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Correlational Research Vs Experimental Research

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Correlational research and experimental research are two different research approaches used in social sciences and other fields of research.

Correlational Research

Correlational Research is a research approach that examines the relationship between two or more variables. It involves measuring the degree of association or correlation between the variables without manipulating them. The goal of correlational research is to identify whether there is a relationship between the variables and the strength of that relationship. Correlational research is typically conducted through surveys, observational studies, or secondary data analysis.

Experimental Research

Experimental Research , on the other hand, is a research approach that involves the manipulation of one or more variables to observe the effect on another variable. The goal of experimental research is to establish a cause-and-effect relationship between the variables. Experimental research is typically conducted in a controlled environment and involves random assignment of participants to different groups to ensure that the groups are equivalent. The data is collected through measurements and observations, and statistical analysis is used to test the hypotheses.

Difference Between Correlational Research and Experimental Research

Here’s a comparison table that highlights the differences between correlational research and experimental research:

Also see Research Methods

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Correlational Study Overview & Examples

By Jim Frost 2 Comments

What is a Correlational Study?

A correlational study is an experimental design that evaluates only the correlation between variables. The researchers record measurements but do not control or manipulate the variables. Correlational research is a form of observational study .

A correlation indicates that as the value of one variable increases, the other tends to change in a specific direction:

• Positive correlation : Two variables increase or decrease together (as height increases, weight tends to increase).
• Negative correlation : As one variable increases, the other tends to decrease (as school absences increase, grades tend to fall).
• No correlation : No relationship exists between the two variables. As one increases, the other does not change in a specific direction (as absences increase, height doesn’t tend to increase or decrease).

For example, researchers conducting correlational research explored the relationship between social media usage and levels of anxiety in young adults. Participants reported their demographic information and daily time on various social media platforms and completed a standardized anxiety assessment tool.

The correlational study looked for relationships between social media usage and anxiety. Is increased social media usage associated with higher anxiety? Is it worse for particular demographics?

Learn more about Interpreting Correlation .

Using Correlational Research

Correlational research design is crucial in various disciplines, notably psychology and medicine. This type of design is generally cheaper, easier, and quicker to conduct than an experiment because the researchers don’t control any variables or conditions. Consequently, these studies often serve as an initial assessment, especially when random assignment and controlling variables for a true experiment are not feasible or unethical.

However, an unfortunate aspect of a correlational study is its limitation in establishing causation. While these studies can reveal connections between variables, they cannot prove that altering one variable will cause changes in another. Hence, correlational research can determine whether relationships exist but cannot confirm causality.

Remember, correlation doesn’t necessarily imply causation !

Correlational Study vs Experiment

The difference between the two designs is simple.

In a correlational study, the researchers don’t systematically control any variables. They’re simply observing events and do not want to influence outcomes.

In an experiment, researchers manipulate variables and explicitly hope to affect the outcomes. For example, they might control the treatment condition by giving a medication or placebo to each subject. They also randomly assign subjects to the control and treatment groups, which helps establish causality.

Learn more about Randomized Controlled Trials (RCTs) , which statisticians consider to be true experiments.

Types of Correlation Studies and Examples

Researchers divide these studies into three broad types.

Secondary Data Sources

One approach to correlational research is to utilize pre-existing data, which may include official records, public polls, or data from earlier studies. This method can be cost-effective and time-efficient because other researchers have already gathered the data. These existing data sources can provide large sample sizes and longitudinal data , thereby showing relationship trends.

However, it also comes with potential drawbacks. The data may be incomplete or irrelevant to the new research question. Additionally, as a researcher, you won’t have control over the original data collection methods, potentially impacting the data’s reliability and validity .

Using existing data makes this approach a retrospective study .

Surveys in Correlation Research

Surveys are a great way to collect data for correlational studies while using a consistent instrument across all respondents. You can use various formats, such as in-person, online, and by phone. And you can ask the questions necessary to obtain the particular variables you need for your project. In short, it’s easy to customize surveys to match your study’s requirements.

However, you’ll need to carefully word all the questions to be clear and not introduce bias in the results. This process can take multiple iterations and pilot studies to produce the finished survey.

For example, you can use a survey to find correlations between various demographic variables and political opinions.

Naturalistic Observation

Naturalistic observation is a method of collecting field data for a correlational study. Researchers observe and measure variables in a natural environment. The process can include counting events, categorizing behavior, and describing outcomes without interfering with the activities.

For example, researchers might observe and record children’s behavior after watching television. Does a relationship exist between the type of television program and behaviors?

Naturalistic observations occur in a prospective study .

Analyzing Data from a Correlational Study

Statistical analysis of correlational research frequently involves correlation and regression analysis .

A correlation coefficient describes the strength and direction of the relationship between two variables with a single number.

Regression analysis can evaluate how multiple variables relate to a single outcome. For example, in the social media correlational study example, how do the demographic variables and daily social media usage collectively correlate with anxiety?

Curtis EA, Comiskey C, Dempsey O.  Importance and use of correlational research .  Nurse Researcher . 2016;23(6):20-25. doi:10.7748/nr.2016.e1382

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Reader Interactions

January 14, 2024 at 4:34 pm

Hi Jim. Have you written a blog note dedicated to clinical trials? If not, besides the note on hypothesis testing, are there other blogs ypo have written that touch on clinical trials?

January 14, 2024 at 5:49 pm

Hi Stan, I haven’t written a blog post specifically about clinical trials, but I have the following related posts:

Randomized Controlled Trials Clinical Trial about a COVID vaccine Clinical Trials about flu vaccines

Comments and Questions Cancel reply

Excerpt taken from Grant MacEwan Community College weg page.

Correlational versus Experimental Studies

Psychological studies vary in design. In correlational studies a researcher looks for associations among naturally occurring variables, whereas in experimental studies the researcher introduces a change and then monitors its effects. It is important to be able to distinguish between correlational and experimental designs, because only well-controlled experimental designs allow conclusions about cause and effect.

Consider the following example: A psychologist is hired by government to see whether there is any association between children watching violent television and an increase in their aggressive behavior. (question suitable for a correlational design) Does watching violent television make children aggressive? (question suitable for an experiment) Let's look at examples of the two types of studies.

Click here if you want to go directly to the quiz.

Example of a Correlational Study

In a correlational study, the researcher would ask children (or their parents) to document the amount of violent television the child watches in a set time period (perhaps one week) and then the researcher would observe the children's behavior, recording instances of aggression. The researcher does no intervention. Suppose the researcher gets the following results, where each row in the table below corresponds to the scores of one child. Do you notice any pattern between the two columns of numbers? If you knew how much violent television a child watched, would you be able to predict the child's aggression score?

The table shows a strong positive correlation because an increase in the amount of violent television is usually associated with an increase in aggression score. We would predict that children who watch more violent television are likely to score higher on the aggression scale. But can we conclude that watching violent television causes the children to be more aggressive? Although this conclusion is certainly consistent with the results, it cannot be adopted because it is not the only plausible interpretation. It could be that children who are more aggressive choose to watch more violent television (aggression causes the television viewing habits rather than the other way around) or that television habits and aggression are not causally related, but appear to go together because both are associated with a third factor, such as amount of parent supervision. Perhaps children who are not supervised are more likely to watch more violent television, as well as score higher on an aggression scale. Correlational studies do not allow us to determine which of these interpretations is the correct one. To do that, we would have to do an experiment, as described in the next section.

Example of an Experiment

In an experiment the researcher exposes the children to various types of television and then monitors their behavior for aggression. In a simple experiment, a researcher would create two groups of children (an experimental and a control group) through randomization (each participant has an equal chance of getting in either group). Randomization is used as a control so that the two groups are roughly equivalent at the start of the experiment. (see the section on Identifying variables and confounds for more information) The experimental group is shown violent television, and under identical conditions, the control group is shown nonviolent television. Afterwards the researcher sees whether children in the experimental group score higher on the aggression scale than children in the control group. If they do, the experimenter can conclude that watching violent television makes children aggressive (provided the researcher has controlled for confounds and extraneous variables.

Quiz - Distinguishing Between Correlational Studies and Experiments

Answers to the quiz.

A newspaper headline reads, Heavy Drinkers Get Lower College Grades. What would you conclude from this headline? What type of study was this based on?

This study was most likely correlational because an experiment would not be ethical. (In order to do an experiment, the researcher would have to control the students' drinking, forcing some students to drink heavily and then observing the effects of the drinking on their grades.) All we can conclude from the headline is that heavy drinking is associated with lower grades. We cannot conclude that drinking caused the lower grades because other plausible interpretations have not been ruled out. (Perhaps students drink more because they make lower grades. Or perhaps drinking and grades appear related only because they are both related to the degree of student commitment to being in school.)

Back to the Questions

Your textbook states that people remember concrete words better than abstract ones. Could this finding have come from an experiment? Would it be reasonable to infer that concreteness facilitates memory?

