded as a number. These variables will measure “how many” or “how much” of the data being collected. These can be broken down further as either continuous variables or discrete variables (see below for definitions). 30 Other Variable Types Used in Experiments This is by no means a comprehensive list, as the list of all variable types would be difficult to document in one place. Below are many of the common and some less common variable types used in scientific experiments and statistical studies. Included is a brief overview of what that variable type measures. Active variable. An active variable is a variable that can be manipulated by those running the experiment. Antecedent variable. Antecedent variables come before the independent and dependent variables. With “antecedent” meaning “preceding in time or order,” this is not surprising. Attribute variable. An attribute variable also called a passive variable, is not manipulated during the experiment. It may be a fixed variable or simply a variable that is not manipulated for one experiment but could be for another. Binary variable. Binary variables only have two values. Typically, this will be represented as a zero or one but can be yes/no or another two-value combination. Categorical variable. Categorical variables are variables that can be divided into larger buckets or categories. Shoe brands, for instance, could include Nike, Reebok, or Adidas. Composite variable. This variable type is a bit different from others. A composite variable is made up of two or more other variables. The individual variables that make up the composite variable will be closely related either conceptually or statistically. Confounding variable. Confounding variables are not good. They can affect both independent and dependent variables and invalidate results. Sometimes referred to as a lurking variable, these variables are considered “extra” and were not accounted for during the designing phase. Continuous variable. Continuous variables have an infinite number of values between the highest point and lowest point. Distance is a continuous variable. Covariate variable. A covariate variable can affect the dependent variable in addition to the independent variable. It will not be of interest in the results of the experiment, though. Criterion variable. This is a statistical variable only. It is another name for the dependent variable. Dichotomous variable. This is another name for a binary variable. Dichotomous variables will have two values only. Discrete variable. Discrete variables are the opposite of continuous variables. Where continuous variables have an infinite number of possible values, discrete variables have a finite number. Endogenous variable. Endogenous variables are dependent on other variables and are used only in statistical studies, in econometrics specifically. The value of these variables is determined by the model. Exogenous variable. An exogenous variable is the opposite of an endogenous variable. The value of this type of variable is determined outside of the model and will have an impact on other variables within the model. Explanatory variable. This is a commonly used name for the independent variable or the variable that is being manipulated by those running the experiment. Grouping variable. A grouping variable is used to sort, or split up, the data set into groups or categories. Interval variable. Interval variables show the meaningful difference between the two values. Intervening variable. Intervening variables, or mediator variables, explains the cause, connection, or relationship between two other variables being measured. Manifest variable. A manifest variable is a variable that can be directly observed or measured within the experiment. Moderating variable. A moderating variable can affect the relationship between the independent variable and dependent variable. It can either strengthen, diminish, or negate the relationship. Nominal variable. This is another way of saying categorical value. Nominal values will have two or more categories. Observed variable. Observed variables are variables that are being measured during the experiment. Ordinal variable. Ordinal variables are similar to categorical or nominal variables but have a clear ordering of categories. Examples such as High to low and like to dislike would both be ordinal variables. Polychotomous variable. Polychotomous variables have more than two possible categories or values. These can be either nominal or ordinal. Ranked variable. Ranked variables are ordinal variables. The researcher may not know the exact value, but they will know the order in which the data points should fall. Ratio variable. Ratio variables are similar to interval variables but have a clear definition of zero. Responding variable. Responding variables are the effect or outcome of the experiment. Similar to dependent variables, responding variables will “respond” to changes being made in the experiment. Scale variable. A scale variable is a variable that has a numeric value that can be ordered with a meaningful metric. It will be the amount or number of something. Study variable. Often referred to as a research variable, a study variable is any variable used that has some kind of cause and effect relationship. Test variable. A test variable also referred to as the dependent variable, is a variable that represents the outcome of the experiment. How useful was this post? Click on a star to rate it! Average rating / 5. Vote count: No votes so far! Be the first to rate this post. Chris Kolmar is a co-founder of Zippia and the editor-in-chief of the Zippia career advice blog. He has hired over 50 people in his career, been hired five times, and wants to help you land your next job. His research has been featured on the New York Times, Thrillist, VOX, The Atlantic, and a host of local news. More recently, he's been quoted on USA Today, BusinessInsider, and CNBC. Related posts Workplace Nepotism: What To Look For And How To Handle The Situation Professionally Nurse Vs. Nurse Practitioner Understanding Cost Of Capital (With Examples) How To Calculate Operating Cash Flow (OCF) Career Advice > An official website of the United States government The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site. The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely. Publications Account settings The PMC website is updating on October 15, 2024. Learn More or Try it out now . Advanced Search Journal List Indian Dermatol Online J v.10(1); Jan-Feb 2019 Types of Variables, Descriptive Statistics, and Sample Size Feroze kaliyadan. Department of Dermatology, King Faisal University, Al Hofuf, Saudi Arabia Vinay Kulkarni 1 Department of Dermatology, Prayas Amrita Clinic, Pune, Maharashtra, India This short “snippet” covers three important aspects related to statistics – the concept of variables , the importance, and practical aspects related to descriptive statistics and issues related to sampling – types of sampling and sample size estimation. What is a variable?[ 1 , 2 ] To put it in very simple terms, a variable is an entity whose value varies. A variable is an essential component of any statistical data. It is a feature of a member of a given sample or population, which is unique, and can differ in quantity or quantity from another member of the same sample or population. Variables either are the primary quantities of interest or act as practical substitutes for the same. The importance of variables is that they help in operationalization of concepts for data collection. For example, if you want to do an experiment based on the severity of urticaria, one option would be to measure the severity using a scale to grade severity of itching. This becomes an operational variable. For a variable to be “good,” it needs to have some properties such as good reliability and validity, low bias, feasibility/practicality, low cost, objectivity, clarity, and acceptance. Variables can be classified into various ways as discussed below. Quantitative vs qualitative A variable can collect either qualitative or quantitative data. A variable differing in quantity is called a quantitative variable (e.g., weight of a group of patients), whereas a variable differing in quality is called a qualitative variable (e.g., the Fitzpatrick skin type) A simple test which can be used to differentiate between qualitative and quantitative variables is the subtraction test. If you can subtract the value of one variable from the other to get a meaningful result, then you are dealing with a quantitative variable (this of course will not apply to rating scales/ranks). Quantitative variables can be either discrete or continuous Discrete variables are variables in which no values may be assumed between the two given values (e.g., number of lesions in each patient in a sample of patients with urticaria). Continuous variables, on the other hand, can take any value in between the two given values (e.g., duration for which the weals last in the same sample of patients with