explanatory variable in experimental design

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1.1.2 - explanatory & response variables.

In some research studies one variable is used to predict or explain differences in another variable. In those cases, the  explanatory variable  is used to predict or explain differences in the  response variable . In an experimental study, the explanatory variable is the variable that is manipulated by the researcher. 

Also known as the independent  or  predictor variable , it explains variations in the response variable; in an experimental study, it is manipulated by the researcher

Also known as the  dependent  or  outcome variable,  its value is predicted or its variation is explained by the explanatory variable; in an experimental study, this is the outcome that is measured following manipulation of the explanatory variable

Example: Panda Fertility Treatments Section  

A team of veterinarians wants to compare the effectiveness of two fertility treatments for pandas in captivity. The two treatments are in-vitro fertilization and male fertility medications. This experiment has one  explanatory variable : type of fertility treatment. The  response variable  is a measure of fertility rate.

Example: Public Speaking Approaches Section  

A public speaking teacher has developed a new lesson that she believes decreases student anxiety in public speaking situations more than the old lesson. She designs an experiment to test if her new lesson works better than the old lesson. Public speaking students are randomly assigned to receive either the new or old lesson; their anxiety levels during a variety of public speaking experiences are measured.  This experiment has one  explanatory variable : the lesson received. The  response variable  is anxiety level.

Example: Coffee Bean Origin Section  

A researcher believes that the origin of the beans used to make a cup of coffee affects hyperactivity. He wants to compare coffee from three different regions: Africa, South America, and Mexico. The  explanatory variable is the origin of coffee bean; this has three levels: Africa, South America, and Mexico. The  response variable  is hyperactivity level.

Example: Height & Age Section  

A group of middle school students wants to know if they can use height to predict age. They take a random sample of 50 people at their school, both students and teachers, and record each individual's height and age. This is an observational study. The students want to use height to predict age so the  explanatory variable  is height and the  response variable  is age.

Example: Grade & Height Section  

Research question:  Do fourth graders tend to be taller than third graders?

This is an observational study. The researcher wants to use grade level to explain differences in height. The  explanatory variable  is grade level. The  response variable  is height. 

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Methodology

• Guide to Experimental Design | Overview, Steps, & Examples

Guide to Experimental Design | Overview, 5 steps & Examples

Published on December 3, 2019 by Rebecca Bevans . Revised on June 21, 2023.

Experiments are used to study causal relationships . You manipulate one or more independent variables and measure their effect on one or more dependent variables.

Experimental design create a set of procedures to systematically test a hypothesis . A good experimental design requires a strong understanding of the system you are studying.

There are five key steps in designing an experiment:

• Consider your variables and how they are related
• Write a specific, testable hypothesis
• Design experimental treatments to manipulate your independent variable
• Assign subjects to groups, either between-subjects or within-subjects
• Plan how you will measure your dependent variable

For valid conclusions, you also need to select a representative sample and control any  extraneous variables that might influence your results. If random assignment of participants to control and treatment groups is impossible, unethical, or highly difficult, consider an observational study instead. This minimizes several types of research bias, particularly sampling bias , survivorship bias , and attrition bias as time passes.

Table of contents

Step 1: define your variables, step 2: write your hypothesis, step 3: design your experimental treatments, step 4: assign your subjects to treatment groups, step 5: measure your dependent variable, other interesting articles, frequently asked questions about experiments.

You should begin with a specific research question . We will work with two research question examples, one from health sciences and one from ecology:

To translate your research question into an experimental hypothesis, you need to define the main variables and make predictions about how they are related.

Start by simply listing the independent and dependent variables .

Then you need to think about possible extraneous and confounding variables and consider how you might control  them in your experiment.

Finally, you can put these variables together into a diagram. Use arrows to show the possible relationships between variables and include signs to show the expected direction of the relationships.

Here we predict that increasing temperature will increase soil respiration and decrease soil moisture, while decreasing soil moisture will lead to decreased soil respiration.

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Now that you have a strong conceptual understanding of the system you are studying, you should be able to write a specific, testable hypothesis that addresses your research question.

The next steps will describe how to design a controlled experiment . In a controlled experiment, you must be able to:

• Systematically and precisely manipulate the independent variable(s).
• Precisely measure the dependent variable(s).
• Control any potential confounding variables.

If your study system doesn’t match these criteria, there are other types of research you can use to answer your research question.

How you manipulate the independent variable can affect the experiment’s external validity – that is, the extent to which the results can be generalized and applied to the broader world.

First, you may need to decide how widely to vary your independent variable.

• just slightly above the natural range for your study region.
• over a wider range of temperatures to mimic future warming.
• over an extreme range that is beyond any possible natural variation.

Second, you may need to choose how finely to vary your independent variable. Sometimes this choice is made for you by your experimental system, but often you will need to decide, and this will affect how much you can infer from your results.

• a categorical variable : either as binary (yes/no) or as levels of a factor (no phone use, low phone use, high phone use).
• a continuous variable (minutes of phone use measured every night).

How you apply your experimental treatments to your test subjects is crucial for obtaining valid and reliable results.

First, you need to consider the study size : how many individuals will be included in the experiment? In general, the more subjects you include, the greater your experiment’s statistical power , which determines how much confidence you can have in your results.

Then you need to randomly assign your subjects to treatment groups . Each group receives a different level of the treatment (e.g. no phone use, low phone use, high phone use).

You should also include a control group , which receives no treatment. The control group tells us what would have happened to your test subjects without any experimental intervention.

When assigning your subjects to groups, there are two main choices you need to make:

• A completely randomized design vs a randomized block design .
• A between-subjects design vs a within-subjects design .

Randomization

An experiment can be completely randomized or randomized within blocks (aka strata):

• In a completely randomized design , every subject is assigned to a treatment group at random.
• In a randomized block design (aka stratified random design), subjects are first grouped according to a characteristic they share, and then randomly assigned to treatments within those groups.

Sometimes randomization isn’t practical or ethical , so researchers create partially-random or even non-random designs. An experimental design where treatments aren’t randomly assigned is called a quasi-experimental design .

Between-subjects vs. within-subjects

In a between-subjects design (also known as an independent measures design or classic ANOVA design), individuals receive only one of the possible levels of an experimental treatment.

In medical or social research, you might also use matched pairs within your between-subjects design to make sure that each treatment group contains the same variety of test subjects in the same proportions.

In a within-subjects design (also known as a repeated measures design), every individual receives each of the experimental treatments consecutively, and their responses to each treatment are measured.

Within-subjects or repeated measures can also refer to an experimental design where an effect emerges over time, and individual responses are measured over time in order to measure this effect as it emerges.

Counterbalancing (randomizing or reversing the order of treatments among subjects) is often used in within-subjects designs to ensure that the order of treatment application doesn’t influence the results of the experiment.

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Finally, you need to decide how you’ll collect data on your dependent variable outcomes. You should aim for reliable and valid measurements that minimize research bias or error.

Some variables, like temperature, can be objectively measured with scientific instruments. Others may need to be operationalized to turn them into measurable observations.

• Ask participants to record what time they go to sleep and get up each day.
• Ask participants to wear a sleep tracker.

How precisely you measure your dependent variable also affects the kinds of statistical analysis you can use on your data.

Experiments are always context-dependent, and a good experimental design will take into account all of the unique considerations of your study system to produce information that is both valid and relevant to your research question.

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.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic

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

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

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

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

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

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

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

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

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

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

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

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Home » Explanatory Research – Types, Methods, Guide

Explanatory Research – Types, Methods, Guide

Table of Contents

Explanatory Research

Definition :

Explanatory research is a type of research that aims to uncover the underlying causes and relationships between different variables. It seeks to explain why a particular phenomenon occurs and how it relates to other factors.

This type of research is typically used to test hypotheses or theories and to establish cause-and-effect relationships. Explanatory research often involves collecting data through surveys , experiments , or other empirical methods, and then analyzing that data to identify patterns and correlations. The results of explanatory research can provide a better understanding of the factors that contribute to a particular phenomenon and can help inform future research or policy decisions.

Types of Explanatory Research

There are several types of explanatory research, each with its own approach and focus. Some common types include:

Experimental Research

This involves manipulating one or more variables to observe the effect on other variables. It allows researchers to establish a cause-and-effect relationship between variables and is often used in natural and social sciences.

Quasi-experimental Research

This type of research is similar to experimental research but lacks full control over the variables. It is often used in situations where it is difficult or impossible to manipulate certain variables.

Correlational Research

This type of research aims to identify relationships between variables without manipulating them. It involves measuring and analyzing the strength and direction of the relationship between variables.

Case study Research

This involves an in-depth investigation of a specific case or situation. It is often used in social sciences and allows researchers to explore complex phenomena and contexts.

Historical Research

This involves the systematic study of past events and situations to understand their causes and effects. It is often used in fields such as history and sociology.

Survey Research

This involves collecting data from a sample of individuals through structured questionnaires or interviews. It allows researchers to investigate attitudes, behaviors, and opinions.

Explanatory Research Methods

There are several methods that can be used in explanatory research, depending on the research question and the type of data being collected. Some common methods include:

Experiments

In experimental research, researchers manipulate one or more variables to observe their effect on other variables. This allows them to establish a cause-and-effect relationship between the variables.

Surveys are used to collect data from a sample of individuals through structured questionnaires or interviews. This method can be used to investigate attitudes, behaviors, and opinions.

Correlational studies

This method aims to identify relationships between variables without manipulating them. It involves measuring and analyzing the strength and direction of the relationship between variables.

Case studies

Case studies involve an in-depth investigation of a specific case or situation. This method is often used in social sciences and allows researchers to explore complex phenomena and contexts.

Secondary Data Analysis

This method involves analyzing data that has already been collected by other researchers or organizations. It can be useful when primary data collection is not feasible or when additional data is needed to support research findings.

Data Analysis Methods

Explanatory research data analysis methods are used to explore the relationships between variables and to explain how they interact with each other. Here are some common data analysis methods used in explanatory research:

Correlation Analysis

Correlation analysis is used to identify the strength and direction of the relationship between two or more variables. This method is particularly useful when exploring the relationship between quantitative variables.

Regression Analysis

Regression analysis is used to identify the relationship between a dependent variable and one or more independent variables. This method is particularly useful when exploring the relationship between a dependent variable and several predictor variables.

Path Analysis

Path analysis is a method used to examine the direct and indirect relationships between variables. It is particularly useful when exploring complex relationships between variables.

Structural Equation Modeling (SEM)

SEM is a statistical method used to test and validate theoretical models of the relationships between variables. It is particularly useful when exploring complex models with multiple variables and relationships.

Factor Analysis

Factor analysis is used to identify underlying factors that contribute to the variation in a set of variables. This method is particularly useful when exploring relationships between multiple variables.

Content Analysis

Content analysis is used to analyze qualitative data by identifying themes and patterns in text, images, or other forms of data. This method is particularly useful when exploring the meaning and context of data.

Applications of Explanatory Research

The applications of explanatory research include:

• Social sciences: Explanatory research is commonly used in social sciences to investigate the causes and effects of social phenomena, such as the relationship between poverty and crime, or the impact of social policies on individuals or communities.
• Marketing : Explanatory research can be used in marketing to understand the reasons behind consumer behavior, such as why certain products are preferred over others or why customers choose to purchase from certain brands.
• Healthcare : Explanatory research can be used in healthcare to identify the factors that contribute to disease or illness, as well as the effectiveness of different treatments and interventions.
• Education : Explanatory research can be used in education to investigate the causes of academic achievement or failure, as well as the factors that influence teaching and learning processes.
• Business : Explanatory research can be used in business to understand the factors that contribute to the success or failure of different strategies, as well as the impact of external factors, such as economic or political changes, on business operations.
• Public policy: Explanatory research can be used in public policy to evaluate the effectiveness of policies and programs, as well as to identify the factors that contribute to social problems or inequalities.

Explanatory Research Question

An explanatory research question is a type of research question that seeks to explain the relationship between two or more variables, and to identify the underlying causes of that relationship. The goal of explanatory research is to test hypotheses or theories about the relationship between variables, and to gain a deeper understanding of complex phenomena.