Yes. An experiment could have been set up in two different ways. In one, called a between-subjects design, people are randomly assigned to groups. One group learns concrete words; the other learns abstract ones to see whether the group learning concrete words remembers more. In another experimental design, called a within-subjects design, all participants learn both the concrete and abstract words to see whether individuals learn concrete words better than abstract ones. (Of course, the order in which people learn the words would have to be controlled using a procedure called counterbalancing)

Are people who were abused as children more likely than others to become child abusers? What type of study would be used to research this question?

This question can only be addressed by correlational studies. (In order to do an experiment, the researcher would have to randomly assign some children to a group that gets abused; others to a group that does not get abused. Obviously, this cannot be done!) Thus, we must be cautious about assuming the cause of any association between experiencing abuse as a child and perpetrating it as an adult. Correlational studies do not support one interpretation over others.

A health magazine reports that depressed people who continue medication for at least six months are less likely to relapse than are people who take medication for less than six months. What would you need to know about the design of this study, in order to interpret the report?

Enough information to see whether it was a correlational study or an experiment. In a correlational study, the researcher would take advantage of the fact that some depressed people stay on medication longer than others. (The researcher does not control how long people are on medication). Suppose the researcher finds that people on medication more than six months are less likely to relapse. The researcher cannot conclude that the increased time on medication improved the relapse rate because other explanations have not been ruled out. (Perhaps people who stay on medication longer differ from the others in ways that would protect them from relapse. Maybe the people on medication longer are also more likely to receive psychotherapy.) In an experimental study, the researcher controls how long people stay on medication. Half of a sample of depressed people is randomly chosen to receive medication for less than six months; the others receive medication for more than six months. The only way the two groups differ is in the duration of the medication.

A private school advertises that a group of their students recently scored 10 points higher on a math test than a group of other students from a public school? What can you conclude from this advertisment? Is this an example of an experiment?

We cannot conclude anything about the cause of the difference in scores between the two groups. This is not an experiment because the researcher did not control group membership to ensure that the groups were roughly equivalent when they started school. (Imagine the reaction of parents if the researcher randomly assigned some children to attend private school and others to go to public school.). It is a quasi-experiment (resembles an experiment because it compares groups, but is not an experiment because the researcher did not control group membership) and should be interpreted like a correlational study. We cannot conclude that private school caused students to score higher on the math test. (Perhaps the students from private school are more likely to practice math on computer at home and it is this home activity, rather than experiences at school, that leads to the higher math scores.)

Back to Helpful Course Materials Page

Correlation Study vs. Experimental Study

What's the difference.

Correlation study and experimental study are two different research methods used in the field of social sciences. A correlation study aims to examine the relationship between two or more variables without manipulating any of them. It measures the degree of association between variables, but it does not establish causation. On the other hand, an experimental study involves manipulating one or more variables to determine their effect on another variable. It allows researchers to establish cause-and-effect relationships by controlling and manipulating variables. While correlation studies are useful for identifying patterns and relationships, experimental studies provide more rigorous evidence for causal relationships.

Further Detail

Introduction.

When conducting research in various fields, it is essential to choose the appropriate study design to answer specific research questions. Two commonly used study designs are correlation study and experimental study. While both approaches aim to explore relationships between variables, they differ in their methodology, control over variables, and ability to establish causation. In this article, we will delve into the attributes of correlation study and experimental study, highlighting their strengths and limitations.

Correlation Study

A correlation study is a research design that examines the relationship between two or more variables without manipulating them. It focuses on measuring the degree of association or correlation between variables. Researchers collect data on the variables of interest and analyze them to determine if there is a statistical relationship between them. The strength and direction of the relationship are typically expressed using correlation coefficients, such as Pearson's r or Spearman's rho.

One of the key advantages of correlation studies is their ability to explore naturally occurring relationships in real-world settings. They are often used in observational research, where manipulating variables may not be feasible or ethical. Correlation studies also allow researchers to examine associations between variables that cannot be manipulated, such as gender, age, or genetic factors.

However, correlation studies have limitations. They cannot establish causation between variables since they do not involve experimental manipulation. Correlation does not imply causation, as there may be other underlying factors or confounding variables influencing the observed relationship. Additionally, correlation studies are susceptible to the ecological fallacy, where conclusions about individuals are drawn from group-level data.

Experimental Study

An experimental study, on the other hand, is a research design that involves manipulating one or more variables to determine their effect on other variables. It aims to establish cause-and-effect relationships by controlling and manipulating independent variables while measuring the impact on dependent variables. Experimental studies often include a control group to compare the outcomes of the manipulated variables with those of the unmanipulated variables.

One of the primary advantages of experimental studies is their ability to establish causation. By manipulating variables and controlling for confounding factors, researchers can determine whether changes in the independent variable directly lead to changes in the dependent variable. Experimental studies also provide a high level of control over extraneous variables, reducing the likelihood of alternative explanations for the observed results.

However, experimental studies also have limitations. They are often conducted in controlled laboratory settings, which may limit the generalizability of the findings to real-world situations. Experimental designs may also face ethical constraints, as manipulating variables or withholding treatment from participants may raise ethical concerns. Additionally, some research questions may not be amenable to experimental manipulation, such as studying the impact of natural disasters or historical events.

Comparison of Attributes

Now that we have explored the basic attributes of correlation studies and experimental studies, let's compare them in more detail:

Methodology

In correlation studies, researchers collect data on variables of interest without manipulating them. They analyze the data to determine the strength and direction of the relationship between variables. In contrast, experimental studies involve manipulating independent variables and measuring the impact on dependent variables. Researchers randomly assign participants to different groups to compare the outcomes.

Control over Variables

Correlation studies have limited control over variables since they rely on naturally occurring data. Researchers cannot manipulate variables or control for confounding factors. On the other hand, experimental studies provide a high level of control over variables. Researchers can manipulate independent variables, control for confounding factors, and randomize participants to ensure comparability between groups.

Correlation studies cannot establish causation between variables. They can only identify associations or relationships. Experimental studies, on the other hand, are designed to establish cause-and-effect relationships. By manipulating variables and controlling for confounding factors, researchers can determine whether changes in the independent variable directly lead to changes in the dependent variable.

Generalizability

Correlation studies often rely on data collected in real-world settings, making them more applicable to real-life situations. However, the findings may not be generalizable to the entire population due to sampling limitations or the presence of confounding variables. Experimental studies, while providing more control, are often conducted in controlled laboratory settings, which may limit their generalizability to real-world contexts.

Ethical Considerations

Correlation studies generally have fewer ethical concerns since they do not involve manipulating variables or withholding treatment. They are often used in observational research, where participants are not directly influenced by the study. Experimental studies, on the other hand, may face ethical constraints, especially when manipulating variables or withholding treatment from participants. Researchers must ensure that the potential benefits outweigh any potential harm or discomfort to participants.

Applicability

Correlation studies are particularly useful when exploring relationships between variables that cannot be manipulated, such as gender, age, or genetic factors. They are also valuable in generating hypotheses for further research. Experimental studies, on the other hand, are well-suited for establishing cause-and-effect relationships and testing specific interventions or treatments. They provide a more rigorous approach to answering research questions that require manipulation of variables.

Correlation studies and experimental studies are two distinct research designs with their own strengths and limitations. Correlation studies are valuable for exploring naturally occurring relationships and identifying associations between variables. However, they cannot establish causation and are susceptible to confounding variables. Experimental studies, on the other hand, provide a higher level of control and can establish cause-and-effect relationships. However, they may face ethical constraints and may not always be applicable to real-world situations. Researchers must carefully consider their research questions and choose the appropriate study design to ensure valid and reliable results.

Comparisons may contain inaccurate information about people, places, or facts. Please report any issues.

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Methodology

• Correlational Research | When & How to Use

Correlational Research | When & How to Use

Published on July 7, 2021 by Pritha Bhandari . Revised on June 22, 2023.

A correlational research design investigates relationships between variables without the researcher controlling or manipulating any of them.

A correlation reflects the strength and/or direction of the relationship between two (or more) variables. The direction of a correlation can be either positive or negative.

Table of contents

Correlational vs. experimental research, when to use correlational research, how to collect correlational data, how to analyze correlational data, correlation and causation, other interesting articles, frequently asked questions about correlational research.

Correlational and experimental research both use quantitative methods to investigate relationships between variables. But there are important differences in data collection methods and the types of conclusions you can draw.

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Correlational research is ideal for gathering data quickly from natural settings. That helps you generalize your findings to real-life situations in an externally valid way.

There are a few situations where correlational research is an appropriate choice.

To investigate non-causal relationships

You want to find out if there is an association between two variables, but you don’t expect to find a causal relationship between them.

Correlational research can provide insights into complex real-world relationships, helping researchers develop theories and make predictions.

To explore causal relationships between variables

You think there is a causal relationship between two variables, but it is impractical, unethical, or too costly to conduct experimental research that manipulates one of the variables.

Correlational research can provide initial indications or additional support for theories about causal relationships.

To test new measurement tools

You have developed a new instrument for measuring your variable, and you need to test its reliability or validity .

Correlational research can be used to assess whether a tool consistently or accurately captures the concept it aims to measure.

There are many different methods you can use in correlational research. In the social and behavioral sciences, the most common data collection methods for this type of research include surveys, observations , and secondary data.