urticaria). One way of differentiating between continuous and discrete variables is to use the “mid-way” test. If, for every pair of values of a variable, a value exactly mid-way between them is meaningful, the variable is continuous. For example, two values for the time taken for a weal to subside can be 10 and 13 min. The mid-way value would be 11.5 min which makes sense. However, for a number of weals, suppose you have a pair of values – 5 and 8 – the midway value would be 6.5 weals, which does not make sense. Under the umbrella of qualitative variables, you can have nominal/categorical variables and ordinal variables Nominal/categorical variables are, as the name suggests, variables which can be slotted into different categories (e.g., gender or type of psoriasis). Ordinal variables or ranked variables are similar to categorical, but can be put into an order (e.g., a scale for severity of itching). Dependent and independent variables In the context of an experimental study, the dependent variable (also called outcome variable) is directly linked to the primary outcome of the study. For example, in a clinical trial on psoriasis, the PASI (psoriasis area severity index) would possibly be one dependent variable. The independent variable (sometime also called explanatory variable) is something which is not affected by the experiment itself but which can be manipulated to affect the dependent variable. Other terms sometimes used synonymously include blocking variable, covariate, or predictor variable. Confounding variables are extra variables, which can have an effect on the experiment. They are linked with dependent and independent variables and can cause spurious association. For example, in a clinical trial for a topical treatment in psoriasis, the concomitant use of moisturizers might be a confounding variable. A control variable is a variable that must be kept constant during the course of an experiment. Descriptive Statistics Statistics can be broadly divided into descriptive statistics and inferential statistics.[ 3 , 4 ] Descriptive statistics give a summary about the sample being studied without drawing any inferences based on probability theory. Even if the primary aim of a study involves inferential statistics, descriptive statistics are still used to give a general summary. When we describe the population using tools such as frequency distribution tables, percentages, and other measures of central tendency like the mean, for example, we are talking about descriptive statistics. When we use a specific statistical test (e.g., Mann–Whitney U-test) to compare the mean scores and express it in terms of statistical significance, we are talking about inferential statistics. Descriptive statistics can help in summarizing data in the form of simple quantitative measures such as percentages or means or in the form of visual summaries such as histograms and box plots. Descriptive statistics can be used to describe a single variable (univariate analysis) or more than one variable (bivariate/multivariate analysis). In the case of more than one variable, descriptive statistics can help summarize relationships between variables using tools such as scatter plots. Descriptive statistics can be broadly put under two categories: Sorting/grouping and illustration/visual displays Summary statistics. Sorting and grouping Sorting and grouping is most commonly done using frequency distribution tables. For continuous variables, it is generally better to use groups in the frequency table. Ideally, group sizes should be equal (except in extreme ends where open groups are used; e.g., age “greater than” or “less than”). Another form of presenting frequency distributions is the “stem and leaf” diagram, which is considered to be a more accurate form of description. Suppose the weight in kilograms of a group of 10 patients is as follows: 56, 34, 48, 43, 87, 78, 54, 62, 61, 59 The “stem” records the value of the “ten's” place (or higher) and the “leaf” records the value in the “one's” place [ Table 1 ]. Stem and leaf plot 0 - 1 - 2 - 3 4 4 3 8 5 4 6 9 6 1 2 7 8 8 7 9 - Illustration/visual display of data The most common tools used for visual display include frequency diagrams, bar charts (for noncontinuous variables) and histograms (for continuous variables). Composite bar charts can be used to compare variables. For example, the frequency distribution in a sample population of males and females can be illustrated as given in Figure 1 . Composite bar chart A pie chart helps show how a total quantity is divided among its constituent variables. Scatter diagrams can be used to illustrate the relationship between two variables. For example, global scores given for improvement in a condition like acne by the patient and the doctor [ Figure 2 ]. Scatter diagram Summary statistics The main tools used for summary statistics are broadly grouped into measures of central tendency (such as mean, median, and mode) and measures of dispersion or variation (such as range, standard deviation, and variance). Imagine that the data below represent the weights of a sample of 15 pediatric patients arranged in ascending order: 30, 35, 37, 38, 38, 38, 42, 42, 44, 46, 47, 48, 51, 53, 86 Just having the raw data does not mean much to us, so we try to express it in terms of some values, which give a summary of the data. The mean is basically the sum of all the values divided by the total number. In this case, we get a value of 45. The problem is that some extreme values (outliers), like “'86,” in this case can skew the value of the mean. In this case, we consider other values like the median, which is the point that divides the distribution into two equal halves. It is also referred to as the 50 th percentile (50% of the values are above it and 50% are below it). In our previous example, since we have already arranged the values in ascending order we find that the point which divides it into two equal halves is the 8 th value – 42. In case of a total number of values being even, we choose the two middle points and take an average to reach the median. The mode is the most common data point. In our example, this would be 38. The mode as in our case may not necessarily be in the center of the distribution. The median is the best measure of central tendency from among the mean, median, and mode. In a “symmetric” distribution, all three are the same, whereas in skewed data the median and mean are not the same; lie more toward the skew, with the mean lying further to the skew compared with the median. For example, in Figure 3 , a right skewed distribution is seen (direction of skew is based on the tail); data values' distribution is longer on the right-hand (positive) side than on the left-hand side. The mean is typically greater than the median in such cases. Location of mode, median, and mean Measures of dispersion The range gives the spread between the lowest and highest values. In our previous example, this will be 86-30 = 56. A more valuable measure is the interquartile range. A quartile is one of the values which break the distribution into four equal parts. The 25 th percentile is the data point which divides the group between the first one-fourth and the last three-fourth of the data. The first one-fourth will form the first quartile. The 75 th percentile is the data point which divides the distribution into a first three-fourth and last one-fourth (the last one-fourth being the fourth quartile). The range between the 25 th percentile and 75 th percentile is called the interquartile range. Variance is also a measure of dispersion. The larger the variance, the further the individual units are from the mean. Let us consider the same example we used for calculating the mean. The mean was 45. For the first value (30), the deviation from the mean will be 15; for the last value (86), the deviation will be 41. Similarly we can calculate the deviations for all values in a sample. Adding these deviations and averaging will give a clue to the total dispersion, but the problem is that since the deviations are a mix of negative and positive values, the final total becomes zero. To calculate the variance, this problem is overcome by adding squares of the deviations. So variance would be the sum of squares of the variation divided by the total number in the population (for a sample we use “n − 1”). To get a more realistic value of the average dispersion, we take the square root of the variance, which is called the “standard deviation.” The box plot The box plot is a composite representation that portrays the mean, median, range, and the outliers [ Figure 4 ]. The concept of skewness and kurtosis Skewness is a measure of the symmetry of distribution. Basically if the distribution curve is symmetric, it looks the same on either side of the central point. When this is not the case, it is said to be skewed. Kurtosis is a representation of outliers. Distributions with high kurtosis tend to have “heavy tails” indicating a larger number of outliers, whereas distributions with low kurtosis have light tails, indicating lesser outliers. There are formulas to calculate both skewness and kurtosis [Figures ​ [Figures5 5 – 8 ]. Positive skew High kurtosis (positive kurtosis – also called leptokurtic) Negative skew Low kurtosis (negative kurtosis – also called “Platykurtic”) Sample Size In an ideal study, we should be able to include all units of a particular population under study, something that is referred to as a census.