Examples of explanatory research questions include:

• What is the relationship between sleep quality and academic performance among college students, and what factors contribute to this relationship?
• How do environmental factors, such as temperature and humidity, affect the spread of infectious diseases?
• What are the factors that contribute to the success or failure of small businesses in a particular industry, and how do these factors interact with each other?
• How do different teaching strategies impact student engagement and learning outcomes in the classroom?
• What is the relationship between social support and mental health outcomes among individuals with chronic illnesses, and how does this relationship vary across different populations?

Examples of Explanatory Research

Here are a few Real-Time Examples of explanatory research:

• Exploring the factors influencing customer loyalty: A business might conduct explanatory research to determine which factors, such as product quality, customer service, or price, have the greatest impact on customer loyalty. This research could involve collecting data through surveys, interviews, or other means and analyzing it using methods such as correlation or regression analysis.
• Understanding the causes of crime: Law enforcement agencies might conduct explanatory research to identify the factors that contribute to crime in a particular area. This research could involve collecting data on factors such as poverty, unemployment, drug use, and social inequality and analyzing it using methods such as regression analysis or structural equation modeling.
• Investigating the effectiveness of a new medical treatment: Medical researchers might conduct explanatory research to determine whether a new medical treatment is effective and which variables, such as dosage or patient age, are associated with its effectiveness. This research could involve conducting clinical trials and analyzing data using methods such as path analysis or SEM.
• Exploring the impact of social media on mental health : Researchers might conduct explanatory research to determine whether social media use has a positive or negative impact on mental health and which variables, such as frequency of use or type of social media, are associated with mental health outcomes. This research could involve collecting data through surveys or interviews and analyzing it using methods such as factor analysis or content analysis.

When to use Explanatory Research

Here are some situations where explanatory research might be appropriate:

• When exploring a new or complex phenomenon: Explanatory research can be used to understand the mechanisms of a new or complex phenomenon and to identify the variables that are most strongly associated with it.
• When testing a theoretical model: Explanatory research can be used to test a theoretical model of the relationships between variables and to validate or modify the model based on empirical data.
• When identifying the causal relationships between variables: Explanatory research can be used to identify the causal relationships between variables and to determine which variables have the greatest impact on the outcome of interest.
• When conducting program evaluation: Explanatory research can be used to evaluate the effectiveness of a program or intervention and to identify the factors that contribute to its success or failure.
• When making informed decisions: Explanatory research can be used to provide a basis for informed decision-making in business, government, or other contexts by identifying the factors that contribute to a particular outcome.

How to Conduct Explanatory Research

Here are the steps to conduct explanatory research:

• Identify the research problem: Clearly define the research question or problem you want to investigate. This should involve identifying the variables that you want to explore, and the potential relationships between them.
• Conduct a literature review: Review existing research on the topic to gain a deeper understanding of the variables and relationships you plan to explore. This can help you develop a hypothesis or research questions to guide your study.
• Develop a research design: Decide on the research design that best suits your study. This may involve collecting data through surveys, interviews, experiments, or observations.
• Collect and analyze data: Collect data from your selected sample and analyze it using appropriate statistical methods to identify any significant relationships between variables.
• Interpret findings: Interpret the results of your analysis in light of your research question or hypothesis. Identify any patterns or relationships between variables, and discuss the implications of your findings for the wider field of study.
• Draw conclusions: Draw conclusions based on your analysis and identify any areas for further research. Make recommendations for future research or policy based on your findings.

Purpose of Explanatory Research

The purpose of explanatory research is to identify and explain the relationships between different variables, as well as to determine the causes of those relationships. This type of research is often used to test hypotheses or theories, and to explore complex phenomena that are not well understood.

Explanatory research can help to answer questions such as “why” and “how” by providing a deeper understanding of the underlying causes and mechanisms of a particular phenomenon. For example, explanatory research can be used to determine the factors that contribute to a particular health condition, or to identify the reasons why certain marketing strategies are more effective than others.

The main purpose of explanatory research is to gain a deeper understanding of a particular phenomenon, with the goal of developing more effective solutions or interventions to address the problem. By identifying the underlying causes and mechanisms of a phenomenon, explanatory research can help to inform decision-making, policy development, and best practices in a wide range of fields, including healthcare, social sciences, business, and education

Advantages of Explanatory Research

Here are some advantages of explanatory research:

• Provides a deeper understanding: Explanatory research aims to uncover the underlying causes and mechanisms of a particular phenomenon, providing a deeper understanding of complex phenomena that is not possible with other research designs.
• Test hypotheses or theories: Explanatory research can be used to test hypotheses or theories by identifying the relationships between variables and determining the causes of those relationships.
• Provides insights for decision-making: Explanatory research can provide insights that can inform decision-making in a wide range of fields, from healthcare to business.
• Can lead to the development of effective solutions: By identifying the underlying causes of a problem, explanatory research can help to develop more effective solutions or interventions to address the problem.
• Can improve the validity of research: By identifying and controlling for potential confounding variables, explanatory research can improve the validity and reliability of research findings.
• Can be used in combination with other research designs : Explanatory research can be used in combination with other research designs, such as exploratory or descriptive research, to provide a more comprehensive understanding of a phenomenon.

Limitations of Explanatory Research

Here are some limitations of explanatory research:

• Limited generalizability: Explanatory research typically involves studying a specific sample, which can limit the generalizability of findings to other populations or settings.
• Time-consuming and resource-intensive: Explanatory research can be time-consuming and resource-intensive, particularly if it involves collecting and analyzing large amounts of data.
• Limited scope: Explanatory research is typically focused on a narrow research question or hypothesis, which can limit its scope in comparison to other research designs such as exploratory or descriptive research.
• Limited control over variables: Explanatory research can be limited by the researcher’s ability to control for all possible variables that may influence the relationship between variables of interest.
• Potential for bias: Explanatory research can be subject to various types of bias, such as selection bias, measurement bias, and recall bias, which can influence the validity of research findings.
• Ethical considerations: Explanatory research may involve the use of invasive or risky procedures, which can raise ethical concerns and require careful consideration of the potential risks and benefits of the study.

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1.4 Designed Experiments

Observational studies vs. experiments.

Ignoring anecdotal evidence, there are two primary types of data collection: observational studies and controlled (designed) experiments .  Remember, we typically cannot make claims of causality from observation studies because of the potential presence of confounding factors.  However, making causal conclusions based on experiments is often reasonable by controlling for those factors. Consider the following example:

Suppose you want to investigate the effectiveness of vitamin D in preventing disease. You recruit a group of subjects and ask them if they regularly take vitamin D. You notice that the subjects who take vitamin D exhibit better health on average than those who do not. Does this prove that vitamin D is effective in disease prevention? It does not. There are many differences between the two groups compared in addition to vitamin D consumption. People who take vitamin D regularly often take other steps to improve their health: exercise, diet, other vitamin supplements, choosing not to smoke. Any one of these factors could be influencing health. As described, this study does not necessarily prove that vitamin D is the key to disease prevention.

Experiments ultimately provide evidence to make decisions, so how could we narrow our focus and make claims of causality? In this section, you will learn important aspects of experimental design.

Designed Experiments

The purpose of an experiment is to investigate the relationship between two variables. When one variable causes change in another, we call the first variable the explanatory variable . The affected variable is called the response variable . In a randomized experiment, the researcher manipulates values of the explanatory variable and measures the resulting changes in the response variable. The different values of the explanatory variable may be called treatments . An experimental unit is a single object or individual to be measured. 

The main principles we want to follow in experimental design are:

Randomization

Replication.

In order to provide evidence that the explanatory variable is indeed causing the changes in the response variable, it is necessary to isolate the explanatory variable. The researcher must design their experiment in such a way that there is only one difference between groups being compared: the planned treatments. This is accomplished by randomization of experimental units to treatment groups. When subjects are assigned treatments randomly, all of the potential lurking variables are spread equally among the groups. At this point the only difference between groups is the one imposed by the researcher. Different outcomes measured in the response variable, therefore, must be a direct result of the different treatments. In this way, an experiment can show an apparent cause-and-effect connection between the explanatory and response variables.

Recall our previous example of investigating the effectiveness of vitamin D in preventing disease. Individuals in our trial could be randomly assigned, perhaps by flipping a coin, into one of two groups:  The control group (no treatment) and the second group receives extra doses of Vitamin D.

The more cases researchers observe, the more accurately they can estimate the effect of the explanatory variable on the response. In a single study, we replicate by collecting a sufficiently large sample. Additionally, a group of scientists may replicate an entire study to verify an earlier finding.  Having individuals experience a treatment more than once, called repeated measures is often helpful as well.

The power of suggestion can have an important influence on the outcome of an experiment. Studies have shown that the expectation of the study participant can be as important as the actual medication. In one study of performance-enhancing drugs, researchers noted:

Results showed that believing one had taken the substance resulted in [ performance ] times almost as fast as those associated with consuming the drug itself. In contrast, taking the drug without knowledge yielded no significant performance increment. [1]

It is often difficult to isolate the effects of the explanatory variable. To counter the power of suggestion, researchers set aside one treatment group as a control group . This group is given a placebo treatment–a treatment that cannot influence the response variable. The control group helps researchers balance the effects of being in an experiment with the effects of the active treatments. Of course, if you are participating in a study and you know that you are receiving a pill which contains no actual medication, then the power of suggestion is no longer a factor. Blinding in a randomized experiment preserves the power of suggestion. When a person involved in a research study is blinded, he does not know who is receiving the active treatment(s) and who is receiving the placebo treatment. A double-blind experiment is one in which both the subjects and the researchers involved with the subjects are blinded.

Randomized experiments are an essential tool in research. The US Food and Drug Administration typically requires that a new drug can only be marketed after two independently conducted randomized trials confirm its safety and efficacy; the European Medicines Agency has a similar policy. Large randomized experiments in medicine have provided the basis for major public health initiatives. In 1954 approximately 750,000 children participated in a randomized study comparing the polio vaccine with a placebo. In the United States, the results of the study quickly led to the widespread and successful use of the vaccine for polio prevention.

How does sleep deprivation affect your ability to drive? A recent study measured the effects on 19 professional drivers. Each driver participated in two experimental sessions: one after normal sleep and one after 27 hours of total sleep deprivation. The treatments were assigned in random order. In each session, performance was measured on a variety of tasks including a driving simulation.

The Smell & Taste Treatment and Research Foundation conducted a study to investigate whether smell can affect learning. Subjects completed mazes multiple times while wearing masks. They completed the pencil and paper mazes three times wearing floral-scented masks, and three times with unscented masks. Participants were assigned at random to wear the floral mask during the first three trials or during the last three trials. For each trial, researchers recorded the time it took to complete the maze and the subject’s impression of the mask’s scent: positive, negative, or neutral.

More Experimental Design

There are many different experimental designs from the most basic, a single treatment and control group, to some very complicated designs.  In an experimental design setting, when working with more than one variable, or treatment, they are often called factors , especially if it is categorical .  The values of factors are are often called levels .  When there are multiple factors, the combinations of each of the levels are called treatment combinations , or interactions.  Some basic ones you may see are:

• Completely randomized
• Block design
• Matched pairs design

Completely Randomized

While very important and an essential research tool, not much explanation is needed for this design.  It involves figuring out how many treatments will be administered and randomly assigning participants to their respective groups.

Block Design 

Researchers sometimes know or suspect that variables, other than the treatment, influence the response. Under these circumstances, they may first group individuals based on this variable into blocks and then randomize cases within each block to the treatment groups. This strategy is often referred to as blocking. For instance, if we are looking at the effect of a drug on heart attacks, we might first split patients in the study into low-risk and high-risk blocks, then randomly assign half the patients from each block to the control group and the other half to the treatment group, as shown in the figure below. This strategy ensures each treatment group has an equal number of low-risk and high-risk patients.