It’s important to carefully choose and plan your methods to ensure the reliability and validity of your results. You should carefully select a representative sample so that your data reflects the population you’re interested in without research bias .

In survey research , you can use questionnaires to measure your variables of interest. You can conduct surveys online, by mail, by phone, or in person.

Surveys are a quick, flexible way to collect standardized data from many participants, but it’s important to ensure that your questions are worded in an unbiased way and capture relevant insights.

Naturalistic observation

Naturalistic observation is a type of field research where you gather data about a behavior or phenomenon in its natural environment.

This method often involves recording, counting, describing, and categorizing actions and events. Naturalistic observation can include both qualitative and quantitative elements, but to assess correlation, you collect data that can be analyzed quantitatively (e.g., frequencies, durations, scales, and amounts).

Naturalistic observation lets you easily generalize your results to real world contexts, and you can study experiences that aren’t replicable in lab settings. But data analysis can be time-consuming and unpredictable, and researcher bias may skew the interpretations.

Secondary data

Instead of collecting original data, you can also use data that has already been collected for a different purpose, such as official records, polls, or previous studies.

Using secondary data is inexpensive and fast, because data collection is complete. However, the data may be unreliable, incomplete or not entirely relevant, and you have no control over the reliability or validity of the data collection procedures.

After collecting data, you can statistically analyze the relationship between variables using correlation or regression analyses, or both. You can also visualize the relationships between variables with a scatterplot.

Different types of correlation coefficients and regression analyses are appropriate for your data based on their levels of measurement and distributions .

Correlation analysis

Using a correlation analysis, you can summarize the relationship between variables into a correlation coefficient : a single number that describes the strength and direction of the relationship between variables. With this number, you’ll quantify the degree of the relationship between variables.

The Pearson product-moment correlation coefficient , also known as Pearson’s r , is commonly used for assessing a linear relationship between two quantitative variables.

Correlation coefficients are usually found for two variables at a time, but you can use a multiple correlation coefficient for three or more variables.

Regression analysis

With a regression analysis , you can predict how much a change in one variable will be associated with a change in the other variable. The result is a regression equation that describes the line on a graph of your variables.

You can use this equation to predict the value of one variable based on the given value(s) of the other variable(s). It’s best to perform a regression analysis after testing for a correlation between your variables.

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It’s important to remember that correlation does not imply causation . Just because you find a correlation between two things doesn’t mean you can conclude one of them causes the other for a few reasons.

Directionality problem

If two variables are correlated, it could be because one of them is a cause and the other is an effect. But the correlational research design doesn’t allow you to infer which is which. To err on the side of caution, researchers don’t conclude causality from correlational studies.

Third variable problem

A confounding variable is a third variable that influences other variables to make them seem causally related even though they are not. Instead, there are separate causal links between the confounder and each variable.

In correlational research, there’s limited or no researcher control over extraneous variables . Even if you statistically control for some potential confounders, there may still be other hidden variables that disguise the relationship between your study variables.

Although a correlational study can’t demonstrate causation on its own, it can help you develop a causal hypothesis that’s tested in controlled experiments.

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

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

Research bias

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

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

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

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

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

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

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

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

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

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

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• Knowledge Base
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• Correlational Research | Guide, Design & Examples

Correlational Research | Guide, Design & Examples

Published on 5 May 2022 by Pritha Bhandari . Revised on 5 December 2022.

A correlational research design investigates relationships between variables without the researcher controlling or manipulating any of them.

A correlation reflects the strength and/or direction of the relationship between two (or more) variables. The direction of a correlation can be either positive or negative.

Table of contents

Correlational vs experimental research, when to use correlational research, how to collect correlational data, how to analyse correlational data, correlation and causation, frequently asked questions about correlational research.

Correlational and experimental research both use quantitative methods to investigate relationships between variables. But there are important differences in how data is collected and the types of conclusions you can draw.

Prevent plagiarism, run a free check.

Correlational research is ideal for gathering data quickly from natural settings. That helps you generalise your findings to real-life situations in an externally valid way.

There are a few situations where correlational research is an appropriate choice.

To investigate non-causal relationships

You want to find out if there is an association between two variables, but you don’t expect to find a causal relationship between them.

Correlational research can provide insights into complex real-world relationships, helping researchers develop theories and make predictions.

To explore causal relationships between variables

You think there is a causal relationship between two variables, but it is impractical, unethical, or too costly to conduct experimental research that manipulates one of the variables.

Correlational research can provide initial indications or additional support for theories about causal relationships.

To test new measurement tools

You have developed a new instrument for measuring your variable, and you need to test its reliability or validity .

Correlational research can be used to assess whether a tool consistently or accurately captures the concept it aims to measure.

There are many different methods you can use in correlational research. In the social and behavioural sciences, the most common data collection methods for this type of research include surveys, observations, and secondary data.

It’s important to carefully choose and plan your methods to ensure the reliability and validity of your results. You should carefully select a representative sample so that your data reflects the population you’re interested in without bias .

In survey research , you can use questionnaires to measure your variables of interest. You can conduct surveys online, by post, by phone, or in person.

Surveys are a quick, flexible way to collect standardised data from many participants, but it’s important to ensure that your questions are worded in an unbiased way and capture relevant insights.

Naturalistic observation

Naturalistic observation is a type of field research where you gather data about a behaviour or phenomenon in its natural environment.

This method often involves recording, counting, describing, and categorising actions and events. Naturalistic observation can include both qualitative and quantitative elements, but to assess correlation, you collect data that can be analysed quantitatively (e.g., frequencies, durations, scales, and amounts).

Naturalistic observation lets you easily generalise your results to real-world contexts, and you can study experiences that aren’t replicable in lab settings. But data analysis can be time-consuming and unpredictable, and researcher bias may skew the interpretations.

Secondary data

Instead of collecting original data, you can also use data that has already been collected for a different purpose, such as official records, polls, or previous studies.

Using secondary data is inexpensive and fast, because data collection is complete. However, the data may be unreliable, incomplete, or not entirely relevant, and you have no control over the reliability or validity of the data collection procedures.

After collecting data, you can statistically analyse the relationship between variables using correlation or regression analyses, or both. You can also visualise the relationships between variables with a scatterplot.

Different types of correlation coefficients and regression analyses are appropriate for your data based on their levels of measurement and distributions .

Correlation analysis

Using a correlation analysis, you can summarise the relationship between variables into a correlation coefficient : a single number that describes the strength and direction of the relationship between variables. With this number, you’ll quantify the degree of the relationship between variables.

The Pearson product-moment correlation coefficient, also known as Pearson’s r , is commonly used for assessing a linear relationship between two quantitative variables.

Correlation coefficients are usually found for two variables at a time, but you can use a multiple correlation coefficient for three or more variables.

Regression analysis

With a regression analysis , you can predict how much a change in one variable will be associated with a change in the other variable. The result is a regression equation that describes the line on a graph of your variables.

You can use this equation to predict the value of one variable based on the given value(s) of the other variable(s). It’s best to perform a regression analysis after testing for a correlation between your variables.

It’s important to remember that correlation does not imply causation . Just because you find a correlation between two things doesn’t mean you can conclude one of them causes the other, for a few reasons.

Directionality problem

If two variables are correlated, it could be because one of them is a cause and the other is an effect. But the correlational research design doesn’t allow you to infer which is which. To err on the side of caution, researchers don’t conclude causality from correlational studies.

Third variable problem

A confounding variable is a third variable that influences other variables to make them seem causally related even though they are not. Instead, there are separate causal links between the confounder and each variable.

In correlational research, there’s limited or no researcher control over extraneous variables . Even if you statistically control for some potential confounders, there may still be other hidden variables that disguise the relationship between your study variables.

Although a correlational study can’t demonstrate causation on its own, it can help you develop a causal hypothesis that’s tested in controlled experiments.

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

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

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

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

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

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

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

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

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

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2.2 Psychologists Use Descriptive, Correlational, and Experimental Research Designs to Understand Behavior

Learning objectives.

• Differentiate the goals of descriptive, correlational, and experimental research designs and explain the advantages and disadvantages of each.
• Explain the goals of descriptive research and the statistical techniques used to interpret it.
• Summarize the uses of correlational research and describe why correlational research cannot be used to infer causality.
• Review the procedures of experimental research and explain how it can be used to draw causal inferences.

Psychologists agree that if their ideas and theories about human behavior are to be taken seriously, they must be backed up by data. However, the research of different psychologists is designed with different goals in mind, and the different goals require different approaches. These varying approaches, summarized in Table 2.2 “Characteristics of the Three Research Designs” , are known as research designs . A research design is the specific method a researcher uses to collect, analyze, and interpret data . Psychologists use three major types of research designs in their research, and each provides an essential avenue for scientific investigation. Descriptive research is research designed to provide a snapshot of the current state of affairs . Correlational research is research designed to discover relationships among variables and to allow the prediction of future events from present knowledge . Experimental research is research in which initial equivalence among research participants in more than one group is created, followed by a manipulation of a given experience for these groups and a measurement of the influence of the manipulation . Each of the three research designs varies according to its strengths and limitations, and it is important to understand how each differs.