[ 5 , 6 ] This would remove the chances of sampling error (difference between the outcome characteristics in a random sample when compared with the true population values – something that is virtually unavoidable when you take a random sample). However, it is obvious that this would not be feasible in most situations. Hence, we have to study a subset of the population to reach to our conclusions. This representative subset is a sample and we need to have sufficient numbers in this sample to make meaningful and accurate conclusions and reduce the effect of sampling error. We also need to know that broadly sampling can be divided into two types – probability sampling and nonprobability sampling. Examples of probability sampling include methods such as simple random sampling (each member in a population has an equal chance of being selected), stratified random sampling (in nonhomogeneous populations, the population is divided into subgroups – followed be random sampling in each subgroup), systematic (sampling is based on a systematic technique – e.g., every third person is selected for a survey), and cluster sampling (similar to stratified sampling except that the clusters here are preexisting clusters unlike stratified sampling where the researcher decides on the stratification criteria), whereas nonprobability sampling, where every unit in the population does not have an equal chance of inclusion into the sample, includes methods such as convenience sampling (e.g., sample selected based on ease of access) and purposive sampling (where only people who meet specific criteria are included in the sample). An accurate calculation of sample size is an essential aspect of good study design. It is important to calculate the sample size much in advance, rather than have to go for post hoc analysis. A sample size that is too less may make the study underpowered, whereas a sample size which is more than necessary might lead to a wastage of resources. We will first go through the sample size calculation for a hypothesis-based design (like a randomized control trial). The important factors to consider for sample size calculation include study design, type of statistical test, level of significance, power and effect size, variance (standard deviation for quantitative data), and expected proportions in the case of qualitative data. This is based on previous data, either based on previous studies or based on the clinicians' experience. In case the study is something being conducted for the first time, a pilot study might be conducted which helps generate these data for further studies based on a larger sample size). It is also important to know whether the data follow a normal distribution or not. Two essential aspects we must understand are the concept of Type I and Type II errors. In a study that compares two groups, a null hypothesis assumes that there is no significant difference between the two groups, and any observed difference being due to sampling or experimental error. When we reject a null hypothesis, when it is true, we label it as a Type I error (also denoted as “alpha,” correlating with significance levels). In a Type II error (also denoted as “beta”), we fail to reject a null hypothesis, when the alternate hypothesis is actually true. Type II errors are usually expressed as “1- β,” correlating with the power of the test. While there are no absolute rules, the minimal levels accepted are 0.05 for α (corresponding to a significance level of 5%) and 0.20 for β (corresponding to a minimum recommended power of “1 − 0.20,” or 80%). Effect size and minimal clinically relevant difference For a clinical trial, the investigator will have to decide in advance what clinically detectable change is significant (for numerical data, this is could be the anticipated outcome means in the two groups, whereas for categorical data, it could correlate with the proportions of successful outcomes in two groups.). While we will not go into details of the formula for sample size calculation, some important points are as follows: In the context where effect size is involved, the sample size is inversely proportional to the square of the effect size. What this means in effect is that reducing the effect size will lead to an increase in the required sample size. Reducing the level of significance (alpha) or increasing power (1-β) will lead to an increase in the calculated sample size. An increase in variance of the outcome leads to an increase in the calculated sample size. A note is that for estimation type of studies/surveys, sample size calculation needs to consider some other factors too. This includes an idea about total population size (this generally does not make a major difference when population size is above 20,000, so in situations where population size is not known we can assume a population of 20,000 or more). The other factor is the “margin of error” – the amount of deviation which the investigators find acceptable in terms of percentages. Regarding confidence levels, ideally, a 95% confidence level is the minimum recommended for surveys too. Finally, we need an idea of the expected/crude prevalence – either based on previous studies or based on estimates. Sample size calculation also needs to add corrections for patient drop-outs/lost-to-follow-up patients and missing records. An important point is that in some studies dealing with rare diseases, it may be difficult to achieve desired sample size. In these cases, the investigators might have to rework outcomes or maybe pool data from multiple centers. Although post hoc power can be analyzed, a better approach suggested is to calculate 95% confidence intervals for the outcome and interpret the study results based on this. Financial support and sponsorship Conflicts of interest. There are no conflicts of interest. Transcription Service for Your Academic Paper Start Transcription now Editing & Proofreading for Your Research Paper Get it proofread now Online Printing & Binding with Free Express Delivery Configure binding now Academic essay overview The writing process Structuring academic essays Types of academic essays Academic writing overview Sentence structure Academic writing process Improving your academic writing Titles and headings APA style overview APA citation & referencing APA structure & sections Citation & referencing Structure and sections APA examples overview Commonly used citations Other examples British English vs. American English Chicago style overview Chicago citation & referencing Chicago structure & sections Chicago style examples Citing sources overview Citation format Citation examples College essay overview Application How to write a college essay Types of college essays Commonly confused words Definitions Dissertation overview Dissertation structure & sections Dissertation writing process Graduate school overview Application & admission Study abroad Master degree Harvard referencing overview Language rules overview Grammatical rules & structures Parts of speech Punctuation Methodology overview Analyzing data Experiments Observations Inductive vs. Deductive Qualitative vs. Quantitative Types of validity Types of reliability Sampling methods Theories & Concepts Types of research studies Types of variables MLA style overview MLA examples MLA citation & referencing MLA structure & sections Plagiarism overview Plagiarism checker Types of plagiarism Printing production overview Research bias overview Types of research bias Example sections Types of research papers Research process overview Problem statement Research proposal Research topic Statistics overview Levels of measurment Frequency distribution Measures of central tendency Measures of variability Hypothesis testing Parameters & test statistics Types of distributions Correlation Effect size Hypothesis testing assumptions Types of ANOVAs Types of chi-square Statistical data Statistical models Spelling mistakes Tips overview Academic writing tips Dissertation tips Sources tips Working with sources overview Evaluating sources Finding sources Including sources Types of sources Your Step to Success Transcription Service for Your Paper Printing & Binding with 3D Live Preview Types of Variables in Research – Definition & Examples How do you like this article cancel reply. Save my name, email, and website in this browser for the next time I comment. A fundamental component in statistical investigations is the methodology you employ in selecting your research variables. The careful selection of appropriate variable types can significantly enhance the robustness