Matched Pairs

A matched pairs design is one where we have very similar individuals (or even the same individual) receiving different two treatments (or treatment vs. control), then comparing their results.  This design is very powerful, however, it can be hard to find many like individuals to match up.  Some common ways of creating a matched pairs design are twin studies, before and after measurements,  pre and post test situations, or crossover studies.  Consider the following example:

In the 2000 Olympics, was the use of a new wetsuit design responsible for an observed increase in swim velocities? In a matched pairs study designed to investigate this question, twelve competitive swimmers swam 1500 meters at maximal speed, once wearing a wetsuit and once wearing a regular swimsuit. The order of wetsuit versus swimsuit was randomized for each of the 12 swimmers. Figure 1.6 shows the average velocity recorded for each swimmer, measured in meters per second (m/s).

Notice in this data, two sets of observations are uniquely paired so that an observation in one set matches an observation in the other; in this case, each swimmer has two measured velocities, one with a wetsuit and one with a swimsuit. A natural measure of the effect of the wetsuit on swim velocity is the difference between the measured maximum velocities (velocity.diff = wet.suit.velocity- swim.suit.velocity).  Even though there are two measurements per individual, using the difference in observations as the variable of interest allows for the problem to be analyzed.

A new windshield treatment claims to repel water more effectively. Ten windshields are tested by simulating rain without the new treatment. The same windshields are then treated, and the experiment is run again.  What experiment design is being implemented here?

A new medicine is said to help improve sleep. Eight subjects are picked at random and given the medicine. The means hours slept for each person were recorded before starting the medication and after. What experiment design is being implemented here?

Image References

Figure 1.5: Kindred Grey (2020). “Block Design.” CC BY-SA 4.0. Retrieved from https://commons.wikimedia.org/wiki/File:Block_Design.png

• McClung, M. Collins, D. “Because I know it will!”: placebo effects of an ergogenic aid on athletic performance. Journal of Sport & Exercise Psychology. 2007 Jun. 29(3):382-94. Web. April 30, 2013. ↵

Data collection where no variables are manipulated

Type of experiment where variables are manipulated; data is collected in a controlled setting

The independent variable in an experiment; the value controlled by researchers

The dependent variable in an experiment; the value that is measured for change at the end of an experiment

Different values or components of the explanatory variable applied in an experiment

Any individual or object to be measured

When an individual goes through a single treatment more than once

A group in a randomized experiment that receives no (or an inactive) treatment but is otherwise managed exactly as the other groups

An inactive treatment that has no real effect on the explanatory variable

Not telling participants which treatment they are receiving

The act of blinding both the subjects of an experiment and the researchers who work with the subjects

Variables in an experiment

Certain values of variables in an experiment

Combinations of levels of variables in an experiment

Dividing participants into treatment groups randomly

Grouping individuals based on a variable into "blocks" and then randomizing cases within each block to the treatment groups

Very similar individuals (or even the same individual) receive two different two treatments (or treatment vs. control) then the difference in results are compared

Significant Statistics Copyright © 2020 by John Morgan Russell, OpenStaxCollege, OpenIntro is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License , except where otherwise noted.

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Module 1: Sampling and Data

Experimental design and ethics.

Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments. In this module, you will learn important aspects of experimental design. Proper study design ensures the production of reliable, accurate data.

The purpose of an experiment is to investigate the relationship between two variables. When one variable causes change in another, we call the first variable the explanatory variable . The affected variable is called the response variable . In a randomized experiment, the researcher manipulates values of the explanatory variable and measures the resulting changes in the response variable. The different values of the explanatory variable are called treatments . An experimental unit is a single object or individual to be measured.

The following video explains the difference between collecting data from observations and collecting data from experiments.

You want to investigate the effectiveness of vitamin E in preventing disease. You recruit a group of subjects and ask them if they regularly take vitamin E. You notice that the subjects who take vitamin E exhibit better health on average than those who do not. Does this prove that vitamin E is effective in disease prevention? It does not. There are many differences between the two groups compared in addition to vitamin E consumption. People who take vitamin E regularly often take other steps to improve their health: exercise, diet, other vitamin supplements, choosing not to smoke. Any one of these factors could be influencing health. As described, this study does not prove that vitamin E is the key to disease prevention.

Additional variables that can cloud a study are called lurking variables . In order to prove that the explanatory variable is causing a change in the response variable, it is necessary to isolate the explanatory variable. The researcher must design her experiment in such a way that there is only one difference between groups being compared: the planned treatments. This is accomplished by the random assignment of experimental units to treatment groups. When subjects are assigned treatments randomly, all of the potential lurking variables are spread equally among the groups. At this point the only difference between groups is the one imposed by the researcher. Different outcomes measured in the response variable, therefore, must be a direct result of the different treatments. In this way, an experiment can prove a cause-and-effect connection between the explanatory and response variables.

The power of suggestion can have an important influence on the outcome of an experiment. Studies have shown that the expectation of the study participant can be as important as the actual medication. In one study of performance-enhancing drugs, researchers noted:

Results showed that believing one had taken the substance resulted in [ performance ] times almost as fast as those associated with consuming the drug itself. In contrast, taking the drug without knowledge yielded no significant performance increment. 1

When participation in a study prompts a physical response from a participant, it is difficult to isolate the effects of the explanatory variable. To counter the power of suggestion, researchers set aside one treatment group as a control group . This group is given a placebo treatment–a treatment that cannot influence the response variable. The control group helps researchers balance the effects of being in an experiment with the effects of the active treatments. Of course, if you are participating in a study and you know that you are receiving a pill which contains no actual medication, then the power of suggestion is no longer a factor. Blinding in a randomized experiment preserves the power of suggestion. When a person involved in a research study is blinded, he does not know who is receiving the active treatment(s) and who is receiving the placebo treatment. A double-blind experiment is one in which both the subjects and the researchers involved with the subjects are blinded.

Researchers want to investigate whether taking aspirin regularly reduces the risk of heart attack. Four hundred men between the ages of 50 and 84 are recruited as participants. The men are divided randomly into two groups: one group will take aspirin, and the other group will take a placebo. Each man takes one pill each day for three years, but he does not know whether he is taking aspirin or the placebo. At the end of the study, researchers count the number of men in each group who have had heart attacks.

Identify the following values for this study: population, sample, experimental units, explanatory variable, response variable, treatments.

The Smell & Taste Treatment and Research Foundation conducted a study to investigate whether smell can affect learning. Subjects completed mazes multiple times while wearing masks. They completed the pencil and paper mazes three times wearing floral-scented masks, and three times with unscented masks. Participants were assigned at random to wear the floral mask during the first three trials or during the last three trials. For each trial, researchers recorded the time it took to complete the maze and the subject’s impression of the mask’s scent: positive, negative, or neutral.

• Describe the explanatory and response variables in this study.
• What are the treatments?
• Identify any lurking variables that could interfere with this study.
• Is it possible to use blinding in this study?
• The explanatory variable is scent, and the response variable is the time it takes to complete the maze.
• There are two treatments: a floral-scented mask and an unscented mask.
• All subjects experienced both treatments. The order of treatments was randomly assigned so there were no differences between the treatment groups. Random assignment eliminates the problem of lurking variables.
• Subjects will clearly know whether they can smell flowers or not, so subjects cannot be blinded in this study. Researchers timing the mazes can be blinded, though. The researcher who is observing a subject will not know which mask is being worn.

A researcher wants to study the effects of birth order on personality. Explain why this study could not be conducted as a randomized experiment. What is the main problem in a study that cannot be designed as a randomized experiment?

You are concerned about the effects of texting on driving performance. Design a study to test the response time of drivers while texting and while driving only. How many seconds does it take for a driver to respond when a leading car hits the brakes?

• Describe the explanatory and response variables in the study.
• What should you consider when selecting participants?
• Your research partner wants to divide participants randomly into two groups: one to drive without distraction and one to text and drive simultaneously. Is this a good idea? Why or why not?
• How can blinding be used in this study?

The widespread misuse and misrepresentation of statistical information often gives the field a bad name. Some say that “numbers don’t lie,” but the people who use numbers to support their claims often do.

A recent investigation of famous social psychologist, Diederik Stapel, has led to the retraction of his articles from some of the world’s top journals including Journal of Experimental Social Psychology, Social Psychology, Basic and Applied Social Psychology, British Journal of Social Psychology, and the magazine Science . Diederik Stapel is a former professor at Tilburg University in the Netherlands. Over the past two years, an extensive investigation involving three universities where Stapel has worked concluded that the psychologist is guilty of fraud on a colossal scale. Falsified data taints over [latex]55[/latex] papers he authored and [latex]10[/latex] Ph.D. dissertations that he supervised.

Stapel did not deny that his deceit was driven by ambition. But it was more complicated than that, he told me. He insisted that he loved social psychology but had been frustrated by the messiness of experimental data, which rarely led to clear conclusions. His lifelong obsession with elegance and order, he said, led him to concoct sexy results that journals found attractive. “It was a quest for aesthetics, for beauty—instead of the truth,” he said. He described his behavior as an addiction that drove him to carry out acts of increasingly daring fraud, like a junkie seeking a bigger and better high. 2

The committee investigating Stapel concluded that he is guilty of several practices including:

• creating datasets, which largely confirmed the prior expectations,
• altering data in existing datasets,
• changing measuring instruments without reporting the change, and
• misrepresenting the number of experimental subjects.

Clearly, it is never acceptable to falsify data the way this researcher did. Sometimes, however, violations of ethics are not as easy to spot.

Researchers have a responsibility to verify that proper methods are being followed. The report describing the investigation of Stapel’s fraud states that, “statistical flaws frequently revealed a lack of familiarity with elementary statistics.” 3 Many of Stapel’s co-authors should have spotted irregularities in his data. Unfortunately, they did not know very much about statistical analysis, and they simply trusted that he was collecting and reporting data properly.

Many types of statistical fraud are difficult to spot. Some researchers simply stop collecting data once they have just enough to prove what they had hoped to prove. They don’t want to take the chance that a more extensive study would complicate their lives by producing data contradicting their hypothesis.

Professional organizations, like the American Statistical Association, clearly define expectations for researchers. There are even laws in the federal code about the use of research data.

When a statistical study uses human participants, as in medical studies, both ethics and the law dictate that researchers should be mindful of the safety of their research subjects. The U.S. Department of Health and Human Services oversees federal regulations of research studies with the aim of protecting participants. When a university or other research institution engages in research, it must ensure the safety of all human subjects. For this reason, research institutions establish oversight committees known as Institutional Review Boards (IRB) . All planned studies must be approved in advance by the IRB. Key protections that are mandated by law include the following:

• Risks to participants must be minimized and reasonable with respect to projected benefits.
• Participants must give informed consent . This means that the risks of participation must be clearly explained to the subjects of the study. Subjects must consent in writing, and researchers are required to keep documentation of their consent.
• Data collected from individuals must be guarded carefully to protect their privacy.

These ideas may seem fundamental, but they can be very difficult to verify in practice. Is removing a participant’s name from the data record sufficient to protect privacy? Perhaps the person’s identity could be discovered from the data that remains. What happens if the study does not proceed as planned and risks arise that were not anticipated? When is informed consent really necessary? Suppose your doctor wants a blood sample to check your cholesterol level. Once the sample has been tested, you expect the lab to dispose of the remaining blood. At that point the blood becomes biological waste. Does a researcher have the right to take it for use in a study?

It is important that students of statistics take time to consider the ethical questions that arise in statistical studies. How prevalent is fraud in statistical studies? You might be surprised—and disappointed. There is a website dedicated to cataloging retractions of study articles that have been proven fraudulent. A quick glance will show that the misuse of statistics is a bigger problem than most people realize.

Vigilance against fraud requires knowledge. Learning the basic theory of statistics will empower you to analyze statistical studies critically.

Describe the unethical behavior in each example and describe how it could impact the reliability of the resulting data. Explain how the problem should be corrected. A researcher is collecting data in a community.