Table 2.2 Characteristics of the Three Research Designs

Stangor, C. (2011). Research methods for the behavioral sciences (4th ed.). Mountain View, CA: Cengage.

Descriptive Research: Assessing the Current State of Affairs

Descriptive research is designed to create a snapshot of the current thoughts, feelings, or behavior of individuals. This section reviews three types of descriptive research: case studies , surveys , and naturalistic observation .

Sometimes the data in a descriptive research project are based on only a small set of individuals, often only one person or a single small group. These research designs are known as case studies — descriptive records of one or more individual’s experiences and behavior . Sometimes case studies involve ordinary individuals, as when developmental psychologist Jean Piaget used his observation of his own children to develop his stage theory of cognitive development. More frequently, case studies are conducted on individuals who have unusual or abnormal experiences or characteristics or who find themselves in particularly difficult or stressful situations. The assumption is that by carefully studying individuals who are socially marginal, who are experiencing unusual situations, or who are going through a difficult phase in their lives, we can learn something about human nature.

Sigmund Freud was a master of using the psychological difficulties of individuals to draw conclusions about basic psychological processes. Freud wrote case studies of some of his most interesting patients and used these careful examinations to develop his important theories of personality. One classic example is Freud’s description of “Little Hans,” a child whose fear of horses the psychoanalyst interpreted in terms of repressed sexual impulses and the Oedipus complex (Freud (1909/1964).

Political polls reported in newspapers and on the Internet are descriptive research designs that provide snapshots of the likely voting behavior of a population.

Another well-known case study is Phineas Gage, a man whose thoughts and emotions were extensively studied by cognitive psychologists after a railroad spike was blasted through his skull in an accident. Although there is question about the interpretation of this case study (Kotowicz, 2007), it did provide early evidence that the brain’s frontal lobe is involved in emotion and morality (Damasio et al., 2005). An interesting example of a case study in clinical psychology is described by Rokeach (1964), who investigated in detail the beliefs and interactions among three patients with schizophrenia, all of whom were convinced they were Jesus Christ.

In other cases the data from descriptive research projects come in the form of a survey — a measure administered through either an interview or a written questionnaire to get a picture of the beliefs or behaviors of a sample of people of interest . The people chosen to participate in the research (known as the sample ) are selected to be representative of all the people that the researcher wishes to know about (the population ). In election polls, for instance, a sample is taken from the population of all “likely voters” in the upcoming elections.

The results of surveys may sometimes be rather mundane, such as “Nine out of ten doctors prefer Tymenocin,” or “The median income in Montgomery County is $36,712.” Yet other times (particularly in discussions of social behavior), the results can be shocking: “More than 40,000 people are killed by gunfire in the United States every year,” or “More than 60% of women between the ages of 50 and 60 suffer from depression.” Descriptive research is frequently used by psychologists to get an estimate of the prevalence (or incidence ) of psychological disorders.

A final type of descriptive research—known as naturalistic observation —is research based on the observation of everyday events . For instance, a developmental psychologist who watches children on a playground and describes what they say to each other while they play is conducting descriptive research, as is a biopsychologist who observes animals in their natural habitats. One example of observational research involves a systematic procedure known as the strange situation , used to get a picture of how adults and young children interact. The data that are collected in the strange situation are systematically coded in a coding sheet such as that shown in Table 2.3 “Sample Coding Form Used to Assess Child’s and Mother’s Behavior in the Strange Situation” .

Table 2.3 Sample Coding Form Used to Assess Child’s and Mother’s Behavior in the Strange Situation

The results of descriptive research projects are analyzed using descriptive statistics — numbers that summarize the distribution of scores on a measured variable . Most variables have distributions similar to that shown in Figure 2.5 “Height Distribution” , where most of the scores are located near the center of the distribution, and the distribution is symmetrical and bell-shaped. A data distribution that is shaped like a bell is known as a normal distribution .

Table 2.4 Height and Family Income for 25 Students

Figure 2.5 Height Distribution

The distribution of the heights of the students in a class will form a normal distribution. In this sample the mean ( M ) = 67.12 and the standard deviation ( s ) = 2.74.

A distribution can be described in terms of its central tendency —that is, the point in the distribution around which the data are centered—and its dispersion , or spread. The arithmetic average, or arithmetic mean , is the most commonly used measure of central tendency . It is computed by calculating the sum of all the scores of the variable and dividing this sum by the number of participants in the distribution (denoted by the letter N ). In the data presented in Figure 2.5 “Height Distribution” , the mean height of the students is 67.12 inches. The sample mean is usually indicated by the letter M .

In some cases, however, the data distribution is not symmetrical. This occurs when there are one or more extreme scores (known as outliers ) at one end of the distribution. Consider, for instance, the variable of family income (see Figure 2.6 “Family Income Distribution” ), which includes an outlier (a value of $3,800,000). In this case the mean is not a good measure of central tendency. Although it appears from Figure 2.6 “Family Income Distribution” that the central tendency of the family income variable should be around $70,000, the mean family income is actually $223,960. The single very extreme income has a disproportionate impact on the mean, resulting in a value that does not well represent the central tendency.

The median is used as an alternative measure of central tendency when distributions are not symmetrical. The median is the score in the center of the distribution, meaning that 50% of the scores are greater than the median and 50% of the scores are less than the median . In our case, the median household income ($73,000) is a much better indication of central tendency than is the mean household income ($223,960).

Figure 2.6 Family Income Distribution

The distribution of family incomes is likely to be nonsymmetrical because some incomes can be very large in comparison to most incomes. In this case the median or the mode is a better indicator of central tendency than is the mean.

A final measure of central tendency, known as the mode , represents the value that occurs most frequently in the distribution . You can see from Figure 2.6 “Family Income Distribution” that the mode for the family income variable is $93,000 (it occurs four times).

In addition to summarizing the central tendency of a distribution, descriptive statistics convey information about how the scores of the variable are spread around the central tendency. Dispersion refers to the extent to which the scores are all tightly clustered around the central tendency, like this:

Or they may be more spread out away from it, like this:

One simple measure of dispersion is to find the largest (the maximum ) and the smallest (the minimum ) observed values of the variable and to compute the range of the variable as the maximum observed score minus the minimum observed score. You can check that the range of the height variable in Figure 2.5 “Height Distribution” is 72 – 62 = 10. The standard deviation , symbolized as s , is the most commonly used measure of dispersion . Distributions with a larger standard deviation have more spread. The standard deviation of the height variable is s = 2.74, and the standard deviation of the family income variable is s = $745,337.

An advantage of descriptive research is that it attempts to capture the complexity of everyday behavior. Case studies provide detailed information about a single person or a small group of people, surveys capture the thoughts or reported behaviors of a large population of people, and naturalistic observation objectively records the behavior of people or animals as it occurs naturally. Thus descriptive research is used to provide a relatively complete understanding of what is currently happening.

Despite these advantages, descriptive research has a distinct disadvantage in that, although it allows us to get an idea of what is currently happening, it is usually limited to static pictures. Although descriptions of particular experiences may be interesting, they are not always transferable to other individuals in other situations, nor do they tell us exactly why specific behaviors or events occurred. For instance, descriptions of individuals who have suffered a stressful event, such as a war or an earthquake, can be used to understand the individuals’ reactions to the event but cannot tell us anything about the long-term effects of the stress. And because there is no comparison group that did not experience the stressful situation, we cannot know what these individuals would be like if they hadn’t had the stressful experience.

Correlational Research: Seeking Relationships Among Variables

In contrast to descriptive research, which is designed primarily to provide static pictures, correlational research involves the measurement of two or more relevant variables and an assessment of the relationship between or among those variables. For instance, the variables of height and weight are systematically related (correlated) because taller people generally weigh more than shorter people. In the same way, study time and memory errors are also related, because the more time a person is given to study a list of words, the fewer errors he or she will make. When there are two variables in the research design, one of them is called the predictor variable and the other the outcome variable . The research design can be visualized like this, where the curved arrow represents the expected correlation between the two variables:

Figure 2.2.2

One way of organizing the data from a correlational study with two variables is to graph the values of each of the measured variables using a scatter plot . As you can see in Figure 2.10 “Examples of Scatter Plots” , a scatter plot is a visual image of the relationship between two variables . A point is plotted for each individual at the intersection of his or her scores for the two variables. When the association between the variables on the scatter plot can be easily approximated with a straight line, as in parts (a) and (b) of Figure 2.10 “Examples of Scatter Plots” , the variables are said to have a linear relationship .

When the straight line indicates that individuals who have above-average values for one variable also tend to have above-average values for the other variable, as in part (a), the relationship is said to be positive linear . Examples of positive linear relationships include those between height and weight, between education and income, and between age and mathematical abilities in children. In each case people who score higher on one of the variables also tend to score higher on the other variable. Negative linear relationships , in contrast, as shown in part (b), occur when above-average values for one variable tend to be associated with below-average values for the other variable. Examples of negative linear relationships include those between the age of a child and the number of diapers the child uses, and between practice on and errors made on a learning task. In these cases people who score higher on one of the variables tend to score lower on the other variable.