of your experimental design . This piece explores the diverse array of variable classifications within the field of statistical research. Additionally, understanding the different types of variables in research can greatly aid in shaping your experimental hypotheses and outcomes. Inhaltsverzeichnis 1 Types of Variables in Research – In a Nutshell 2 Definition: Types of variables in research 3 Types of variables in research – Quantitative vs. Categorical 4 Types of variables in research – Independent vs. Dependent 5 Other useful types of variables in research Types of Variables in Research – In a Nutshell A variable is an attribute of an item of analysis in research. The types of variables in research can be categorized into: independent vs. dependent , or categorical vs. quantitative . The types of variables in research (correlational) can be classified into predictor or outcome variables. Other types of variables in research are confounding variables , latent variables , and composite variables. Definition: Types of variables in research A variable is a trait of an item of analysis in research. Types of variables in research are imperative, as they describe and measure places, people, ideas , or other research objects . There are many types of variables in research. Therefore, you must choose the right types of variables in research for your study. Note that the correct variable will help with your research design , test selection, and result interpretation. In a study testing whether some genders are more stress-tolerant than others, variables you can include are the level of stressors in the study setting, male and female subjects, and productivity levels in the presence of stressors. Also, before choosing which types of variables in research to use, you should know how the various types work and the ideal statistical tests and result interpretations you will use for your study. The key is to determine the type of data the variable contains and the part of the experiment the variable represents. Types of variables in research – Quantitative vs. Categorical Data is the precise extent of a variable in statistical research that you record in a data sheet. It is generally divided into quantitative and categorical classes. Quantitative or numerical data represents amounts, while categorical data represents collections or groupings. The type of data contained in your variable will determine the types of variables in research. For instance, variables consisting of quantitative data are called quantitative variables, while those containing categorical data are called categorical variables. The section below explains these two types of variables in research better. Quantitative variables The scores you record when collecting quantitative data usually represent real values you can add, divide , subtract , or multiply . There are two types of quantitative variables: discrete variables and continuous variables . The table below explains the elements that set apart discrete and continuous types of variables in research: Discrete or integer variables Individual item counts or values • Number of employees in a company • Number of students in a school district Continuous or ratio variables Measurements of non-finite or continuous scores • Age • Weight • Volume • Distance Categorical variables Categorical variables contain data representing groupings. Additionally, the data in categorical variables is sometimes recorded as numbers . However, the numbers represent categories instead of real amounts. There are three categorical types of variables in research: nominal variables, ordinal variables , and binary variables . Here is a tabular summary. Binary/dichotomous variables YES/NO outcomes • Win/lose in a game • Pass/fail in an exam Nominal variables No-rank groups or orders between groups • Colors • Participant name • Brand names Ordinal variables Groups ranked in a particular order • Performance rankings in an exam • Rating scales of survey responses It is worth mentioning that some categorical variables can function as multiple types. For example, in some studies, you can use ordinal variables as quantitative variables if the scales are numerical and not discrete. Data sheet of quantitative and categorical variables A data sheet is where you record the data on the variables in your experiment. In a study of the salt-tolerance levels of various plant species, you can record the data on salt addition and how the plant responds in your datasheet. The key is to gather the information and draw a conclusion over a specific period and filling out a data sheet along the process. Below is an example of a data sheet containing binary, nominal, continuous , and ordinal types of variables in research. A 12 0 - - - A 18 50 - - - B 11 0 - - - B 15 50 - - - C 25 0 - - - C 31 50 - - - Types of variables in research – Independent vs. Dependent The purpose of experiments is to determine how the variables affect each other. As stated in our experiment above, the study aims to find out how the quantity of salt introduce in the water affects the plant’s growth and survival. Therefore, the researcher manipulates the independent variables and measures the dependent variables . Additionally, you may have control variables that you hold constant. The table below summarizes independent variables, dependent variables , and control variables . Independent/ treatment variables The variables you manipulate to affect the experiment outcome The amount of salt added to the water Dependent/ response variables The variable that represents the experiment outcomes The plant’s growth or survival Control variables Variables held constant throughout the study Temperature or light in the experiment room Data sheet of independent and dependent variables In salt-tolerance research, there is one independent variable (salt amount) and three independent variables. All other variables are neither dependent nor independent. Below is a data sheet based on our experiment: Types of variables in correlational research The types of variables in research may differ depending on the study. In correlational research , dependent and independent variables do not apply because the study objective is not to determine the cause-and-effect link between variables. However, in correlational research, one variable may precede the other, as illness leads to death, and not vice versa. In such an instance, the preceding variable, like illness, is the predictor variable, while the other one is the outcome variable. Other useful types of variables in research The key to conducting effective research is to define your types of variables as independent and dependent. Next, you must determine if they are categorical or numerical types of variables in research so you can choose the proper statistical tests for your study. Below are other types of variables in research worth understanding. Confounding variables Hides the actual impact of an alternative variable in your study Pot size and soil type Latent variables Cannot be measured directly Salt tolerance Composite variables Formed by combining multiple variables The health variables combined into a single health score What is the definition for independent and dependent variables? An autonomous or independent variable is the one you believe is the origin of the outcome, while the dependent variable is the one you believe affects the outcome of your study. What are quantitative and categorical variables? Knowing the types of variables in research that you can work with will help you choose the best statistical tests and result representation techniques. It will also help you with your study design. Discrete and continuous variables: What is their difference? Discrete variables are types of variables in research that represent counts, like the quantities of objects. In contrast, continuous variables are types of variables in research that represent measurable quantities like age, volume, and weight. I printed 3 copies of my PhD thesis and got 1 copy for free. The quality of... We use cookies on our website. Some of them are essential, while others help us to improve this website and your experience. External Media Individual Privacy Preferences Cookie Details Privacy Policy Imprint Here you will find an overview of all cookies used. You can give your consent to whole categories or display further information and select certain cookies. Accept all Save Essential cookies enable basic functions and are necessary for the proper function of the website. Show Cookie Information Hide Cookie Information Name Anbieter Eigentümer dieser Website, Zweck Speichert die Einstellungen der Besucher, die in der Cookie Box von Borlabs Cookie ausgewählt wurden. Cookie Name borlabs-cookie Cookie Laufzeit 1 Jahr Name Anbieter Bachelorprint Zweck Erkennt das Herkunftsland und leitet zur entsprechenden Sprachversion um. Datenschutzerklärung Host(s) ip-api.com Cookie Name georedirect Cookie Laufzeit 1 Jahr Name Anbieter Playcanvas Zweck Display our 3D product