• She selects a block where she is comfortable walking because she knows many of the people living on the street.
• No one seems to be home at four houses on her route. She does not record the addresses and does not return at a later time to try to find residents at home.
• She skips four houses on her route because she is running late for an appointment. When she gets home, she fills in the forms by selecting random answers from other residents in the neighborhood.
• By selecting a convenient sample, the researcher is intentionally selecting a sample that could be biased. Claiming that this sample represents the community is misleading. The researcher needs to select areas in the community at random.
• Intentionally omitting relevant data will create bias in the sample. Suppose the researcher is gathering information about jobs and child care. By ignoring people who are not home, she may be missing data from working families that are relevant to her study. She needs to make every effort to interview all members of the target sample.
• It is never acceptable to fake data. Even though the responses she uses are “real” responses provided by other participants, the duplication is fraudulent and can create bias in the data. She needs to work diligently to interview everyone on her route.

Describe the unethical behavior, if any, in each example and describe how it could impact the reliability of the resulting data. Explain how the problem should be corrected. A study is commissioned to determine the favorite brand of fruit juice among teens in California.

• The survey is commissioned by the seller of a popular brand of apple juice.
• There are only two types of juice included in the study: apple juice and cranberry juice.
• Researchers allow participants to see the brand of juice as samples are poured for a taste test.
• Twenty-five percent of participants prefer Brand X, [latex]33[/latex]% prefer Brand Y and [latex]42[/latex]% have no preference between the two brands. Brand X references the study in a commercial saying “Most teens like Brand X as much as or more than Brand Y.”

“Vitamin E and Health,” Nutrition Source, Harvard School of Public Health, http://www.hsph.harvard.edu/nutritionsource/vitamin-e/ (accessed May 1, 2013).

Stan Reents. “Don’t Underestimate the Power of Suggestion,” athleteinme.com, http://www.athleteinme.com/ArticleView.aspx?id=1053 (accessed May 1, 2013).

Ankita Mehta. “Daily Dose of Aspiring Helps Reduce Heart Attacks: Study,” International Business Times, July 21, 2011. Also available online at http://www.ibtimes.com/daily-dose-aspirin-helps-reduce-heart-attacks-study-300443 (accessed May 1, 2013).

The Data and Story Library, http://lib.stat.cmu.edu/DASL/Stories/ScentsandLearning.html (accessed May 1, 2013).

M.L. Jacskon et al., “Cognitive Components of Simulated Driving Performance: Sleep Loss effect and Predictors,” Accident Analysis and Prevention Journal, Jan no. 50 (2013), http://www.ncbi.nlm.nih.gov/pubmed/22721550 (accessed May 1, 2013).

“Earthquake Information by Year,” U.S. Geological Survey. http://earthquake.usgs.gov/earthquakes/eqarchives/year/ (accessed May 1, 2013).

“Fatality Analysis Report Systems (FARS) Encyclopedia,” National Highway Traffic and Safety Administration. http://www-fars.nhtsa.dot.gov/Main/index.aspx (accessed May 1, 2013).

Data from www.businessweek.com (accessed May 1, 2013).

Data from www.forbes.com (accessed May 1, 2013).

“America’s Best Small Companies,” http://www.forbes.com/best-small-companies/list/ (accessed May 1, 2013).

U.S. Department of Health and Human Services, Code of Federal Regulations Title 45 Public Welfare Department of Health and Human Services Part 46 Protection of Human Subjects revised January 15, 2009. Section 46.111:Criteria for IRB Approval of Research.

“April 2013 Air Travel Consumer Report,” U.S. Department of Transportation, April 11 (2013), http://www.dot.gov/airconsumer/april-2013-air-travel-consumer-report (accessed May 1, 2013).

Lori Alden, “Statistics can be Misleading,” econoclass.com, http://www.econoclass.com/misleadingstats.html (accessed May 1, 2013).

Maria de los A. Medina, “Ethics in Statistics,” Based on “Building an Ethics Module for Business, Science, and Engineering Students” by Jose A. Cruz-Cruz and William Frey, Connexions, http://cnx.org/content/m15555/latest/ (accessed May 1, 2013).

Concept Review

A poorly designed study will not produce reliable data. There are certain key components that must be included in every experiment. To eliminate lurking variables, subjects must be assigned randomly to different treatment groups. One of the groups must act as a control group, demonstrating what happens when the active treatment is not applied. Participants in the control group receive a placebo treatment that looks exactly like the active treatments but cannot influence the response variable. To preserve the integrity of the placebo, both researchers and subjects may be blinded. When a study is designed properly, the only difference between treatment groups is the one imposed by the researcher. Therefore, when groups respond differently to different treatments, the difference must be due to the influence of the explanatory variable.

“An ethics problem arises when you are considering an action that benefits you or some cause you support, hurts or reduces benefits to others, and violates some rule.” 4 Ethical violations in statistics are not always easy to spot. Professional associations and federal agencies post guidelines for proper conduct. It is important that you learn basic statistical procedures so that you can recognize proper data analysis.

• Experimental Design and Ethics . Provided by : OpenStax. Located at : http://cnx.org/contents/[email protected]. . License : CC BY: Attribution
• Introductory Statistics . Authored by : Barbara Illowski, Susan Dean. Provided by : Open Stax. Located at : http://cnx.org/contents/[email protected] . License : CC BY: Attribution . License Terms : Download for free at http://cnx.org/contents/[email protected]

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• Explanatory Research | Definition, Guide, & Examples

Explanatory Research | Definition, Guide & Examples

Published on 7 May 2022 by Tegan George and Julia Merkus. Revised on 20 January 2023.

Explanatory research is a research method that explores why something occurs when limited information is available. It can help you increase your understanding of a given topic, ascertain how or why a particular phenomenon is occurring, and predict future occurrences.

Explanatory research can also be explained as a ’cause and effect’ model, investigating patterns and trends in existing data that haven’t been previously investigated. For this reason, it is often considered a type of causal research .

Table of contents

When to use explanatory research, explanatory research questions, explanatory research data collection, explanatory research data analysis, step-by-step example of explanatory research, explanatory vs exploratory research, advantages and disadvantages of exploratory research, frequently asked questions about explanatory research.

Explanatory research is used to investigate how or why a phenomenon takes place. Therefore, this type of research is often one of the first stages in the research process, serving as a jumping-off point for future research. While there is often data available about your topic, it’s possible the particular causal relationship you are interested in has not been robustly studied.

Explanatory research helps you analyse these patterns, formulating hypotheses that can guide future endeavors. If you are seeking a more complete understanding of a relationship between variables, explanatory research is a great place to start. However, keep in mind that it will likely not yield conclusive results.

You analysed their final grades and noticed that the students who take your course in the first semester always obtain higher grades than students who take the same course in the second semester.

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Explanatory research answers ‘why’ and ‘what’ questions, leading to an improved understanding of a previously unresolved problem or providing clarity for related future research initiatives.

Here are a few examples:

• Why do undergraduate students obtain higher average grades in the first semester than in the second semester?
• How does marital status affect labour market participation?
• Why do multilingual individuals show more risky behaviour during business negotiations than monolingual individuals?
• How does a child’s ability to delay immediate gratification predict success later in life?
• Why are teenagers more likely to litter in a highly littered area than in a clean area?

After choosing your research question, there is a variety of options for research and data collection methods to choose from.

A few of the most common research methods include:

• Literature reviews
• Interviews and focus groups
• Pilot studies
• Observations
• Experiments

The method you choose depends on several factors, including your timeline, your budget, and the structure of your question.

If there is already a body of research on your topic, a literature review is a great place to start. If you are interested in opinions and behaviour, consider an interview or focus group format. If you have more time or funding available, an experiment or pilot study may be a good fit for you.

In order to ensure you are conducting your explanatory research correctly, be sure your analysis is definitively causal in nature, and not just correlated.

Always remember the phrase ‘correlation doesn’t imply causation’. Correlated variables are merely associated with one another: when one variable changes, so does the other. However, this isn’t necessarily due to a direct or indirect causal link.

Causation means that changes in the independent variable bring about changes in the dependent variable. In other words, there is a direct cause-and-effect relationship between variables.

Causal evidence must meet three criteria:

• Temporal : What you define as the ’cause’ must precede what you define as the ‘effect’.
• Variation : Intervention must be systematic between your independent variable and dependent variable.
• Non-spurious : Be careful that there are no mitigating factors or hidden third variables that confound your results.

Correlation doesn’t imply causation, but causation always implies correlation. In order to get conclusive causal results, you’ll need to conduct a full experimental design .

Your explanatory research design depends on the research method you choose to collect your data . In most cases, you’ll use an experiment to investigate potential causal relationships. We’ll walk you through the steps using an example.

Step 1: Develop the research question

The first step in conducting explanatory research is getting familiar with the topic you’re interested in, so that you can develop a research question .

Let’s say you’re interested in language retention rates in adults.

You are interested in finding out how the duration of exposure to language influences language retention ability later in life.

Step 2: Formulate a hypothesis

The next step is to address your expectations. In some cases, there is literature available on your subject or on a closely related topic that you can use as a foundation for your hypothesis . In other cases, the topic isn’t well studied, and you’ll have to develop your hypothesis based on your instincts or on existing literature on more distant topics.

• H 0 : The duration of exposure to a language in infancy does not influence language retention in adults who were adopted from abroad as children.
• H 1 : The duration of exposure to a language in infancy has a positive effect on language retention in adults who were adopted from abroad as children.

Step 3: Design your methodology and collect your data

Next, decide what data collection and data analysis methods you will use and write them up. After carefully designing your research, you can begin to collect your data.

• Adults who were adopted from Colombia between 0 and 6 months of age
• Adults who were adopted from Colombia between 6 and 12 months of age
• Adults who were adopted from Colombia between 12 and 18 months of age
• Monolingual adults who have not been exposed to a different language

During the study, you test their Spanish language proficiency twice in a research design that has three stages:

• Pretest : You conduct several language proficiency tests to establish any differences between groups pre-intervention.
• Intervention : You provide all groups with 8 hours of Spanish class.
• Posttest : You again conduct several language proficiency tests to establish any differences between groups post-intervention.

You made sure to control for any confounding variables , such as age, gender, and proficiency in other languages.

Step 4: Analyse your data and report results

After data collection is complete, proceed to analyse your data and report the results.

• The pre-exposed adults showed higher language proficiency in Spanish than those who had not been pre-exposed. The difference is even greater for the posttest.
• The adults who were adopted between 12 and 18 months of age had a higher Spanish language proficiency level than those who were adopted between 0 and 6 months or 6 and 12 months of age, but there was no difference found between the latter two groups.

To determine whether these differences are significant, you conduct a mixed ANOVA. The ANOVA shows that all differences are not significant for the pretest, but they are significant for the posttest.

Step 5: Interpret your results and provide suggestions for future research

As you interpret the results, try to come up with explanations for the results that you did not expect. In most cases, you want to provide suggestions for future research.

However, this difference is only significant after the intervention (the Spanish class).

You decide it’s worth it to further research the matter, and propose a few additional research ideas:

• Replicate the study with a larger sample
• Replicate the study for other maternal languages (e.g., Korean, Lingala, Arabic)
• Replicate the study for other language aspects, such as nativeness of the accent

It can be easy to confuse explanatory research with exploratory research. If you’re in doubt about the relationship between exploratory and explanatory research, just remember that exploratory research lays the groundwork for later explanatory research.

Exploratory research questions often begin with ‘what’. They are designed to guide future research and do not usually have conclusive results. Exploratory research is often utilised as a first step in your research process, to help you focus your research question and fine-tune your hypotheses.

Explanatory research questions often start with ‘why’ or ‘how’. They help you study why and how a previously studied phenomenon takes place.

Like any other research design , exploratory research has its trade-offs: while it provides a unique set of benefits, it also has significant downsides:

• It gives more meaning to previous research. It helps fill in the gaps in existing analyses and provides information on the reasons behind phenomena.
• It is very flexible and often replicable, since the internal validity tends to be high when done correctly.
• As you can often use secondary research, explanatory research is often very cost- and time-effective, allowing you to utilise pre-existing resources to guide your research before committing to heavier analyses.