Relationships between variables that cannot be described with a straight line are known as nonlinear relationships . Part (c) of Figure 2.10 “Examples of Scatter Plots” shows a common pattern in which the distribution of the points is essentially random. In this case there is no relationship at all between the two variables, and they are said to be independent . Parts (d) and (e) of Figure 2.10 “Examples of Scatter Plots” show patterns of association in which, although there is an association, the points are not well described by a single straight line. For instance, part (d) shows the type of relationship that frequently occurs between anxiety and performance. Increases in anxiety from low to moderate levels are associated with performance increases, whereas increases in anxiety from moderate to high levels are associated with decreases in performance. Relationships that change in direction and thus are not described by a single straight line are called curvilinear relationships .

Figure 2.10 Examples of Scatter Plots

Some examples of relationships between two variables as shown in scatter plots. Note that the Pearson correlation coefficient ( r ) between variables that have curvilinear relationships will likely be close to zero.

Adapted from Stangor, C. (2011). Research methods for the behavioral sciences (4th ed.). Mountain View, CA: Cengage.

The most common statistical measure of the strength of linear relationships among variables is the Pearson correlation coefficient , which is symbolized by the letter r . The value of the correlation coefficient ranges from r = –1.00 to r = +1.00. The direction of the linear relationship is indicated by the sign of the correlation coefficient. Positive values of r (such as r = .54 or r = .67) indicate that the relationship is positive linear (i.e., the pattern of the dots on the scatter plot runs from the lower left to the upper right), whereas negative values of r (such as r = –.30 or r = –.72) indicate negative linear relationships (i.e., the dots run from the upper left to the lower right). The strength of the linear relationship is indexed by the distance of the correlation coefficient from zero (its absolute value). For instance, r = –.54 is a stronger relationship than r = .30, and r = .72 is a stronger relationship than r = –.57. Because the Pearson correlation coefficient only measures linear relationships, variables that have curvilinear relationships are not well described by r , and the observed correlation will be close to zero.

It is also possible to study relationships among more than two measures at the same time. A research design in which more than one predictor variable is used to predict a single outcome variable is analyzed through multiple regression (Aiken & West, 1991). Multiple regression is a statistical technique, based on correlation coefficients among variables, that allows predicting a single outcome variable from more than one predictor variable . For instance, Figure 2.11 “Prediction of Job Performance From Three Predictor Variables” shows a multiple regression analysis in which three predictor variables are used to predict a single outcome. The use of multiple regression analysis shows an important advantage of correlational research designs—they can be used to make predictions about a person’s likely score on an outcome variable (e.g., job performance) based on knowledge of other variables.

Figure 2.11 Prediction of Job Performance From Three Predictor Variables

Multiple regression allows scientists to predict the scores on a single outcome variable using more than one predictor variable.

An important limitation of correlational research designs is that they cannot be used to draw conclusions about the causal relationships among the measured variables. Consider, for instance, a researcher who has hypothesized that viewing violent behavior will cause increased aggressive play in children. He has collected, from a sample of fourth-grade children, a measure of how many violent television shows each child views during the week, as well as a measure of how aggressively each child plays on the school playground. From his collected data, the researcher discovers a positive correlation between the two measured variables.

Although this positive correlation appears to support the researcher’s hypothesis, it cannot be taken to indicate that viewing violent television causes aggressive behavior. Although the researcher is tempted to assume that viewing violent television causes aggressive play,

there are other possibilities. One alternate possibility is that the causal direction is exactly opposite from what has been hypothesized. Perhaps children who have behaved aggressively at school develop residual excitement that leads them to want to watch violent television shows at home:

Although this possibility may seem less likely, there is no way to rule out the possibility of such reverse causation on the basis of this observed correlation. It is also possible that both causal directions are operating and that the two variables cause each other:

Still another possible explanation for the observed correlation is that it has been produced by the presence of a common-causal variable (also known as a third variable ). A common-causal variable is a variable that is not part of the research hypothesis but that causes both the predictor and the outcome variable and thus produces the observed correlation between them . In our example a potential common-causal variable is the discipline style of the children’s parents. Parents who use a harsh and punitive discipline style may produce children who both like to watch violent television and who behave aggressively in comparison to children whose parents use less harsh discipline:

In this case, television viewing and aggressive play would be positively correlated (as indicated by the curved arrow between them), even though neither one caused the other but they were both caused by the discipline style of the parents (the straight arrows). When the predictor and outcome variables are both caused by a common-causal variable, the observed relationship between them is said to be spurious . A spurious relationship is a relationship between two variables in which a common-causal variable produces and “explains away” the relationship . If effects of the common-causal variable were taken away, or controlled for, the relationship between the predictor and outcome variables would disappear. In the example the relationship between aggression and television viewing might be spurious because by controlling for the effect of the parents’ disciplining style, the relationship between television viewing and aggressive behavior might go away.

Common-causal variables in correlational research designs can be thought of as “mystery” variables because, as they have not been measured, their presence and identity are usually unknown to the researcher. Since it is not possible to measure every variable that could cause both the predictor and outcome variables, the existence of an unknown common-causal variable is always a possibility. For this reason, we are left with the basic limitation of correlational research: Correlation does not demonstrate causation. It is important that when you read about correlational research projects, you keep in mind the possibility of spurious relationships, and be sure to interpret the findings appropriately. Although correlational research is sometimes reported as demonstrating causality without any mention being made of the possibility of reverse causation or common-causal variables, informed consumers of research, like you, are aware of these interpretational problems.

In sum, correlational research designs have both strengths and limitations. One strength is that they can be used when experimental research is not possible because the predictor variables cannot be manipulated. Correlational designs also have the advantage of allowing the researcher to study behavior as it occurs in everyday life. And we can also use correlational designs to make predictions—for instance, to predict from the scores on their battery of tests the success of job trainees during a training session. But we cannot use such correlational information to determine whether the training caused better job performance. For that, researchers rely on experiments.

Experimental Research: Understanding the Causes of Behavior

The goal of experimental research design is to provide more definitive conclusions about the causal relationships among the variables in the research hypothesis than is available from correlational designs. In an experimental research design, the variables of interest are called the independent variable (or variables ) and the dependent variable . The independent variable in an experiment is the causing variable that is created (manipulated) by the experimenter . The dependent variable in an experiment is a measured variable that is expected to be influenced by the experimental manipulation . The research hypothesis suggests that the manipulated independent variable or variables will cause changes in the measured dependent variables. We can diagram the research hypothesis by using an arrow that points in one direction. This demonstrates the expected direction of causality:

Figure 2.2.3

Research Focus: Video Games and Aggression

Consider an experiment conducted by Anderson and Dill (2000). The study was designed to test the hypothesis that viewing violent video games would increase aggressive behavior. In this research, male and female undergraduates from Iowa State University were given a chance to play with either a violent video game (Wolfenstein 3D) or a nonviolent video game (Myst). During the experimental session, the participants played their assigned video games for 15 minutes. Then, after the play, each participant played a competitive game with an opponent in which the participant could deliver blasts of white noise through the earphones of the opponent. The operational definition of the dependent variable (aggressive behavior) was the level and duration of noise delivered to the opponent. The design of the experiment is shown in Figure 2.17 “An Experimental Research Design” .

Figure 2.17 An Experimental Research Design

Two advantages of the experimental research design are (1) the assurance that the independent variable (also known as the experimental manipulation) occurs prior to the measured dependent variable, and (2) the creation of initial equivalence between the conditions of the experiment (in this case by using random assignment to conditions).

Experimental designs have two very nice features. For one, they guarantee that the independent variable occurs prior to the measurement of the dependent variable. This eliminates the possibility of reverse causation. Second, the influence of common-causal variables is controlled, and thus eliminated, by creating initial equivalence among the participants in each of the experimental conditions before the manipulation occurs.

The most common method of creating equivalence among the experimental conditions is through random assignment to conditions , a procedure in which the condition that each participant is assigned to is determined through a random process, such as drawing numbers out of an envelope or using a random number table . Anderson and Dill first randomly assigned about 100 participants to each of their two groups (Group A and Group B). Because they used random assignment to conditions, they could be confident that, before the experimental manipulation occurred, the students in Group A were, on average, equivalent to the students in Group B on every possible variable, including variables that are likely to be related to aggression, such as parental discipline style, peer relationships, hormone levels, diet—and in fact everything else.

Then, after they had created initial equivalence, Anderson and Dill created the experimental manipulation—they had the participants in Group A play the violent game and the participants in Group B play the nonviolent game. Then they compared the dependent variable (the white noise blasts) between the two groups, finding that the students who had viewed the violent video game gave significantly longer noise blasts than did the students who had played the nonviolent game.

Anderson and Dill had from the outset created initial equivalence between the groups. This initial equivalence allowed them to observe differences in the white noise levels between the two groups after the experimental manipulation, leading to the conclusion that it was the independent variable (and not some other variable) that caused these differences. The idea is that the only thing that was different between the students in the two groups was the video game they had played.