animations Datenschutzerklärung Host(s) playcanv.as, playcanvas.as, playcanvas.com Cookie Laufzeit 1 Jahr Statistics cookies collect information anonymously. This information helps us to understand how our visitors use our website. Akzeptieren Name Anbieter Google Ireland Limited, Gordon House, Barrow Street, Dublin 4, Ireland Zweck Cookie von Google zur Steuerung der erweiterten Script- und Ereignisbehandlung. Datenschutzerklärung Cookie Name _ga,_gat,_gid Cookie Laufzeit 2 Jahre Content from video platforms and social media platforms is blocked by default. If External Media cookies are accepted, access to those contents no longer requires manual consent. Akzeptieren Name Anbieter Meta Platforms Ireland Limited, 4 Grand Canal Square, Dublin 2, Ireland Zweck Wird verwendet, um Facebook-Inhalte zu entsperren. Datenschutzerklärung Host(s) .facebook.com Akzeptieren Name Anbieter Google Ireland Limited, Gordon House, Barrow Street, Dublin 4, Ireland Zweck Wird zum Entsperren von Google Maps-Inhalten verwendet. Datenschutzerklärung Host(s) .google.com Cookie Name NID Cookie Laufzeit 6 Monate Akzeptieren Name Anbieter Meta Platforms Ireland Limited, 4 Grand Canal Square, Dublin 2, Ireland Zweck Wird verwendet, um Instagram-Inhalte zu entsperren. Datenschutzerklärung Host(s) .instagram.com Cookie Name pigeon_state Cookie Laufzeit Sitzung Akzeptieren Name Anbieter Openstreetmap Foundation, St John’s Innovation Centre, Cowley Road, Cambridge CB4 0WS, United Kingdom Zweck Wird verwendet, um OpenStreetMap-Inhalte zu entsperren. Datenschutzerklärung Host(s) .openstreetmap.org Cookie Name _osm_location, _osm_session, _osm_totp_token, _osm_welcome, _pk_id., _pk_ref., _pk_ses., qos_token Cookie Laufzeit 1-10 Jahre Akzeptieren Name Anbieter Twitter International Company, One Cumberland Place, Fenian Street, Dublin 2, D02 AX07, Ireland Zweck Wird verwendet, um Twitter-Inhalte zu entsperren. Datenschutzerklärung Host(s) .twimg.com, .twitter.com Cookie Name __widgetsettings, local_storage_support_test Cookie Laufzeit Unbegrenzt Akzeptieren Name Anbieter Vimeo Inc., 555 West 18th Street, New York, New York 10011, USA Zweck Wird verwendet, um Vimeo-Inhalte zu entsperren. Datenschutzerklärung Host(s) player.vimeo.com Cookie Name vuid Cookie Laufzeit 2 Jahre Akzeptieren Name Anbieter Google Ireland Limited, Gordon House, Barrow Street, Dublin 4, Ireland Zweck Wird verwendet, um YouTube-Inhalte zu entsperren. Datenschutzerklärung Host(s) google.com Cookie Name NID Cookie Laufzeit 6 Monate Privacy Policy Imprint Variables in Quantitative Research: A Beginner's Guide (COUN) Quantitative variables. Because quantitative methodology requires measurement, the concepts being investigated need to be defined in a way that can be measured. Organizational change, reading comprehension, emergency response, or depression are concepts, but they cannot be measured as such. Frequency of organizational change, reading comprehension scores, emergency response time, or types of depression can be measured. They are variables (concepts that can vary). Independent variables (IV). Dependent variables (DV). Sample variables. Extraneous variables. Independent Variables (IV) Independent variables (IV) are those that are suspected of being the cause in a causal relationship. If you are asking a cause and effect question, your IV will be the variable (or variables) that you suspect causes the effect. There are two main sorts of IV—active independent variables and attribute independent variables: Active IV are interventions or conditions that are being applied to the participants. A special tutorial for the third graders, a new therapy for clients, or a new training program being tested on employees would be active IVs. Attribute IV are intrinsic characteristics of the participants that are suspected of causing a result. For example, if you are examining whether gender which is intrinsic to the participants results in higher or lower scores on some skill, gender is an attribute IV. Both types of IV can have what are called levels. For example: In the example above, the active IV special tutorial , receiving the tutorial is one level, and tutorial withheld (control) is a second level. In the same example, being a third grader would be an attribute IV. It could be defined as only one level—being in third grade or you might wish to define it with more than one level, such as first half of third grade and second half of third grade. Indeed, that attribute IV could take many more, for example, if you wished to look at each month of third grade. Independent variables are frequently called different things depending on the nature of the research question. In predictive questions, where a variable is thought to predict another but it is not yet appropriate to ask whether it causes the other, the IV is usually called a predictor or criterion variable rather than an independent variable. Dependent Variables (DV) Dependent variables are variables that depend on or are influenced by the independent variables. They are outcomes or results of the influence of the independent variable. Dependent variables answer the question, "What do I observe happening when I apply the intervention?" The dependent variable receives the intervention. In questions where full clausation is not assumed, such as a predictive question or a question about differences between groups but no manipulation of an IV, the dependent variables are usually called outcome variable s, and the independent variables are usually called the predictor or criterion variables. Sample Variables In some studies, some characteristic of the participants must be measured for some reason, but that characteristic is not the IV or the DV. In this case, these are called sample variables. For example, suppose you are investigating whether amount of sleep affects level of concentration in depressed people. In order to obtain a sample of depressed people, a standard test of depression will be given. So the presence or absence of depression will be a sample variable. That score is not used as an IV or a DV, but simply to get the appropriate people into the sample. When there is no measure of a characteristic of the participants, the characteristic is called a sample characteristic . When the characteristic must be measured, it is called a sample variable . Extraneous Variables Extraneous variables are not of interest to the study, but may influence the dependent variable. For this reason, most quantitative studies attempt to control extraneous variables. The literature should inform you what extraneous variables to account for. For example, in the study of third graders' reading scores, variables such as noise levels in the testing room, the size or lighting or temperature of the room, and whether the children had had a good breakfast might be extraneous variables. There is a special class of extraneous variables called confounding variables. These are variables that can cause the effect we are looking for if they are not controlled for, resulting in a false finding that the IV is effective when it is not. In a study of changes in skill levels in a group of caseworkers after a training program, if the follow-up measure is taken relatively late after the training, the simple effect of practicing the skills might explain improved scores, and the training might be mistakenly thought to be successful when it was not. There are many details about variables not covered in this handout. Please consult any text on research methods for a more comprehensive review. Doc. reference: phd_t2_coun_u02s2_h02_quantvar.html RESEARCH VARIABLES: TYPES, USES AND DEFINITION OF TERMS In book: Research in Education (pp.43-54) Publisher: His Lineage Publishing House University of Ibadan Discover the world's research 25+ million members 160+ million publication pages 2.3+ billion citations Tiffany Angelene Dharsono Marylise Hebrard Ahmed Ali DARAR Susan Sabillo Merry Jean Tiauzon Mylene A Bautista Elsye Mayshelly Jonathan Phelipe Silaban Kapeso Singogo Louis Cohen Lawrence Manion Keith Morrison J O Adeleke O Aderounmu D O Owuamanam A E Uzoagulu Recruit researchers Join for free Login Email Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google Welcome back! Please log in. Email · Hint Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google No account? Sign up Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices. Descriptive Research and Case Studies Learning objectives. Explain the importance and uses of descriptive research, especially case studies, in studying abnormal behavior Types of Research Methods There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior and the cognitive and biological processes that underlie it. Some methods rely on observational techniques. Other approaches involve interactions between the researcher and the individuals who are being studied—ranging from a series of simple questions; to extensive, in-depth interviews; to well-controlled experiments. The three main categories of psychological research are