Disadvantages

• While explanatory research does help you solidify your theories and hypotheses, it usually lacks conclusive results.
• Results can be biased or inadmissible to a larger body of work and are not generally externally valid . You will likely have to conduct more robust (often quantitative ) research later to bolster any possible findings gleaned from explanatory research.
• Coincidences can be mistaken for causal relationships , and it can sometimes be challenging to ascertain which is the causal variable and which is the effect.

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

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

Exploratory research explores the main aspects of a new or barely researched question.

Explanatory research explains the causes and effects of an already widely researched question.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

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Observational vs. Experimental Study: A Comprehensive Guide

Explore the fundamental disparities between experimental and observational studies in this comprehensive guide by Santos Research Center, Corp. Uncover concepts such as control group, random sample, cohort studies, response variable, and explanatory variable that shape the foundation of these methodologies. Discover the significance of randomized controlled trials and case control studies, examining causal relationships and the role of dependent variables and independent variables in research designs.

This enlightening exploration also delves into the meticulous scientific study process, involving survey members, systematic reviews, and statistical analyses. Investigate the careful balance of control group and treatment group dynamics, highlighting how researchers meticulously assign variables and analyze statistical patterns to discern meaningful insights. From dissecting issues like lung cancer to understanding sleep patterns, this guide emphasizes the precision of controlled experiments and controlled trials, where variables are isolated and scrutinized, paving the way for a deeper comprehension of the world through empirical research.

Introduction to Observational and Experimental Studies

These two studies are the cornerstones of scientific inquiry, each offering a distinct approach to unraveling the mysteries of the natural world.

Observational studies allow us to observe, document, and gather data without direct intervention. They provide a means to explore real-world scenarios and trends, making them valuable when manipulating variables is not feasible or ethical. From surveys to meticulous observations, these studies shed light on existing conditions and relationships.

Experimental studies , in contrast, put researchers in the driver's seat. They involve the deliberate manipulation of variables to understand their impact on specific outcomes. By controlling the conditions, experimental studies establish causal relationships, answering questions of causality with precision. This approach is pivotal for hypothesis testing and informed decision-making.

At Santos Research Center, Corp., we recognize the importance of both observational and experimental studies. We employ these methodologies in our diverse research projects to ensure the highest quality of scientific investigation and to answer a wide range of research questions.

Observational Studies: A Closer Look

In our exploration of research methodologies, let's zoom in on observational research studies—an essential facet of scientific inquiry that we at Santos Research Center, Corp., expertly employ in our diverse research projects.

What is an Observational Study?

Observational research studies involve the passive observation of subjects without any intervention or manipulation by researchers. These studies are designed to scrutinize the relationships between variables and test subjects, uncover patterns, and draw conclusions grounded in real-world data.

Researchers refrain from interfering with the natural course of events in controlled experiment. Instead, they meticulously gather data by keenly observing and documenting information about the test subjects and their surroundings. This approach permits the examination of variables that cannot be ethically or feasibly manipulated, making it particularly valuable in certain research scenarios.

Types of Observational Studies

Now, let's delve into the various forms that observational studies can take, each with its distinct characteristics and applications.

Cohort Studies:  A cohort study is a type of observational study that entails tracking one group of individuals over an extended period. Its primary goal is to identify potential causes or risk factors for specific outcomes or treatment group. Cohort studies provide valuable insights into the development of conditions or diseases and the factors that influence them.

Case-Control Studies:  Case-control studies, on the other hand, involve the comparison of individuals with a particular condition or outcome to those without it (the control group). These studies aim to discern potential causal factors or associations that may have contributed to the development of the condition under investigation.

Cross-Sectional Studies:  Cross-sectional studies take a snapshot of a diverse group of individuals at a single point in time. By collecting data from this snapshot, researchers gain insights into the prevalence of a specific condition or the relationships between variables at that precise moment. Cross-sectional studies are often used to assess the health status of the different groups within a population or explore the interplay between various factors.

Advantages and Limitations of Observational Studies

Observational studies, as we've explored, are a vital pillar of scientific research, offering unique insights into real-world phenomena. In this section, we will dissect the advantages and limitations that characterize these studies, shedding light on the intricacies that researchers grapple with when employing this methodology.

Advantages: One of the paramount advantages of observational studies lies in their utilization of real-world data. Unlike controlled experiments that operate in artificial settings, observational studies embrace the complexities of the natural world. This approach enables researchers to capture genuine behaviors, patterns, and occurrences as they unfold. As a result, the data collected reflects the intricacies of real-life scenarios, making it highly relevant and applicable to diverse settings and populations.

Moreover, in a randomized controlled trial, researchers looked to randomly assign participants to a group. Observational studies excel in their capacity to examine long-term trends. By observing one group of subjects over extended periods, research scientists gain the ability to track developments, trends, and shifts in behavior or outcomes. This longitudinal perspective is invaluable when studying phenomena that evolve gradually, such as chronic diseases, societal changes, or environmental shifts. It allows for the detection of subtle nuances that may be missed in shorter-term investigations.

Limitations: However, like any research methodology, observational studies are not without their limitations. One significant challenge of statistical study lies in the potential for biases. Since researchers do not intervene in the subjects' experiences, various biases can creep into the data collection process. These biases may arise from participant self-reporting, observer bias, or selection bias in random sample, among others. Careful design and rigorous data analysis are crucial for mitigating these biases.

Another limitation is the presence of confounding variables. In observational studies, it can be challenging to isolate the effect of a specific variable from the myriad of other factors at play. These confounding variables can obscure the true relationship between the variables of interest, making it difficult to establish causation definitively. Research scientists must employ statistical techniques to control for or adjust these confounding variables.

Additionally, observational studies face constraints in their ability to establish causation. While they can identify associations and correlations between variables, they cannot prove causality or causal relationship. Establishing causation typically requires controlled experiments where researchers can manipulate independent variables systematically. In observational studies, researchers can only infer potential causation based on the observed associations.

Experimental Studies: Delving Deeper

In the intricate landscape of scientific research, we now turn our gaze toward experimental studies—a dynamic and powerful method that Santos Research Center, Corp. skillfully employs in our pursuit of knowledge.

What is an Experimental Study?

While some studies observe and gather data passively, experimental studies take a more proactive approach. Here, researchers actively introduce an intervention or treatment to an experiment group study its effects on one or more variables. This methodology empowers researchers to manipulate independent variables deliberately and examine their direct impact on dependent variables.

Experimental research are distinguished by their exceptional ability to establish cause-and-effect relationships. This invaluable characteristic allows researchers to unlock the mysteries of how one variable influences another, offering profound insights into the scientific questions at hand. Within the controlled environment of an experimental study, researchers can systematically test hypotheses, shedding light on complex phenomena.

Key Features of Experimental Studies

Central to statistical analysis, the rigor and reliability of experimental studies are several key features that ensure the validity of their findings.

Randomized Controlled Trials:  Randomization is a critical element in experimental studies, as it ensures that subjects are assigned to groups in a random assignment. This randomly assigned allocation minimizes the risk of unintentional biases and confounding variables, strengthening the credibility of the study's outcomes.

Control Groups:  Control groups play a pivotal role in experimental studies by serving as a baseline for comparison. They enable researchers to assess the true impact of the intervention being studied. By comparing the outcomes of the intervention group to those of survey members of the control group, researchers can discern whether the intervention caused the observed changes.

Blinding:  Both single-blind and double-blind techniques are employed in experimental studies to prevent biases from influencing the study or controlled trial's outcomes. Single-blind studies keep either the subjects or the researchers unaware of certain aspects of the study, while double-blind studies extend this blindness to both parties, enhancing the objectivity of the study.

These key features work in concert to uphold the integrity and trustworthiness of the results generated through experimental studies.

Advantages and Limitations of Experimental Studies

As with any research methodology, this one comes with its unique set of advantages and limitations.

Advantages:  These studies offer the distinct advantage of establishing causal relationships between two or more variables together. The controlled environment allows researchers to exert authority over variables, ensuring that changes in the dependent variable can be attributed to the independent variable. This meticulous control results in high-quality, reliable data that can significantly contribute to scientific knowledge.

Limitations:  However, experimental ones are not without their challenges. They may raise ethical concerns, particularly when the interventions involve potential risks to subjects. Additionally, their controlled nature can limit their real-world applicability, as the conditions in experiments may not accurately mirror those in the natural world. Moreover, executing an experimental study in randomized controlled, often demands substantial resources, with other variables including time, funding, and personnel.

Observational vs Experimental: A Side-by-Side Comparison

Having previously examined observational and experimental studies individually, we now embark on a side-by-side comparison to illuminate the key distinctions and commonalities between these foundational research approaches.

Key Differences and Notable Similarities

Methodologies

• Observational Studies : Characterized by passive observation, where researchers collect data without direct intervention, allowing the natural course of events to unfold.
• Experimental Studies : Involve active intervention, where researchers deliberately manipulate variables to discern their impact on specific outcomes, ensuring control over the experimental conditions.
• Observational Studies : Designed to identify patterns, correlations, and associations within existing data, shedding light on relationships within real-world settings.
• Experimental Studies : Geared toward establishing causality by determining the cause-and-effect relationships between variables, often in controlled laboratory environments.
• Observational Studies : Yield real-world data, reflecting the complexities and nuances of natural phenomena.
• Experimental Studies : Generate controlled data, allowing for precise analysis and the establishment of clear causal connections.

Observational studies excel at exploring associations and uncovering patterns within the intricacies of real-world settings, while experimental studies shine as the gold standard for discerning cause-and-effect relationships through meticulous control and manipulation in controlled environments. Understanding these differences and similarities empowers researchers to choose the most appropriate method for their specific research objectives.

When to Use Which: Practical Applications

The decision to employ either observational or experimental studies hinges on the research objectives at hand and the available resources. Observational studies prove invaluable when variable manipulation is impractical or ethically challenging, making them ideal for delving into long-term trends and uncovering intricate associations between certain variables (response variable or explanatory variable). On the other hand, experimental studies emerge as indispensable tools when the aim is to definitively establish causation and methodically control variables.

At Santos Research Center, Corp., our approach to both scientific study and methodology is characterized by meticulous consideration of the specific research goals. We recognize that the quality of outcomes hinges on selecting the most appropriate method of research study. Our unwavering commitment to employing both observational and experimental research studies further underscores our dedication to advancing scientific knowledge across diverse domains.

Conclusion: The Synergy of Experimental and Observational Studies in Research

In conclusion, both observational and experimental studies are integral to scientific research, offering complementary approaches with unique strengths and limitations. At Santos Research Center, Corp., we leverage these methodologies to contribute meaningfully to the scientific community.

Explore our projects and initiatives at Santos Research Center, Corp. by visiting our website or contacting us at (813) 249-9100, where our unwavering commitment to rigorous research practices and advancing scientific knowledge awaits.

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Chapter 1: Sampling and Data

1.4 Experimental Design and Ethics

Learning objectives.

By the end of this section, the student should be able to:

• Classify variables used for conducting an experiment.
• Identify the ethical concerns that arise when conducting a study.

Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments. In this module, you will learn important aspects of experimental design. Proper study design ensures the production of reliable, accurate data.

The purpose of an experiment is to investigate the relationship between two variables. When one variable causes change in another, we call the first variable the explanatory variable . The affected variable is called the response variable . In a randomized experiment, the researcher manipulates values of the explanatory variable and measures the resulting changes in the response variable. The different values of the explanatory variable are called treatments . An experimental unit is a single object or individual to be measured.

You want to investigate the effectiveness of vitamin E in preventing disease. You recruit a group of subjects and ask them if they regularly take vitamin E. You notice that the subjects who take vitamin E exhibit better health on average than those who do not. Does this prove that vitamin E is effective in disease prevention? It does not. There are many differences between the two groups compared to vitamin E consumption. People who take vitamin E regularly often take other steps to improve their health: exercise, diet, other vitamin supplements, choosing not to smoke. Any one of these factors could be influencing health. As described, this study does not prove that vitamin E is the key to disease prevention.