Despite the advantage of determining causation, experiments do have limitations. One is that they are often conducted in laboratory situations rather than in the everyday lives of people. Therefore, we do not know whether results that we find in a laboratory setting will necessarily hold up in everyday life. Second, and more important, is that some of the most interesting and key social variables cannot be experimentally manipulated. If we want to study the influence of the size of a mob on the destructiveness of its behavior, or to compare the personality characteristics of people who join suicide cults with those of people who do not join such cults, these relationships must be assessed using correlational designs, because it is simply not possible to experimentally manipulate these variables.

Key Takeaways

• Descriptive, correlational, and experimental research designs are used to collect and analyze data.
• Descriptive designs include case studies, surveys, and naturalistic observation. The goal of these designs is to get a picture of the current thoughts, feelings, or behaviors in a given group of people. Descriptive research is summarized using descriptive statistics.
• Correlational research designs measure two or more relevant variables and assess a relationship between or among them. The variables may be presented on a scatter plot to visually show the relationships. The Pearson Correlation Coefficient ( r ) is a measure of the strength of linear relationship between two variables.
• Common-causal variables may cause both the predictor and outcome variable in a correlational design, producing a spurious relationship. The possibility of common-causal variables makes it impossible to draw causal conclusions from correlational research designs.
• Experimental research involves the manipulation of an independent variable and the measurement of a dependent variable. Random assignment to conditions is normally used to create initial equivalence between the groups, allowing researchers to draw causal conclusions.

Exercises and Critical Thinking

• There is a negative correlation between the row that a student sits in in a large class (when the rows are numbered from front to back) and his or her final grade in the class. Do you think this represents a causal relationship or a spurious relationship, and why?
• Think of two variables (other than those mentioned in this book) that are likely to be correlated, but in which the correlation is probably spurious. What is the likely common-causal variable that is producing the relationship?
• Imagine a researcher wants to test the hypothesis that participating in psychotherapy will cause a decrease in reported anxiety. Describe the type of research design the investigator might use to draw this conclusion. What would be the independent and dependent variables in the research?

Aiken, L., & West, S. (1991). Multiple regression: Testing and interpreting interactions . Newbury Park, CA: Sage.

Ainsworth, M. S., Blehar, M. C., Waters, E., & Wall, S. (1978). Patterns of attachment: A psychological study of the strange situation . Hillsdale, NJ: Lawrence Erlbaum Associates.

Anderson, C. A., & Dill, K. E. (2000). Video games and aggressive thoughts, feelings, and behavior in the laboratory and in life. Journal of Personality and Social Psychology, 78 (4), 772–790.

Damasio, H., Grabowski, T., Frank, R., Galaburda, A. M., Damasio, A. R., Cacioppo, J. T., & Berntson, G. G. (2005). The return of Phineas Gage: Clues about the brain from the skull of a famous patient. In Social neuroscience: Key readings. (pp. 21–28). New York, NY: Psychology Press.

Freud, S. (1964). Analysis of phobia in a five-year-old boy. In E. A. Southwell & M. Merbaum (Eds.), Personality: Readings in theory and research (pp. 3–32). Belmont, CA: Wadsworth. (Original work published 1909)

Kotowicz, Z. (2007). The strange case of Phineas Gage. History of the Human Sciences, 20 (1), 115–131.

Rokeach, M. (1964). The three Christs of Ypsilanti: A psychological study . New York, NY: Knopf.

Introduction to Psychology Copyright © 2015 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Non-Experimental Research

29 Correlational Research

Learning objectives.

• Define correlational research and give several examples.
• Explain why a researcher might choose to conduct correlational research rather than experimental research or another type of non-experimental research.
• Interpret the strength and direction of different correlation coefficients.
• Explain why correlation does not imply causation.

What Is Correlational Research?

Correlational research is a type of non-experimental research in which the researcher measures two variables (binary or continuous) and assesses the statistical relationship (i.e., the correlation) between them with little or no effort to control extraneous variables. There are many reasons that researchers interested in statistical relationships between variables would choose to conduct a correlational study rather than an experiment. The first is that they do not believe that the statistical relationship is a causal one or are not interested in causal relationships. Recall two goals of science are to describe and to predict and the correlational research strategy allows researchers to achieve both of these goals. Specifically, this strategy can be used to describe the strength and direction of the relationship between two variables and if there is a relationship between the variables then the researchers can use scores on one variable to predict scores on the other (using a statistical technique called regression, which is discussed further in the section on Complex Correlation in this chapter).

Another reason that researchers would choose to use a correlational study rather than an experiment is that the statistical relationship of interest is thought to be causal, but the researcher  cannot manipulate the independent variable because it is impossible, impractical, or unethical. For example, while a researcher might be interested in the relationship between the frequency people use cannabis and their memory abilities they cannot ethically manipulate the frequency that people use cannabis. As such, they must rely on the correlational research strategy; they must simply measure the frequency that people use cannabis and measure their memory abilities using a standardized test of memory and then determine whether the frequency people use cannabis is statistically related to memory test performance. 

Correlation is also used to establish the reliability and validity of measurements. For example, a researcher might evaluate the validity of a brief extraversion test by administering it to a large group of participants along with a longer extraversion test that has already been shown to be valid. This researcher might then check to see whether participants’ scores on the brief test are strongly correlated with their scores on the longer one. Neither test score is thought to cause the other, so there is no independent variable to manipulate. In fact, the terms  independent variable  and dependent variabl e  do not apply to this kind of research.

Another strength of correlational research is that it is often higher in external validity than experimental research. Recall there is typically a trade-off between internal validity and external validity. As greater controls are added to experiments, internal validity is increased but often at the expense of external validity as artificial conditions are introduced that do not exist in reality. In contrast, correlational studies typically have low internal validity because nothing is manipulated or controlled but they often have high external validity. Since nothing is manipulated or controlled by the experimenter the results are more likely to reflect relationships that exist in the real world.

Finally, extending upon this trade-off between internal and external validity, correlational research can help to provide converging evidence for a theory. If a theory is supported by a true experiment that is high in internal validity as well as by a correlational study that is high in external validity then the researchers can have more confidence in the validity of their theory. As a concrete example, correlational studies establishing that there is a relationship between watching violent television and aggressive behavior have been complemented by experimental studies confirming that the relationship is a causal one (Bushman & Huesmann, 2001) [1] .

Does Correlational Research Always Involve Quantitative Variables?

A common misconception among beginning researchers is that correlational research must involve two quantitative variables, such as scores on two extraversion tests or the number of daily hassles and number of symptoms people have experienced. However, the defining feature of correlational research is that the two variables are measured—neither one is manipulated—and this is true regardless of whether the variables are quantitative or categorical. Imagine, for example, that a researcher administers the Rosenberg Self-Esteem Scale to 50 American college students and 50 Japanese college students. Although this “feels” like a between-subjects experiment, it is a correlational study because the researcher did not manipulate the students’ nationalities. The same is true of the study by Cacioppo and Petty comparing college faculty and factory workers in terms of their need for cognition. It is a correlational study because the researchers did not manipulate the participants’ occupations.

Figure 6.2 shows data from a hypothetical study on the relationship between whether people make a daily list of things to do (a “to-do list”) and stress. Notice that it is unclear whether this is an experiment or a correlational study because it is unclear whether the independent variable was manipulated. If the researcher randomly assigned some participants to make daily to-do lists and others not to, then it is an experiment. If the researcher simply asked participants whether they made daily to-do lists, then it is a correlational study. The distinction is important because if the study was an experiment, then it could be concluded that making the daily to-do lists reduced participants’ stress. But if it was a correlational study, it could only be concluded that these variables are statistically related. Perhaps being stressed has a negative effect on people’s ability to plan ahead (the directionality problem). Or perhaps people who are more conscientious are more likely to make to-do lists and less likely to be stressed (the third-variable problem). The crucial point is that what defines a study as experimental or correlational is not the variables being studied, nor whether the variables are quantitative or categorical, nor the type of graph or statistics used to analyze the data. What defines a study is how the study is conducted.

Data Collection in Correlational Research

Again, the defining feature of correlational research is that neither variable is manipulated. It does not matter how or where the variables are measured. A researcher could have participants come to a laboratory to complete a computerized backward digit span task and a computerized risky decision-making task and then assess the relationship between participants’ scores on the two tasks. Or a researcher could go to a shopping mall to ask people about their attitudes toward the environment and their shopping habits and then assess the relationship between these two variables. Both of these studies would be correlational because no independent variable is manipulated. 

Correlations Between Quantitative Variables

Correlations between quantitative variables are often presented using scatterplots . Figure 6.3 shows some hypothetical data on the relationship between the amount of stress people are under and the number of physical symptoms they have. Each point in the scatterplot represents one person’s score on both variables. For example, the circled point in Figure 6.3 represents a person whose stress score was 10 and who had three physical symptoms. Taking all the points into account, one can see that people under more stress tend to have more physical symptoms. This is a good example of a positive relationship , in which higher scores on one variable tend to be associated with higher scores on the other. In other words, they move in the same direction, either both up or both down. A negative relationship is one in which higher scores on one variable tend to be associated with lower scores on the other. In other words, they move in opposite directions. There is a negative relationship between stress and immune system functioning, for example, because higher stress is associated with lower immune system functioning.