descriptive, correlational, and experimental research. Research studies that do not test specific relationships between variables are called descriptive, or qualitative, studies . These studies are used to describe general or specific behaviors and attributes that are observed and measured. In the early stages of research, it might be difficult to form a hypothesis, especially when there is not any existing literature in the area. In these situations designing an experiment would be premature, as the question of interest is not yet clearly defined as a hypothesis. Often a researcher will begin with a non-experimental approach, such as a descriptive study, to gather more information about the topic before designing an experiment or correlational study to address a specific hypothesis. Descriptive research is distinct from correlational research , in which psychologists formally test whether a relationship exists between two or more variables. Experimental research goes a step further beyond descriptive and correlational research and randomly assigns people to different conditions, using hypothesis testing to make inferences about how these conditions affect behavior. It aims to determine if one variable directly impacts and causes another. Correlational and experimental research both typically use hypothesis testing, whereas descriptive research does not. Each of these research methods has unique strengths and weaknesses, and each method may only be appropriate for certain types of research questions. For example, studies that rely primarily on observation produce incredible amounts of information, but the ability to apply this information to the larger population is somewhat limited because of small sample sizes. Survey research, on the other hand, allows researchers to easily collect data from relatively large samples. While surveys allow results to be generalized to the larger population more easily, the information that can be collected on any given survey is somewhat limited and subject to problems associated with any type of self-reported data. Some researchers conduct archival research by using existing records. While existing records can be a fairly inexpensive way to collect data that can provide insight into a number of research questions, researchers using this approach have no control on how or what kind of data was collected. Correlational research can find a relationship between two variables, but the only way a researcher can claim that the relationship between the variables is cause and effect is to perform an experiment. In experimental research, which will be discussed later, there is a tremendous amount of control over variables of interest. While performing an experiment is a powerful approach, experiments are often conducted in very artificial settings, which calls into question the validity of experimental findings with regard to how they would apply in real-world settings. In addition, many of the questions that psychologists would like to answer cannot be pursued through experimental research because of ethical concerns. The three main types of descriptive studies are case studies, naturalistic observation, and surveys. Clinical or Case Studies Psychologists can use a detailed description of one person or a small group based on careful observation.  Case studies  are intensive studies of individuals and have commonly been seen as a fruitful way to come up with hypotheses and generate theories. Case studies add descriptive richness. Case studies are also useful for formulating concepts, which are an important aspect of theory construction. Through fine-grained knowledge and description, case studies can fully specify the causal mechanisms in a way that may be harder in a large study. Sigmund Freud   developed  many theories from case studies (Anna O., Little Hans, Wolf Man, Dora, etc.). F or example, he conducted a case study of a man, nicknamed “Rat Man,”  in which he claimed that this patient had been cured by psychoanalysis.  T he nickname derives from the fact that among the patient’s many compulsions, he had an obsession with nightmarish fantasies about rats.  Today, more commonly, case studies reflect an up-close, in-depth, and detailed examination of an individual’s course of treatment. Case studies typically include a complete history of the subject’s background and response to treatment. From the particular client’s experience in therapy, the therapist’s goal is to provide information that may help other therapists who treat similar clients. Case studies are generally a single-case design, but can also be a multiple-case design, where replication instead of sampling is the criterion for inclusion. Like other research methodologies within psychology, the case study must produce valid and reliable results in order to be useful for the development of future research. Distinct advantages and disadvantages are associated with the case study in psychology. A commonly described limit of case studies is that they do not lend themselves to generalizability . The other issue is that the case study is subject to the bias of the researcher in terms of how the case is written, and that cases are chosen because they are consistent with the researcher’s preconceived notions, resulting in biased research. Another common problem in case study research is that of reconciling conflicting interpretations of the same case history. Despite these limitations, there are advantages to using case studies. One major advantage of the case study in psychology is the potential for the development of novel hypotheses of the  cause of abnormal behavior   for later testing. Second, the case study can provide detailed descriptions of specific and rare cases and help us study unusual conditions that occur too infrequently to study with large sample sizes. The major disadvantage is that case studies cannot be used to determine causation, as is the case in experimental research, where the factors or variables hypothesized to play a causal role are manipulated or controlled by the researcher.  Link to Learning: Famous Case Studies Some well-known case studies that related to abnormal psychology include the following: Harlow— Phineas Gage Breuer & Freud (1895)— Anna O. Cleckley’s case studies: on psychopathy ( The Mask of Sanity ) (1941) and multiple personality disorder ( The Three Faces of Eve ) (1957) Freud and  Little Hans Freud and the  Rat Man John Money and the  John/Joan case Genie (feral child) Piaget’s studies Rosenthal’s book on the  murder of Kitty Genovese Washoe (sign language) Patient H.M. Naturalistic Observation If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances are that almost everyone in the classroom will raise their hand, but do you think hand washing after every trip to the restroom is really that universal? This is very similar to the phenomenon mentioned earlier in this module: many individuals do not feel comfortable answering a question honestly. But if we are committed to finding out the facts about handwashing, we have other options available to us. Suppose we send a researcher to a school playground to observe how aggressive or socially anxious children interact with peers. Will our observer blend into the playground environment by wearing a white lab coat, sitting with a clipboard, and staring at the swings? We want our researcher to be inconspicuous and unobtrusively positioned—perhaps pretending to be a school monitor while secretly recording the relevant information. This type of observational study is called naturalistic observation : observing behavior in its natural setting. To better understand peer exclusion, Suzanne Fanger collaborated with colleagues at the University of Texas to observe the behavior of preschool children on a playground. How did the observers remain inconspicuous over the duration of the study? They equipped a few of the children with wireless microphones (which the children quickly forgot about) and observed while taking notes from a distance. Also, the children in that particular preschool (a “laboratory preschool”) were accustomed to having observers on the playground (Fanger, Frankel, & Hazen, 2012). It is critical that the observer be as unobtrusive and as inconspicuous as possible: when people know they are being watched, they are less likely to behave naturally. For example, psychologists have spent weeks observing the behavior of homeless people on the streets, in train stations, and bus terminals. They try to ensure that their naturalistic observations are unobtrusive, so as to minimize interference with the behavior they observe. Nevertheless, the presence of the observer may distort the behavior that is observed, and this must be taken into consideration (Figure 1). The greatest benefit of naturalistic observation is the validity, or accuracy, of information collected unobtrusively in a natural setting. Having individuals behave as they normally would in a given situation means that we have a higher degree of ecological validity, or