Additional variables that can cloud a study are called lurking variables . In order to prove that the explanatory variable is causing a change in the response variable, it is necessary to isolate the explanatory variable. The researcher must design her experiment in such a way that there is only one difference between groups being compared: the planned treatments. This is accomplished by the random assignment of experimental units to treatment groups. When subjects are assigned treatments randomly, all of the potential lurking variables are spread equally among the groups. At this point the only difference between groups is the one imposed by the researcher. Different outcomes measured in the response variable, therefore, must be a direct result of the different treatments. In this way, an experiment can prove a cause-and-effect connection between the explanatory and response variables.

The power of suggestion can have an important influence on the outcome of an experiment. Studies have shown that the expectation of the study participant can be as important as the actual medication. In one study of performance-enhancing drugs, researchers noted:

Results showed that believing one had taken the substance resulted in [ performance ] times almost as fast as those associated with consuming the drug itself. In contrast, taking the drug without knowledge yielded no significant performance increment. 1

When participation in a study prompts a physical response from a participant, it is difficult to isolate the effects of the explanatory variable. To counter the power of suggestion, researchers set aside one treatment group as a control group . This group is given a placebo treatment–a treatment that cannot influence the response variable. The control group helps researchers balance the effects of being in an experiment with the effects of the active treatments. Of course, if you are participating in a study and you know that you are receiving a pill which contains no actual medication, then the power of suggestion is no longer a factor. Blinding in a randomized experiment preserves the power of suggestion. When a person involved in a research study is blinded, he does not know who is receiving the active treatment(s) and who is receiving the placebo treatment . A double-blind experiment is one in which both the subjects and the researchers involved with the subjects are blinded.

The Smell & Taste Treatment and Research Foundation conducted a study to investigate whether smell can affect learning. Subjects completed mazes multiple times while wearing masks. They completed the pencil and paper mazes three times wearing floral-scented masks, and three times with unscented masks. Participants were assigned at random to wear the floral mask during the first three trials or during the last three trials. For each trial, researchers recorded the time it took to complete the maze and the subject’s impression of the mask’s scent: positive, negative, or neutral.

• The explanatory variable is scent, and the response variable is the time it takes to complete the maze.
• There are two treatments: a floral-scented mask and an unscented mask.
• All subjects experienced both treatments. The order of treatments was randomly assigned so there were no differences between the treatment groups. Random assignment eliminates the problem of lurking variables.
• Subjects will clearly know whether they can smell flowers or not, so subjects cannot be blinded in this study. Researchers timing the mazes can be blinded, though. The researcher who is observing a subject will not know which mask is being worn.

A researcher wants to study the effects of birth order on personality. Explain why this study could not be conducted as a randomized experiment. What is the main problem in a study that cannot be designed as a randomized experiment?

The explanatory variable is birth order. You cannot randomly assign a person’s birth order. Random assignment eliminates the impact of lurking variables. When you cannot assign subjects to treatment groups at random, there will be differences between the groups other than the explanatory variable.

You are concerned about the effects of texting on driving performance. Design a study to test the response time of drivers while texting and while driving only. How many seconds does it take for a driver to respond when a leading car hits the brakes?

• Describe the explanatory and response variables in the study.
• What are the treatments?
• What should you consider when selecting participants?
• Your research partner wants to divide participants randomly into two groups: one to drive without distraction and one to text and drive simultaneously. Is this a good idea? Why or why not?
• Identify any lurking variables that could interfere with this study.
• How can blinding be used in this study?
• Explanatory: presence of distraction from texting; response: response time measured in seconds
• Driving without distraction and driving while texting
• Answers will vary. Possible responses: Do participants regularly send and receive text messages? How long has the subject been driving? What is the age of the participants? Do participants have similar texting and driving experience?
• This is not a good plan because it compares drivers with different abilities. It would be better to assign both treatments to each participant in random order.
• Possible responses include: texting ability, driving experience, type of phone.
• The researchers observing the trials and recording response time could be blinded to the treatment being applied.

The widespread misuse and misrepresentation of statistical information often gives the field a bad name. Some say that “numbers don’t lie,” but the people who use numbers to support their claims often do.

A recent investigation of famous social psychologist, Diederik Stapel, has led to the retraction of his articles from some of the world’s top journals including Journal of Experimental Social Psychology, Social Psychology, Basic and Applied Social Psychology, British Journal of Social Psychology, and the magazine Science . Diederik Stapel is a former professor at Tilburg University in the Netherlands. Over the past two years, an extensive investigation involving three universities where Stapel has worked concluded that the psychologist is guilty of fraud on a colossal scale. Falsified data taints over 55 papers he authored and 10 Ph.D. dissertations that he supervised.

Stapel did not deny that his deceit was driven by ambition. But it was more complicated than that, he told me. He insisted that he loved social psychology but had been frustrated by the messiness of experimental data, which rarely led to clear conclusions. His lifelong obsession with elegance and order, he said, led him to concoct sexy results that journals found attractive. “It was a quest for aesthetics, for beauty—instead of the truth,” he said. He described his behavior as an addiction that drove him to carry out acts of increasingly daring fraud, like a junkie seeking a bigger and better high. 2

The committee investigating Stapel concluded that he is guilty of several practices including:

• creating datasets, which largely confirmed the prior expectations,
• altering data in existing datasets,
• changing measuring instruments without reporting the change, and
• misrepresenting the number of experimental subjects.

Clearly, it is never acceptable to falsify data the way this researcher did. Sometimes, however, violations of ethics are not as easy to spot.

Researchers have a responsibility to verify that proper methods are being followed. The report describing the investigation of Stapel’s fraud states that, “statistical flaws frequently revealed a lack of familiarity with elementary statistics.” 3 Many of Stapel’s co-authors should have spotted irregularities in his data. Unfortunately, they did not know very much about statistical analysis, and they simply trusted that he was collecting and reporting data properly.

Many types of statistical fraud are difficult to spot. Some researchers simply stop collecting data once they have just enough to prove what they had hoped to prove. They don’t want to take the chance that a more extensive study would complicate their lives by producing data contradicting their hypothesis.

Professional organizations, like the American Statistical Association, clearly define expectations for researchers. There are even laws in the federal code about the use of research data.

When a statistical study uses human participants, as in medical studies, both ethics and the law dictate that researchers should be mindful of the safety of their research subjects. The U.S. Department of Health and Human Services oversees federal regulations of research studies with the aim of protecting participants. When a university or other research institution engages in research, it must ensure the safety of all human subjects. For this reason, research institutions establish oversight committees known as Institutional Review Boards (IRB) . All planned studies must be approved in advance by the IRB. Key protections that are mandated by law include the following:

• Risks to participants must be minimized and reasonable with respect to projected benefits.
• Participants must give informed consent . This means that the risks of participation must be clearly explained to the subjects of the study. Subjects must consent in writing, and researchers are required to keep documentation of their consent.
• Data collected from individuals must be guarded carefully to protect their privacy.

These ideas may seem fundamental, but they can be very difficult to verify in practice. Is removing a participant’s name from the data record sufficient to protect privacy? Perhaps the person’s identity could be discovered from the data that remains. What happens if the study does not proceed as planned and risks arise that were not anticipated? When is informed consent really necessary? Suppose your doctor wants a blood sample to check your cholesterol level. Once the sample has been tested, you expect the lab to dispose of the remaining blood. At that point the blood becomes biological waste. Does a researcher have the right to take it for use in a study?

It is important that students of statistics take time to consider the ethical questions that arise in statistical studies. How prevalent is fraud in statistical studies? You might be surprised—and disappointed. There is a website (www.retractionwatch.com) dedicated to cataloging retractions of study articles that have been proven fraudulent. A quick glance will show that the misuse of statistics is a bigger problem than most people realize.

Vigilance against fraud requires knowledge. Learning the basic theory of statistics will empower you to analyze statistical studies critically.

Describe the unethical behavior in each example and describe how it could impact the reliability of the resulting data. Explain how the problem should be corrected.

A researcher is collecting data in a community.

• She selects a block where she is comfortable walking because she knows many of the people living on the street.
• No one seems to be home at four houses on her route. She does not record the addresses and does not return at a later time to try to find residents at home.
• She skips four houses on her route because she is running late for an appointment. When she gets home, she fills in the forms by selecting random answers from other residents in the neighborhood.
• By selecting a convenient sample, the researcher is intentionally selecting a sample that could be biased. Claiming that this sample represents the community is misleading. The researcher needs to select areas in the community at random.
• Intentionally omitting relevant data will create bias in the sample. Suppose the researcher is gathering information about jobs and child care. By ignoring people who are not home, she may be missing data from working families that are relevant to her study. She needs to make every effort to interview all members of the target sample.
• It is never acceptable to fake data. Even though the responses she uses are “real” responses provided by other participants, the duplication is fraudulent and can create bias in the data. She needs to work diligently to interview everyone on her route.

Describe the unethical behavior, if any, in each example and describe how it could impact the reliability of the resulting data. Explain how the problem should be corrected.

A study is commissioned to determine the favorite brand of fruit juice among teens in California.

• The survey is commissioned by the seller of a popular brand of apple juice.
• There are only two types of juice included in the study: apple juice and cranberry juice.
• Researchers allow participants to see the brand of juice as samples are poured for a taste test.
• Twenty-five percent of participants prefer Brand X, 33% prefer Brand Y and 42% have no preference between the two brands. Brand X references the study in a commercial saying “Most teens like Brand X as much as or more than Brand Y.”
• This is not necessarily a problem. The study should be monitored carefully, however, to ensure that the company is not pressuring researchers to return biased results.
• If the researchers truly want to determine the favorite brand of juice, then researchers should ask teens to compare different brands of the same type of juice. Choosing a sweet juice to compare against a sharp-flavored juice will not lead to an accurate comparison of brand quality.
• Participants could be biased by the knowledge. The results may be different from those obtained in a blind taste test.
• The commercial tells the truth, but not the whole truth. It leads consumers to believe that Brand X was preferred by more participants than Brand Y while the opposite is true.

“Vitamin E and Health,” Nutrition Source, Harvard School of Public Health, http://www.hsph.harvard.edu/nutritionsource/vitamin-e/ (accessed May 1, 2013).

Stan Reents. “Don’t Underestimate the Power of Suggestion,” athleteinme.com, http://www.athleteinme.com/ArticleView.aspx?id=1053 (accessed May 1, 2013).

Ankita Mehta. “Daily Dose of Aspirin Helps Reduce Heart Attacks: Study,” International Business Times, July 21, 2011. Also available online at http://www.ibtimes.com/daily-dose-aspirin-helps-reduce-heart-attacks-study-300443 (accessed May 1, 2013).

The Data and Story Library, http://lib.stat.cmu.edu/DASL/Stories/ScentsandLearning.html (accessed May 1, 2013).

M.L. Jacskon et al., “Cognitive Components of Simulated Driving Performance: Sleep Loss Effect and Predictors,” Accident Analysis and Prevention Journal, Jan no. 50 (2013), http://www.ncbi.nlm.nih.gov/pubmed/22721550 (accessed May 1, 2013).

“Earthquake Information by Year,” U.S. Geological Survey. http://earthquake.usgs.gov/earthquakes/eqarchives/year/ (accessed May 1, 2013).

“Fatality Analysis Report Systems (FARS) Encyclopedia,” National Highway Traffic and Safety Administration. http://www-fars.nhtsa.dot.gov/Main/index.aspx (accessed May 1, 2013).

Data from www.businessweek.com (accessed May 1, 2013).

Data from www.forbes.com (accessed May 1, 2013).

“America’s Best Small Companies,” http://www.forbes.com/best-small-companies/list/ (accessed May 1, 2013).

U.S. Department of Health and Human Services, Code of Federal Regulations Title 45 Public Welfare Department of Health and Human Services Part 46 Protection of Human Subjects revised January 15, 2009. Section 46.111:Criteria for IRB Approval of Research.

“April 2013 Air Travel Consumer Report,” U.S. Department of Transportation, April 11 (2013), http://www.dot.gov/airconsumer/april-2013-air-travel-consumer-report (accessed May 1, 2013).