The strength of a correlation between quantitative variables is typically measured using a statistic called  Pearson’s Correlation Coefficient (or Pearson's  r ) . As Figure 6.4 shows, Pearson’s r ranges from −1.00 (the strongest possible negative relationship) to +1.00 (the strongest possible positive relationship). A value of 0 means there is no relationship between the two variables. When Pearson’s  r  is 0, the points on a scatterplot form a shapeless “cloud.” As its value moves toward −1.00 or +1.00, the points come closer and closer to falling on a single straight line. Correlation coefficients near ±.10 are considered small, values near ± .30 are considered medium, and values near ±.50 are considered large. Notice that the sign of Pearson’s  r  is unrelated to its strength. Pearson’s  r  values of +.30 and −.30, for example, are equally strong; it is just that one represents a moderate positive relationship and the other a moderate negative relationship. With the exception of reliability coefficients, most correlations that we find in Psychology are small or moderate in size. The website http://rpsychologist.com/d3/correlation/ , created by Kristoffer Magnusson, provides an excellent interactive visualization of correlations that permits you to adjust the strength and direction of a correlation while witnessing the corresponding changes to the scatterplot.

There are two common situations in which the value of Pearson’s  r  can be misleading. Pearson’s  r  is a good measure only for linear relationships, in which the points are best approximated by a straight line. It is not a good measure for nonlinear relationships, in which the points are better approximated by a curved line. Figure 6.5, for example, shows a hypothetical relationship between the amount of sleep people get per night and their level of depression. In this example, the line that best approximates the points is a curve—a kind of upside-down “U”—because people who get about eight hours of sleep tend to be the least depressed. Those who get too little sleep and those who get too much sleep tend to be more depressed. Even though Figure 6.5 shows a fairly strong relationship between depression and sleep, Pearson’s  r  would be close to zero because the points in the scatterplot are not well fit by a single straight line. This means that it is important to make a scatterplot and confirm that a relationship is approximately linear before using Pearson’s  r . Nonlinear relationships are fairly common in psychology, but measuring their strength is beyond the scope of this book.

The other common situations in which the value of Pearson’s  r  can be misleading is when one or both of the variables have a limited range in the sample relative to the population. This problem is referred to as  restriction of range . Assume, for example, that there is a strong negative correlation between people’s age and their enjoyment of hip hop music as shown by the scatterplot in Figure 6.6. Pearson’s  r  here is −.77. However, if we were to collect data only from 18- to 24-year-olds—represented by the shaded area of Figure 6.6—then the relationship would seem to be quite weak. In fact, Pearson’s  r  for this restricted range of ages is 0. It is a good idea, therefore, to design studies to avoid restriction of range. For example, if age is one of your primary variables, then you can plan to collect data from people of a wide range of ages. Because restriction of range is not always anticipated or easily avoidable, however, it is good practice to examine your data for possible restriction of range and to interpret Pearson’s  r  in light of it. (There are also statistical methods to correct Pearson’s  r  for restriction of range, but they are beyond the scope of this book).

Correlation Does Not Imply Causation

You have probably heard repeatedly that “Correlation does not imply causation.” An amusing example of this comes from a 2012 study that showed a positive correlation (Pearson’s r = 0.79) between the per capita chocolate consumption of a nation and the number of Nobel prizes awarded to citizens of that nation [2] . It seems clear, however, that this does not mean that eating chocolate causes people to win Nobel prizes, and it would not make sense to try to increase the number of Nobel prizes won by recommending that parents feed their children more chocolate.

There are two reasons that correlation does not imply causation. The first is called the  directionality problem . Two variables,  X  and  Y , can be statistically related because X  causes  Y  or because  Y  causes  X . Consider, for example, a study showing that whether or not people exercise is statistically related to how happy they are—such that people who exercise are happier on average than people who do not. This statistical relationship is consistent with the idea that exercising causes happiness, but it is also consistent with the idea that happiness causes exercise. Perhaps being happy gives people more energy or leads them to seek opportunities to socialize with others by going to the gym. The second reason that correlation does not imply causation is called the  third-variable problem . Two variables,  X  and  Y , can be statistically related not because  X  causes  Y , or because  Y  causes  X , but because some third variable,  Z , causes both  X  and  Y . For example, the fact that nations that have won more Nobel prizes tend to have higher chocolate consumption probably reflects geography in that European countries tend to have higher rates of per capita chocolate consumption and invest more in education and technology (once again, per capita) than many other countries in the world. Similarly, the statistical relationship between exercise and happiness could mean that some third variable, such as physical health, causes both of the others. Being physically healthy could cause people to exercise and cause them to be happier. Correlations that are a result of a third-variable are often referred to as  spurious correlations .

Some excellent and amusing examples of spurious correlations can be found at http://www.tylervigen.com  (Figure 6.7  provides one such example).

“Lots of Candy Could Lead to Violence”

Although researchers in psychology know that correlation does not imply causation, many journalists do not. One website about correlation and causation, http://jonathan.mueller.faculty.noctrl.edu/100/correlation_or_causation.htm , links to dozens of media reports about real biomedical and psychological research. Many of the headlines suggest that a causal relationship has been demonstrated when a careful reading of the articles shows that it has not because of the directionality and third-variable problems.

One such article is about a study showing that children who ate candy every day were more likely than other children to be arrested for a violent offense later in life. But could candy really “lead to” violence, as the headline suggests? What alternative explanations can you think of for this statistical relationship? How could the headline be rewritten so that it is not misleading?

As you have learned by reading this book, there are various ways that researchers address the directionality and third-variable problems. The most effective is to conduct an experiment. For example, instead of simply measuring how much people exercise, a researcher could bring people into a laboratory and randomly assign half of them to run on a treadmill for 15 minutes and the rest to sit on a couch for 15 minutes. Although this seems like a minor change to the research design, it is extremely important. Now if the exercisers end up in more positive moods than those who did not exercise, it cannot be because their moods affected how much they exercised (because it was the researcher who used random assignment to determine how much they exercised). Likewise, it cannot be because some third variable (e.g., physical health) affected both how much they exercised and what mood they were in. Thus experiments eliminate the directionality and third-variable problems and allow researchers to draw firm conclusions about causal relationships.

Media Attributions

• Nicholas Cage and Pool Drownings  © Tyler Viegen is licensed under a  CC BY (Attribution)  license
• Bushman, B. J., & Huesmann, L. R. (2001). Effects of televised violence on aggression. In D. Singer & J. Singer (Eds.), Handbook of children and the media (pp. 223–254). Thousand Oaks, CA: Sage. ↵
• Messerli, F. H. (2012). Chocolate consumption, cognitive function, and Nobel laureates. New England Journal of Medicine, 367 , 1562-1564. ↵

A graph that presents correlations between two quantitative variables, one on the x-axis and one on the y-axis. Scores are plotted at the intersection of the values on each axis.

A relationship in which higher scores on one variable tend to be associated with higher scores on the other.

A relationship in which higher scores on one variable tend to be associated with lower scores on the other.

A statistic that measures the strength of a correlation between quantitative variables.

When one or both variables have a limited range in the sample relative to the population, making the value of the correlation coefficient misleading.

The problem where two variables, X  and  Y , are statistically related either because X  causes  Y, or because  Y  causes  X , and thus the causal direction of the effect cannot be known.

Two variables, X and Y, can be statistically related not because X causes Y, or because Y causes X, but because some third variable, Z, causes both X and Y.

Correlations that are a result not of the two variables being measured, but rather because of a third, unmeasured, variable that affects both of the measured variables.

Research Methods in Psychology Copyright © 2019 by Rajiv S. Jhangiani, I-Chant A. Chiang, Carrie Cuttler, & Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Module 1: Lifespan Development

Correlational and experimental research, learning outcomes.

• Explain correlational research
• Describe the value of experimental research

Correlational Research

When scientists passively observe and measure phenomena it is called correlational research . Here, researchers do not intervene and change behavior, as they do in experiments. In correlational research, the goal is to identify patterns of relationships, but not cause and effect. Importantly, with correlational research, you can examine only two variables at a time, no more and no less.

So, what if you wanted to test whether spending money on others is related to happiness, but you don’t have $20 to give to each participant in order to have them spend it for your experiment? You could use a correlational design—which is exactly what Professor Elizabeth Dunn (2008) at the University of British Columbia did when she conducted research on spending and happiness. She asked people how much of their income they spent on others or donated to charity, and later she asked them how happy they were. Do you think these two variables were related? Yes, they were! The more money people reported spending on others, the happier they were.

Understanding Correlation

To find out how well two variables correlate, you can plot the relationship between the two scores on what is known as a scatterplot . In the scatterplot, each dot represents a data point. (In this case it’s individuals, but it could be some other unit.) Importantly, each dot provides us with two pieces of information—in this case, information about how good the person rated the past month (x-axis) and how happy the person felt in the past month (y-axis). Which variable is plotted on which axis does not matter.

Figure 1 . Scatterplot of the association between happiness and ratings of the past month, a positive correlation (r = .81). Each dot represents an individual.

The association between two variables can be summarized statistically using the correlation coefficient (abbreviated as r). A correlation coefficient provides information about the direction and strength of the association between two variables. For the example above, the direction of the association is positive. This means that people who perceived the past month as being good reported feeling more happy, whereas people who perceived the month as being bad reported feeling less happy.