realism, than we might achieve with other research approaches. Therefore, our ability to generalize the findings of the research to real-world situations is enhanced. If done correctly, we need not worry about people modifying their behavior simply because they are being observed. Sometimes, people may assume that reality programs give us a glimpse into authentic human behavior. However, the principle of inconspicuous observation is violated as reality stars are followed by camera crews and are interviewed on camera for personal confessionals. Given that environment, we must doubt how natural and realistic their behaviors are. The major downside of naturalistic observation is that they are often difficult to set up and control. Although something as simple as observation may seem like it would be a part of all research methods, participant observation is a distinct methodology that involves the researcher embedding themselves into a group in order to study its dynamics. For example, Festinger, Riecken, and Shacter (1956) were very interested in the psychology of a particular cult. However, this cult was very secretive and wouldn’t grant interviews to outside members. So, in order to study these people, Festinger and his colleagues pretended to be cult members, allowing them access to the behavior and psychology of the cult. Despite this example, it should be noted that the people being observed in a participant observation study usually know that the researcher is there to study them. [1] Another potential problem in observational research is observer bias . Generally, people who act as observers are closely involved in the research project and may unconsciously skew their observations to fit their research goals or expectations. To protect against this type of bias, researchers should have clear criteria established for the types of behaviors recorded and how those behaviors should be classified. In addition, researchers often compare observations of the same event by multiple observers, in order to test inter-rater reliability : a measure of reliability that assesses the consistency of observations by different observers. Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally (Figure 3). Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people. Surveys allow researchers to gather data from larger samples than may be afforded by other research methods . A sample is a subset of individuals selected from a population , which is the overall group of individuals that the researchers are interested in. Researchers study the sample and seek to generalize their findings to the population. There is both strength and weakness in surveys when compared to case studies. By using surveys, we can collect information from a larger sample of people. A larger sample is better able to reflect the actual diversity of the population, thus allowing better generalizability. Therefore, if our sample is sufficiently large and diverse, we can assume that the data we collect from the survey can be generalized to the larger population with more certainty than the information collected through a case study. However, given the greater number of people involved, we are not able to collect the same depth of information on each person that would be collected in a case study. Another potential weakness of surveys is something we touched on earlier in this module: people do not always give accurate responses. They may lie, misremember, or answer questions in a way that they think makes them look good. For example, people may report drinking less alcohol than is actually the case. Any number of research questions can be answered through the use of surveys. One real-world example is the research conducted by Jenkins, Ruppel, Kizer, Yehl, and Griffin (2012) about the backlash against the U.S. Arab-American community following the terrorist attacks of September 11, 2001. Jenkins and colleagues wanted to determine to what extent these negative attitudes toward Arab-Americans still existed nearly a decade after the attacks occurred. In one study, 140 research participants filled out a survey with 10 questions, including questions asking directly about the participant’s overt prejudicial attitudes toward people of various ethnicities. The survey also asked indirect questions about how likely the participant would be to interact with a person of a given ethnicity in a variety of settings (such as, “How likely do you think it is that you would introduce yourself to a person of Arab-American descent?”). The results of the research suggested that participants were unwilling to report prejudicial attitudes toward any ethnic group. However, there were significant differences between their pattern of responses to questions about social interaction with Arab-Americans compared to other ethnic groups: they indicated less willingness for social interaction with Arab-Americans compared to the other ethnic groups. This suggested that the participants harbored subtle forms of prejudice against Arab-Americans, despite their assertions that this was not the case (Jenkins et al., 2012). Think it Over Research has shown that parental depressive symptoms are linked to a number of negative child outcomes. A classmate of yours is interested in  the associations between parental depressive symptoms and actual child behaviors in everyday life [2] because this associations remains largely unknown. After reading this section, what do you think is the best way to better understand such associations? Which method might result in the most valid data? clinical or case study:  observational research study focusing on one or a few people correlational research:  tests whether a relationship exists between two or more variables descriptive research:  research studies that do not test specific relationships between variables; they are used to describe general or specific behaviors and attributes that are observed and measured experimental research:  tests a hypothesis to determine cause-and-effect relationships generalizability:  inferring that the results for a sample apply to the larger population inter-rater reliability:  measure of agreement among observers on how they record and classify a particular event naturalistic observation:  observation of behavior in its natural setting observer bias:  when observations may be skewed to align with observer expectations population:  overall group of individuals that the researchers are interested in sample:  subset of individuals selected from the larger population survey:  list of questions to be answered by research participants—given as paper-and-pencil questionnaires, administered electronically, or conducted verbally—allowing researchers to collect data from a large number of people CC Licensed Content, Shared Previously Descriptive Research and Case Studies . Authored by : Sonja Ann Miller for Lumen Learning.  Provided by : Lumen Learning.  License :  CC BY-SA: Attribution-ShareAlike Approaches to Research.  Authored by : OpenStax College.  Located at :  http://cnx.org/contents/[email protected]:iMyFZJzg@5/Approaches-to-Research .  License :  CC BY: Attribution .  License Terms : Download for free at http://cnx.org/contents/[email protected] Descriptive Research.  Provided by : Boundless.  Located at :  https://www.boundless.com/psychology/textbooks/boundless-psychology-textbook/researching-psychology-2/types-of-research-studies-27/descriptive-research-124-12659/ .  License :  CC BY-SA: Attribution-ShareAlike Case Study.  Provided by : Wikipedia.  Located at :  https://en.wikipedia.org/wiki/Case_study .  License :  CC BY-SA: Attribution-ShareAlike Rat man.  Provided by : Wikipedia.  Located at :  https://en.wikipedia.org/wiki/Rat_Man#Legacy .  License :  CC BY-SA: Attribution-ShareAlike Case study in psychology.  Provided by : Wikipedia.  Located at :  https://en.wikipedia.org/wiki/Case_study_in_psychology .  License :  CC BY-SA: Attribution-ShareAlike Research Designs.  Authored by : Christie Napa Scollon.  Provided by : Singapore Management University.  Located at :  https://nobaproject.com/modules/research-designs#reference-6 .  Project : The Noba Project.  License :  CC BY-NC-SA: Attribution-NonCommercial-ShareAlike Single subject design.  Provided by : Wikipedia.  Located at :  https://en.wikipedia.org/wiki/Single-subject_design .  License :  CC BY-SA: Attribution-ShareAlike Single subject research.  Provided by : Wikipedia.  Located at :  https://en.wikipedia.org/wiki/Single-subject_research#A-B-A-B .  License :  Public Domain: No Known Copyright Pills.  Authored by : qimono.  Provided by : Pixabay.  Located at :  https://pixabay.com/illustrations/pill-capsule-medicine-medical-1884775/ .  License :  CC0: No Rights Reserved ABAB Design.  Authored by : Doc. Yu.  Provided by : Wikimedia.  Located at :  https://commons.wikimedia.org/wiki/File:A-B-A-B_Design.png .  