Lori Alden, “Statistics can be Misleading,” econoclass.com, http://www.econoclass.com/misleadingstats.html (accessed May 1, 2013).

Maria de los A. Medina, “Ethics in Statistics,” Based on “Building an Ethics Module for Business, Science, and Engineering Students” by Jose A. Cruz-Cruz and William Frey, Connexions, http://cnx.org/content/m15555/latest/ (accessed May 1, 2013).

Chapter Review

A poorly designed study will not produce reliable data. There are certain key components that must be included in every experiment. To eliminate lurking variables, subjects must be assigned randomly to different treatment groups. One of the groups must act as a control group, demonstrating what happens when the active treatment is not applied. Participants in the control group receive a placebo treatment that looks exactly like the active treatments but cannot influence the response variable. To preserve the integrity of the placebo, both researchers and subjects may be blinded. When a study is designed properly, the only difference between treatment groups is the one imposed by the researcher. Therefore, when groups respond differently to different treatments, the difference must be due to the influence of the explanatory variable.

“An ethics problem arises when you are considering an action that benefits you or some cause you support, hurts or reduces benefits to others, and violates some rule.” 4 Ethical violations in statistics are not always easy to spot. Professional associations and federal agencies post guidelines for proper conduct. It is important that you learn basic statistical procedures so that you can recognize proper data analysis.

Design an experiment. Identify the explanatory and response variables. Describe the population being studied and the experimental units. Explain the treatments that will be used and how they will be assigned to the experimental units. Describe how blinding and placebos may be used to counter the power of suggestion.

Answers will vary. –>

Discuss potential violations of the rule requiring informed consent.

• Inmates in a correctional facility are offered good behavior credit in return for participation in a study.
• A research study is designed to investigate a new children’s allergy medication.
• Participants in a study are told that the new medication being tested is highly promising, but they are not told that only a small portion of participants will receive the new medication. Others will receive placebo treatments and traditional treatments.
• Inmates may not feel comfortable refusing participation, or may feel obligated to take advantage of the promised benefits. They may not feel truly free to refuse participation.
• Parents can provide consent on behalf of their children, but children are not competent to provide consent for themselves.
• All risks and benefits must be clearly outlined. Study participants must be informed of relevant aspects of the study in order to give appropriate consent.

How does sleep deprivation affect your ability to drive? A recent study measured the effects on 19 professional drivers. Each driver participated in two experimental sessions: one after normal sleep and one after 27 hours of total sleep deprivation. The treatments were assigned in random order. In each session, performance was measured on a variety of tasks including a driving simulation.

Use key terms from this module to describe the design of this experiment.

Explanatory variable: amount of sleep

Response variable: performance measured in assigned tasks

Treatments: normal sleep and 27 hours of total sleep deprivation

Experimental Units: 19 professional drivers

Lurking variables: none – all drivers participated in both treatments

Random assignment: treatments were assigned in random order; this eliminated the effect of any “learning” that may take place during the first experimental session

Control/Placebo: completing the experimental session under normal sleep conditions

Blinding: researchers evaluating subjects’ performance must not know which treatment is being applied at the time

An advertisement for Acme Investments displays the two graphs in [link] to show the value of Acme’s product in comparison with the Other Guy’s product. Describe the potentially misleading visual effect of these comparison graphs. How can this be corrected?

The graphs do not show scales of values. We do not know the period of time each graph represents; they may show data from different years. We also do not know if the vertical scales on each graph are equivalent. The scales may have been adjusted to exaggerate or minimize trends. There is no reliable information to be gleaned from these graphs, and setting them up as examples of performance is misleading. –>

The graph in [link] shows the number of complaints for six different airlines as reported to the US Department of Transportation in February 2013. Alaska, Pinnacle, and Airtran Airlines have far fewer complaints reported than American, Delta, and United. Can we conclude that American, Delta, and United are the worst airline carriers since they have the most complaints?

You cannot assume that the numbers of complaints reflect the quality of the airlines. The airlines shown with the greatest number of complaints are the ones with the most passengers. You must consider the appropriateness of methods for presenting data; in this case displaying totals is misleading.

• 1 McClung, M. Collins, D. “Because I know it will!”: placebo effects of an ergogenic aid on athletic performance. Journal of Sport & Exercise Psychology. 2007 Jun. 29(3):382-94. Web. April 30, 2013.
• 2 Yudhijit Bhattacharjee, “The Mind of a Con Man,” Magazine, New York Times, April 26, 2013. Available online at: http://www.nytimes.com/2013/04/28/magazine/diederik-stapels-audacious-academic-fraud.html?src=dayp&_r=2& (accessed May 1, 2013).
• 3 “Flawed Science: The Fraudulent Research Practices of Social Psychologist Diederik Stapel,” Tilburg University, November 28, 2012, http://www.tilburguniversity.edu/upload/064a10cd-bce5-4385-b9ff-05b840caeae6_120695_Rapp_nov_2012_UK_web.pdf (accessed May 1, 2013).
• 4 Andrew Gelman, “Open Data and Open Methods,” Ethics and Statistics, http://www.stat.columbia.edu/~gelman/research/published/ChanceEthics1.pdf (accessed May 1, 2013).

the independent variable in an experiment; the value controlled by researchers

the dependent variable in an experiment; the value that is measured for change at the end of an experiment

different values or components of the explanatory variable applied in an experiment

a variable that has an effect on a study even though it is neither an explanatory variable nor a response variable

the act of organizing experimental units into treatment groups using random methods

any individual or object to be measured

a group in a randomized experiment that receives an inactive treatment but is otherwise managed exactly as the other groups

not telling participants which treatment a subject is receiving

an inactive treatment that has no real effect on the explanatory variable

the act of blinding both the subjects of an experiment and the researchers who work with the subjects

a committee tasked with oversight of research programs that involve human subjects

Any human subject in a research study must be cognizant of any risks or costs associated with the study. The subject has the right to know the nature of the treatments included in the study, their potential risks, and their potential benefits. Consent must be given freely by an informed, fit participant.

Introductory Statistics Copyright © 2024 by LOUIS: The Louisiana Library Network is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License , except where otherwise noted.

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1.4 Experimental Design and Ethics

Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments. In this module, you will learn important aspects of experimental design. Proper study design ensures the production of reliable, accurate data.

The purpose of an experiment is to investigate the relationship between two variables. When one variable causes change in another, we call the first variable the explanatory variable . The affected variable is called the response variable . In a randomized experiment, the researcher manipulates values of the explanatory variable and measures the resulting changes in the response variable. The different values of the explanatory variable are called treatments . An experimental unit is a single object or individual to be measured.

You want to investigate the effectiveness of vitamin E in preventing disease. You recruit a group of subjects and ask them if they regularly take vitamin E. You notice that the subjects who take vitamin E exhibit better health on average than those who do not. Does this prove that vitamin E is effective in disease prevention? It does not. There are many differences between the two groups compared in addition to vitamin E consumption. People who take vitamin E regularly often take other steps to improve their health: exercise, diet, other vitamin supplements, choosing not to smoke. Any one of these factors could be influencing health. As described, this study does not prove that vitamin E is the key to disease prevention.

Additional variables that can cloud a study are called lurking variables . In order to prove that the explanatory variable is causing a change in the response variable, it is necessary to isolate the explanatory variable. The researcher must design her experiment in such a way that there is only one difference between groups being compared: the planned treatments. This is accomplished by the random assignment of experimental units to treatment groups. When subjects are assigned treatments randomly, all of the potential lurking variables are spread equally among the groups. At this point the only difference between groups is the one imposed by the researcher. Different outcomes measured in the response variable, therefore, must be a direct result of the different treatments. In this way, an experiment can prove a cause-and-effect connection between the explanatory and response variables.

The power of suggestion can have an important influence on the outcome of an experiment. Studies have shown that the expectation of the study participant can be as important as the actual medication. In one study of performance-enhancing drugs, researchers noted:

Results showed that believing one had taken the substance resulted in [ performance ] times almost as fast as those associated with consuming the drug itself. In contrast, taking the drug without knowledge yielded no significant performance increment. 1

When participation in a study prompts a physical response from a participant, it is difficult to isolate the effects of the explanatory variable. To counter the power of suggestion, researchers set aside one treatment group as a control group . This group is given a placebo treatment–a treatment that cannot influence the response variable. The control group helps researchers balance the effects of being in an experiment with the effects of the active treatments. Of course, if you are participating in a study and you know that you are receiving a pill which contains no actual medication, then the power of suggestion is no longer a factor. Blinding or masking in a randomized experiment preserves the power of suggestion. When a person involved in a research study is blinded, they do not know who is receiving the active treatment(s) and who is receiving the placebo treatment. A double-blind experiment is one in which both the subjects and the researchers involved with the subjects are blinded.

Example 1.19

Researchers want to investigate whether taking aspirin regularly reduces the risk of heart attack. Four hundred people between the ages of 50 and 84 are recruited as participants. The people are divided randomly into two groups: one group will take aspirin, and the other group will take a placebo. Each person takes one pill each day for three years, but they don't know whether they are taking aspirin or the placebo. At the end of the study, researchers count the number of people in each group who have had heart attacks.

Identify the following values for this study: population, sample, experimental units, explanatory variable, response variable, treatments.

The population is people aged 50 to 84. The sample is the 400 people who participated. The experimental units are the individual people in the study. The explanatory variable is oral medication. The treatments are aspirin and a placebo. The response variable is whether a subject had a heart attack.

Try It 1.19

A study needs to be conducted of the effect of three medicines A, B, and C on the height of adults aged 30 to 45. 90 adults were selected randomly and divided into three equal groups. The first group was asked to take medicine A for 6 months. The second group was asked to take medicine B for 6 months. The third group was asked to take medicine C for 6 months. The average change in height in each group is calculated at the end of the study.

Identify the following values for this study: population, sample, experimental units, explanatory variables, response variable, treatments.

Example 1.20

The Smell & Taste Treatment and Research Foundation conducted a study to investigate whether smell can affect learning. Subjects completed mazes multiple times while wearing masks. They completed the pencil and paper mazes three times wearing floral-scented masks, and three times with unscented masks. Participants were assigned at random to wear the floral mask during the first three trials or during the last three trials. For each trial, researchers recorded the time it took to complete the maze and the subject’s impression of the mask’s scent: positive, negative, or neutral.

• Describe the explanatory and response variables in this study.
• What are the treatments?
• Identify any lurking variables that could interfere with this study.
• Is it possible to use blinding in this study?
• The explanatory variable is scent, and the response variable is the time it takes to complete the maze.
• There are two treatments: a floral-scented mask and an unscented mask.
• All subjects experienced both treatments. The order of treatments was randomly assigned so there were no differences between the treatment groups. Random assignment eliminates the problem of lurking variables.
• Subjects will clearly know whether they can smell flowers or not, so subjects cannot be blinded in this study. Researchers timing the mazes can be blinded, though. The researcher who is observing a subject will not know which mask is being worn.

Try It 1.20

The Placebo Research Group conducted a study to find the extent of placebo effects. A group of men randomly selected were asked to take a test before and after taking a pill that induces a mild headache. The pill in half of the randomly selected men was replaced with a similar pill that has no effect. For each trial, researchers recorded the change in time men took to complete the tests before and after taking the pill.

• Describe the explanatory and response variable in this study.

Example 1.21

A researcher wants to study the effects of birth order on personality. Explain why this study could not be conducted as a randomized experiment. What is the main problem in a study that cannot be designed as a randomized experiment?

The explanatory variable is birth order. You cannot randomly assign a person’s birth order. Random assignment eliminates the impact of lurking variables. When you cannot assign subjects to treatment groups at random, there will be differences between the groups other than the explanatory variable.

Try It 1.21

You are concerned about the effects of texting on driving performance. Design a study to test the response time of drivers while texting and while driving only. How many seconds does it take for a driver to respond when a leading car hits the brakes?

• Describe the explanatory and response variables in the study.
• What should you consider when selecting participants?
• Your research partner wants to divide participants randomly into two groups: one to drive without distraction and one to text and drive simultaneously. Is this a good idea? Why or why not?
• How can blinding be used in this study?

The widespread misuse and misrepresentation of statistical information often gives the field a bad name. Some say that “numbers don’t lie,” but the people who use numbers to support their claims often do.

An investigation of famous social psychologist, Diederik Stapel, has led to the retraction of his articles from some of the world’s top journals including Journal of Experimental Social Psychology, Social Psychology, Basic and Applied Social Psychology, British Journal of Social Psychology, and the magazine Science . Diederik Stapel is a former professor at Tilburg University in the Netherlands. An extensive investigation involving three universities where Stapel has worked concluded that the psychologist is guilty of fraud on a colossal scale. Falsified data taints over 55 papers he authored and 10 Ph.D. dissertations that he supervised.

Stapel did not deny that his deceit was driven by ambition. But it was more complicated than that, he told me. He insisted that he loved social psychology but had been frustrated by the messiness of experimental data, which rarely led to clear conclusions. His lifelong obsession with elegance and order, he said, led him to concoct sexy results that journals found attractive. “It was a quest for aesthetics, for beauty—instead of the truth,” he said. He described his behavior as an addiction that drove him to carry out acts of increasingly daring fraud, like a junkie seeking a bigger and better high. 2

The committee investigating Stapel concluded that he is guilty of several practices including:

• creating datasets, which largely confirmed the prior expectations,
• altering data in existing datasets,
• changing measuring instruments without reporting the change, and
• misrepresenting the number of experimental subjects.

Clearly, it is never acceptable to falsify data the way this researcher did. Sometimes, however, violations of ethics are not as easy to spot.

Researchers have a responsibility to verify that proper methods are being followed. The report describing the investigation of Stapel’s fraud states that, “statistical flaws frequently revealed a lack of familiarity with elementary statistics.” 3 Many of Stapel’s co-authors should have spotted irregularities in his data. Unfortunately, they did not know very much about statistical analysis, and they simply trusted that he was collecting and reporting data properly.

Many types of statistical fraud are difficult to spot. Some researchers simply stop collecting data once they have just enough to prove what they had hoped to prove. They don’t want to take the chance that a more extensive study would complicate their lives by producing data contradicting their hypothesis.

Professional organizations, like the American Statistical Association, clearly define expectations for researchers. There are even laws in the federal code about the use of research data.

When a statistical study uses human participants, as in medical studies, both ethics and the law dictate that researchers should be mindful of the safety of their research subjects. The U.S. Department of Health and Human Services oversees federal regulations of research studies with the aim of protecting participants. When a university or other research institution engages in research, it must ensure the safety of all human subjects. For this reason, research institutions establish oversight committees known as Institutional Review Boards (IRB) . All planned studies must be approved in advance by the IRB. Key protections that are mandated by law include the following:

• Risks to participants must be minimized and reasonable with respect to projected benefits.
• Participants must give informed consent . This means that the risks of participation must be clearly explained to the subjects of the study. Subjects must consent in writing, and researchers are required to keep documentation of their consent.
• Data collected from individuals must be guarded carefully to protect their privacy.

These ideas may seem fundamental, but they can be very difficult to verify in practice. Is removing a participant’s name from the data record sufficient to protect privacy? Perhaps the person’s identity could be discovered from the data that remains. What happens if the study does not proceed as planned and risks arise that were not anticipated? When is informed consent really necessary? Suppose your doctor wants a blood sample to check your cholesterol level. Once the sample has been tested, you expect the lab to dispose of the remaining blood. At that point the blood becomes biological waste. Does a researcher have the right to take it for use in a study?

It is important that students of statistics take time to consider the ethical questions that arise in statistical studies. How prevalent is fraud in statistical studies? You might be surprised—and disappointed. There is a website dedicated to cataloging retractions of study articles that have been proven fraudulent. A quick glance will show that the misuse of statistics is a bigger problem than most people realize.

Vigilance against fraud requires knowledge. Learning the basic theory of statistics will empower you to analyze statistical studies critically.

Example 1.22

Describe the unethical behavior in each example and describe how it could impact the reliability of the resulting data. Explain how the problem should be corrected.

A researcher is collecting data in a community.

• The researcher selects a block where they are comfortable walking because they know many of the people living on the street.
• No one seems to be home at four houses on the route. They do not record the addresses and do not return at a later time to try to find residents at home.
• The researcher skips four houses on the route because they are running late for an appointment. When they get home, they fill in the forms by selecting random answers from other residents in the neighborhood.
• By selecting a convenient sample, the researcher is intentionally selecting a sample that could be biased. Claiming that this sample represents the community is misleading. The researcher needs to select areas in the community at random.
• Intentionally omitting relevant data will create bias in the sample. Suppose the researcher is gathering information about jobs and child care. By ignoring people who are not home, They may be missing data from working families that are relevant to her study. They need to make every effort to interview all members of the target sample.
• It is never acceptable to fake data. Even though the responses the researcher are “real” responses provided by other participants, the duplication is fraudulent and can create bias in the data. They researcher needs to work diligently to interview everyone on their route.

Try It 1.22

Describe the unethical behavior, if any, in each example and describe how it could impact the reliability of the resulting data. Explain how the problem should be corrected.

A study is commissioned to determine the favorite brand of fruit juice among teens in California.

• The survey is commissioned by the seller of a popular brand of apple juice.
• There are only two types of juice included in the study: apple juice and cranberry juice.
• Researchers allow participants to see the brand of juice as samples are poured for a taste test.
• Twenty-five percent of participants prefer Brand X, 33% prefer Brand Y and 42% have no preference between the two brands. Brand X references the study in a commercial saying “Most teens like Brand X as much as or more than Brand Y.”
• 1 McClung, M. Collins, D. “Because I know it will!”: placebo effects of an ergogenic aid on athletic performance. Journal of Sport & Exercise Psychology. 2007 Jun. 29(3):382-94. Web. April 30, 2013.
• 2 Yudhijit Bhattacharjee, “The Mind of a Con Man,” Magazine, New York Times, April 26, 2013. Available online at: http://www.nytimes.com/2013/04/28/magazine/diederik-stapels-audacious-academic-fraud.html?src=dayp&_r=2& (accessed May 1, 2013).
• 3 “Flawed Science: The Fraudulent Research Practices of Social Psychologist Diederik Stapel,” Tillburg University, November 28, 2012, http://www.tilburguniversity.edu/upload/064a10cd-bce5-4385-b9ff-05b840caeae6_120695_Rapp_nov_2012_UK_web.pdf (accessed May 1, 2013).

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• Book title: Introductory Statistics 2e
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GKT101: General Knowledge for Teachers – Math

Experimental Design and Ethics

Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments. In this module, you will learn important aspects of experimental design. Proper study design ensures the production of reliable, accurate data.

The purpose of an experiment is to investigate the relationship between two variables. When one variable causes change in another, we call the first variable the  explanatory variable . The affected variable is called the  response variable . In a randomized experiment, the researcher manipulates values of the explanatory variable and measures the resulting changes in the response variable. The different values of the explanatory variable are called  treatments . An  experimental unit  is a single object or individual to be measured.

You want to investigate the effectiveness of vitamin E in preventing disease. You recruit a group of subjects and ask them if they regularly take vitamin E. You notice that the subjects who take vitamin E exhibit better health on average than those who do not. Does this prove that vitamin E is effective in disease prevention? It does not. There are many differences between the two groups compared in addition to vitamin E consumption. People who take vitamin E regularly often take other steps to improve their health: exercise, diet, other vitamin supplements, choosing not to smoke. Any one of these factors could be influencing health. As described, this study does not prove that vitamin E is the key to disease prevention.

Additional variables that can cloud a study are called  lurking variables . In order to prove that the explanatory variable is causing a change in the response variable, it is necessary to isolate the explanatory variable. The researcher must design her experiment in such a way that there is only one difference between groups being compared: the planned treatments. This is accomplished by the  random assignment  of experimental units to treatment groups. When subjects are assigned treatments randomly, all of the potential lurking variables are spread equally among the groups. At this point the only difference between groups is the one imposed by the researcher. Different outcomes measured in the response variable, therefore, must be a direct result of the different treatments. In this way, an experiment can prove a cause-and-effect connection between the explanatory and response variables.

The power of suggestion can have an important influence on the outcome of an experiment. Studies have shown that the expectation of the study participant can be as important as the actual medication. In one study of performance-enhancing drugs, researchers noted:

Results showed that believing one had taken the substance resulted in [ performance ] times almost as fast as those associated with consuming the drug itself. In contrast, taking the drug without knowledge yielded no significant performance increment.

When participation in a study prompts a physical response from a participant, it is difficult to isolate the effects of the explanatory variable. To counter the power of suggestion, researchers set aside one treatment group as a  control group . This group is given a  placebo   treatment–a treatment that cannot influence the response variable. The control group helps researchers balance the effects of being in an experiment with the effects of the active treatments. Of course, if you are participating in a study and you know that you are receiving a pill which contains no actual medication, then the power of suggestion is no longer a factor.  Blinding   in a randomized experiment preserves the power of suggestion. When a person involved in a research study is blinded, he does not know who is receiving the active treatment(s) and who is receiving the placebo treatment. A  double-blind experiment  is one in which both the subjects and the researchers involved with the subjects are blinded.

EXAMPLE 1.19

Researchers want to investigate whether taking aspirin regularly reduces the risk of heart attack. Four hundred men between the ages of 50 and 84 are recruited as participants. The men are divided randomly into two groups: one group will take aspirin, and the other group will take a placebo. Each man takes one pill each day for three years, but he does not know whether he is taking aspirin or the placebo. At the end of the study, researchers count the number of men in each group who have had heart attacks.

Identify the following values for this study: population, sample, experimental units, explanatory variable, response variable, treatments.

Solution   1

The  population  is men aged 50 to 84. The  sample  is the 400 men who participated. The  experimental units  are the individual men in the study. The  explanatory variable  is oral medication. The  treatments  are aspirin and a placebo. The  response variable  is whether a subject had a heart attack.

EXAMPLE 1.20

The Smell & Taste Treatment and Research Foundation conducted a study to investigate whether smell can affect learning. Subjects completed mazes multiple times while wearing masks. They completed the pencil and paper mazes three times wearing floral-scented masks, and three times with unscented masks. Participants were assigned at random to wear the floral mask during the first three trials or during the last three trials. For each trial, researchers recorded the time it took to complete the maze and the subject's impression of the mask's scent: positive, negative, or neutral.

• Describe the explanatory and response variables in this study.
• What are the treatments?
• Identify any lurking variables that could interfere with this study.
• Is it possible to use blinding in this study?

Solution 1

• The explanatory variable is scent, and the response variable is the time it takes to complete the maze.
• There are two treatments: a floral-scented mask and an unscented mask.
• All subjects experienced both treatments. The order of treatments was randomly assigned so there were no differences between the treatment groups. Random assignment eliminates the problem of lurking variables.
• Subjects will clearly know whether they can smell flowers or not, so subjects cannot be blinded in this study. Researchers timing the mazes can be blinded, though. The researcher who is observing a subject will not know which mask is being worn.

EXAMPLE 1.21

A researcher wants to study the effects of birth order on personality. Explain why this study could not be conducted as a randomized experiment. What is the main problem in a study that cannot be designed as a randomized experiment?

The explanatory variable is birth order. You cannot randomly assign a person's birth order. Random assignment eliminates the impact of lurking variables. When you cannot assign subjects to treatment groups at random, there will be differences between the groups other than the explanatory variable.

TRY IT 1.21

You are concerned about the effects of texting on driving performance. Design a study to test the response time of drivers while texting and while driving only. How many seconds does it take for a driver to respond when a leading car hits the brakes?

• Describe the explanatory and response variables in the study.
• What should you consider when selecting participants?
• Your research partner wants to divide participants randomly into two groups: one to drive without distraction and one to text and drive simultaneously. Is this a good idea? Why or why not?
• How can blinding be used in this study?