License :  CC BY-SA: Attribution-ShareAlike Scollon, C. N. (2020). Research designs. In R. Biswas-Diener & E. Diener (Eds), Noba textbook series: Psychology. Champaign, IL: DEF publishers. Retrieved from http://noba.to/acxb2thy ↵ Slatcher, R. B., & Trentacosta, C. J. (2011). A naturalistic observation study of the links between parental depressive symptoms and preschoolers' behaviors in everyday life. Journal of family psychology : JFP : journal of the Division of Family Psychology of the American Psychological Association (Division 43), 25(3), 444–448. https://doi.org/10.1037/a0023728 ↵ Descriptive Research and Case Studies Copyright © by Meredith Palm is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted. Share This Book Browse All Articles Newsletter Sign-Up Trademarks → 17 Sep 2024 Cold Call Podcast Fawn Weaver’s Entrepreneurial Journey as an Outsider in the Spirits Industry In 2017 Fawn Weaver launched a premium American whiskey brand, Uncle Nearest. It became the fastest growing and most awarded whiskey brand in America, despite the challenges Weaver faced as a Black woman and outsider to the spirits industry, which is capital-intensive, highly regulated, competitive, and male-dominated. In October 2023, Weaver announced plans to expand into cognac with the goal of building the next major alcoholic beverages conglomerate. But the company was still heavily reliant on capital. How could Weaver convince new investors that her plans for cognac would yield success? Harvard Business School senior lecturer Hise Gibson discusses Weaver’s leadership style, growth strategies, and her use of storytelling to connect customers with her brand in the case, "Uncle Nearest: Creating a Legacy." 322 COMMENTS Types of Variables in Research & Statistics Learn how to identify and classify variables in statistical research based on data type and experiment role. See examples of quantitative, categorical, independent, dependent, and other types of variables with a salt-tolerance experiment. 15 Independent and Dependent Variable Examples An independent variable is what is manipulated in a scientific experiment to determine its effect on the dependent variable. Learn the definition and see 15 examples of independent variables in different fields, such as sports, medicine, and education. Variables in Research Learn what variables are in research and how they are classified, measured, and analyzed. Find out the different types of variables, such as independent, dependent, mediating, moderator, and more, with examples and methods. Independent & Dependent Variables (With Examples) Learn the basics of research variables, including independent, dependent, control, moderating, mediating, confounding and latent variables. Find out how to identify and measure the number of conditions/levels of an independent variable in a study. Independent vs. Dependent Variables Learn the difference between independent and dependent variables in research, and how to identify them in your study. An independent variable is the cause that you manipulate or vary, while a dependent variable is the effect that you measure or observe. 27 Types of Variables in Research and Statistics Learn the definition, explanation, pros and cons of 27 types of variables in research and statistics, such as quantitative, qualitative, discrete, continuous, nominal, ordinal, and more. See examples of each type and how they are used in data analysis. Variables in Research: Breaking Down the ... Learn how variables are the building blocks of scientific inquiry, representing the factors or characteristics that can change or vary within an experiment or study. Understand the difference between independent and dependent variables, and how to control and confounding variables, with examples from plant growth research. Variables in Research Learn about the five main types of variables in research: independent, dependent, categorical, continuous, and confounding. Understand their definitions, roles, and examples to design and analyze your study effectively. Independent and Dependent Variables Learn the definitions and examples of independent and dependent variables in psychology research. The independent variable is the presumed cause that is manipulated or changed, while the dependent variable is the measured outcome that depends on the independent variable. Types of Variables and Commonly Used Statistical Designs Suitable statistical design represents a critical factor in permitting inferences from any research or scientific study.[1] Numerous statistical designs are implementable due to the advancement of software available for extensive data analysis.[1] Healthcare providers must possess some statistical knowledge to interpret new studies and provide up-to-date patient care. We present an overview of ... Independent and Dependent Variables Examples Learn the definitions, examples and tips for identifying and graphing independent and dependent variables in scientific experiments. The independent variable is the factor the researcher controls, while the dependent variable is the one that is measured and responds to the independent variable. Examples of Variables in Research: 6 Noteworthy Phenomena Introduction. Definition of Variable. Examples of Variables in Research: 6 Phenomena. Phenomenon 1: Climate change. Phenomenon 2: Crime and violence in the streets. Phenomenon 3: Poor performance of students in college entrance exams. Phenomenon 4: Fish kill. Phenomenon 5: Poor crop growth. Phenomenon 6: How Content Goes Viral. Variables: Definition, Examples, Types of Variables in Research Learn what a variable is and how to classify variables in research based on their qualitative or quantitative nature, discrete or continuous values, and role in causal relationships. Find out the examples of variables in different fields and contexts. Types of Variables Learn the definition, examples, and categories of variables in quantitative research, such as independent, dependent, control, continuous, discrete, and categorical. Find out how to choose the appropriate statistical test based on the type of variable and data. 10 Types of Variables in Research and Statistics Learn about 10 types of variables with definitions, examples and how they affect study design and analysis. Find out the differences between quantitative, qualitative, independent, dependent, intervening, moderating, extraneous, confounding, control and composite variables. The Different Types Of Variables Used In Research And Statistics Learn about the different types of variables used in scientific experiments and statistical studies, such as independent, dependent, control, qualitative, quantitative, and more. Find out how to choose the right variables and interpret the results of your research. Types of Variables, Descriptive Statistics, and Sample Size Variables. What is a variable?[1,2] To put it in very simple terms, a variable is an entity whose value varies.A variable is an essential component of any statistical data. It is a feature of a member of a given sample or population, which is unique, and can differ in quantity or quantity from another member of the same sample or population. Types of Variables in Research Example. In a study testing whether some genders are more stress-tolerant than others, variables you can include are the level of stressors in the study setting, male and female subjects, and productivity levels in the presence of stressors.. Also, before choosing which types of variables in research to use, you should know how the various types work and the ideal statistical tests and result ... Variables in Quantitative Research: A Beginner's Guide (COUN) Learn about the four main types of variables in quantitative research: independent, dependent, sample, and extraneous. Find out how they are defined, measured, and used in different types of questions and designs. Research Variables: Types, Uses and Definition of Terms The purpose of research is to describe and explain variance in the world, that is, variance that. occurs naturally in the world or chang e that we create due to manipulation. Variables are ... Descriptive Research and Case Studies Learn about the importance and uses of descriptive research, especially case studies, in studying abnormal behavior. Find out the advantages and disadvantages of case studies, and some famous examples of psychological case studies. Types of Validity in Research Test validity is the primary focus of this article. When conducting research, you might also want to consider experimental validity, which concerns research and experiment design more broadly. Internal validity, external validity, and ecological validity relate to the quality of a study and the applicability of its results to the real world. Trademarks: Articles, Research, & Case Studies In 2017 Fawn Weaver launched a premium American whiskey brand, Uncle Nearest. It became the fastest growing and most awarded whiskey brand in America, despite the challenges Weaver faced as a Black woman and outsider to the spirits industry, which is capital-intensive, highly regulated, competitive, and male-dominated. essay homework paper phd project resume review study thesis