With a positive correlation , the two variables go up or down together. In a scatterplot, the dots form a pattern that extends from the bottom left to the upper right (just as they do in Figure 1). The r value for a positive correlation is indicated by a positive number (although, the positive sign is usually omitted). Here, the r value is .81.

A negative correlation is one in which the two variables move in opposite directions. That is, as one variable goes up, the other goes down. Figure 2 shows the association between the average height of males in a country (y-axis) and the pathogen prevalence (or commonness of disease; x-axis) of that country. In this scatterplot, each dot represents a country. Notice how the dots extend from the top left to the bottom right. What does this mean in real-world terms? It means that people are shorter in parts of the world where there is more disease. The r value for a negative correlation is indicated by a negative number—that is, it has a minus (–) sign in front of it. Here, it is –.83.

Figure 2 . Scatterplot showing the association between average male height and pathogen prevalence, a negative correlation (r = –.83). Each dot represents a country (Chiao, 2009).

The strength of a correlation has to do with how well the two variables align. Recall that in Professor Dunn’s correlational study, spending on others positively correlated with happiness; the more money people reported spending on others, the happier they reported to be. At this point you may be thinking to yourself, I know a very generous person who gave away lots of money to other people but is miserable! Or maybe you know of a very stingy person who is happy as can be. Yes, there might be exceptions. If an association has many exceptions, it is considered a weak correlation. If an association has few or no exceptions, it is considered a strong correlation. A strong correlation is one in which the two variables always, or almost always, go together. In the example of happiness and how good the month has been, the association is strong. The stronger a correlation is, the tighter the dots in the scatterplot will be arranged along a sloped line.

The r value of a strong correlation will have a high absolute value (a perfect correlation has an absolute value of the whole number one, or 1.00). In other words, you disregard whether there is a negative sign in front of the r value, and just consider the size of the numerical value itself. If the absolute value is large, it is a strong correlation. A weak correlation is one in which the two variables correspond some of the time, but not most of the time. Figure 3 shows the relation between valuing happiness and grade point average (GPA). People who valued happiness more tended to earn slightly lower grades, but there were lots of exceptions to this. The r value for a weak correlation will have a low absolute value. If two variables are so weakly related as to be unrelated, we say they are uncorrelated, and the r value will be zero or very close to zero. In the previous example, is the correlation between height and pathogen prevalence strong? Compared to Figure 3, the dots in Figure 2 are tighter and less dispersed. The absolute value of –.83 is large (closer to one than to zero). Therefore, it is a strong negative correlation.

Figure 3 . Scatterplot showing the association between valuing happiness and GPA, a weak negative correlation (r = –.32). Each dot represents an individual.

Problems with correlation

If generosity and happiness are positively correlated, should we conclude that being generous causes happiness? Similarly, if height and pathogen prevalence are negatively correlated, should we conclude that disease causes shortness? From a correlation alone, we can’t be certain. For example, in the first case, it may be that happiness causes generosity, or that generosity causes happiness. Or, a third variable might cause both happiness and generosity, creating the illusion of a direct link between the two. For example, wealth could be the third variable that causes both greater happiness and greater generosity. This is why correlation does not mean causation—an often repeated phrase among psychologists.

In this video, University of Pennsylvania psychologist and bestselling author, Angela Duckworth describes the correlational research that informed her understanding of grit.

You can view the transcript for “Grit: The power of passion and perseverance | Angela Lee Duckworth” here (opens in new window) .

link to learning

Click through this interactive presentation to examine actual research studies.

Experimental Research

Experiments  are designed to test  hypotheses  (or specific statements about the relationship between  variables ) in a controlled setting in efforts to explain how certain factors or events produce outcomes. A variable is anything that changes in value. Concepts are  operationalized  or transformed into variables in research which means that the researcher must specify exactly what is going to be measured in the study. For example, if we are interested in studying marital satisfaction, we have to specify what marital satisfaction really means or what we are going to use as an indicator of marital satisfaction. What is something measurable that would indicate some level of marital satisfaction? Would it be the amount of time couples spend together each day? Or eye contact during a discussion about money? Or maybe a subject’s score on a marital satisfaction scale? Each of these is measurable but these may not be equally valid or accurate indicators of marital satisfaction. What do you think? These are the kinds of considerations researchers must make when working through the design.

The experimental method is the only research method that can measure cause and effect relationships between variables. Three conditions must be met in order to establish cause and effect. Experimental designs are useful in meeting these conditions:

• The independent and dependent variables must be related.  In other words, when one is altered, the other changes in response. The independent variable is something altered or introduced by the researcher; sometimes thought of as the treatment or intervention. The dependent variable is the outcome or the factor affected by the introduction of the independent variable; the dependent variable  depends on the independent variable. For example, if we are looking at the impact of exercise on stress levels, the independent variable would be exercise; the dependent variable would be stress.
• The cause must come before the effect.  Experiments measure subjects on the dependent variable before exposing them to the independent variable (establishing a baseline). So we would measure the subjects’ level of stress before introducing exercise and then again after the exercise to see if there has been a change in stress levels. (Observational and survey research does not always allow us to look at the timing of these events which makes understanding causality problematic with these methods.)
• The cause must be isolated.  The researcher must ensure that no outside, perhaps unknown variables, are actually causing the effect we see. The experimental design helps make this possible. In an experiment, we would make sure that our subjects’ diets were held constant throughout the exercise program. Otherwise, the diet might really be creating a change in stress level rather than exercise.

A basic experimental design involves beginning with a sample (or subset of a population) and randomly assigning subjects to one of two groups: the  experimental group or the control group . Ideally, to prevent bias, the participants would be blind to their condition (not aware of which group they are in) and the researchers would also be blind to each participant’s condition (referred to as “ double blind “). The experimental group is the group that is going to be exposed to an independent variable or condition the researcher is introducing as a potential cause of an event. The control group is going to be used for comparison and is going to have the same experience as the experimental group but will not be exposed to the independent variable. This helps address the placebo effect, which is that a group may expect changes to happen just by participating. After exposing the experimental group to the independent variable, the two groups are measured again to see if a change has occurred. If so, we are in a better position to suggest that the independent variable caused the change in the dependent variable . The basic experimental model looks like this:

The major advantage of the experimental design is that of helping to establish cause and effect relationships. A disadvantage of this design is the difficulty of translating much of what concerns us about human behavior into a laboratory setting.

Link to Learning

Have you ever wondered why people make decisions that seem to be in opposition to their longterm best interest? In Eldar Shafir’s TED Talk Living Under Scarcity , Shafir describes a series of experiments that shed light on how scarcity (real or perceived) affects our decisions.

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Correlational Research

Learning objectives.

• Explain correlational research, including what a correlation coefficient tells us about the relationship between variables

One of the primary methods used to study abnormal behavior is the correlational method.  Correlation means that there is a relationship between two or more variables (such between the variables of negative thinking and depressive symptoms), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from negative one to positive one that indicates the strength and direction of the relationship between variables. The association between two variables can be summarized statistically using the correlation coefficient (abbreviated as  r ).

The number portion of the correlation coefficient indicates the strength of the relationship. The closer the number is to one (be it negative or positive), the more strongly related the variables are, and the more predictable changes in one variable will be as the other variable changes. The closer the number is to zero, the weaker the relationship, and the less predictable the relationships between the variables becomes. For instance, a correlation coefficient of 0.9 indicates a far stronger relationship than a correlation coefficient of 0.3. If the variables are not related to one another at all, the correlation coefficient is zero. The example above about negative thinking and depressive symptoms is an example of two variables that we might expect to have a relationship to each other.  When higher values in one variable (negative thinking) are associated with higher values in the other variable (depressive symptoms), there is a  positive correlation  between the variables.

The sign—positive or negative—of the correlation coefficient indicates the direction of the relationship.  Positive correlations carry positive signs; negative correlations carry negative signs.  A positive correlation means that the variables move in the same direction. Put another way, it means that as one variable increases so does the other, and conversely, when one variable decreases so does the other. A negative correlation means that the variables move in opposite directions. If two variables are negatively correlated, a decrease in one variable is associated with an increase in the other and vice versa.

Other examples of positive correlations are the relationship between depression and disturbance in normal sleep patterns. One might expect then that scores on a measure of depression would be positively correlated with scores on a measure of sleep disturbances.

One might expect a negative correlation to exist between  between depression and self-esteem.  The more depressed people are, the lower their scores are on the Rosenberg self-esteem scale (RSES), a self-esteem measure widely used in social-science research.  Keep in mind that a negative correlation is not the same as no correlation. For example, we would probably find no correlation between  depression  and someone’s   height. 

In correlational research,  scientists passively observe and measure phenomena.    Here, we do not intervene and change behavior, as we do in experiments. In correlational research, we identify patterns of relationships, but we usually cannot infer what causes what. Importantly, with correlational research, you can examine only two variables at a time, no more and no less.

As mentioned earlier, correlations have predictive value. So, what if you wanted to test whether spending on others is related to happiness, but you don’t have $20 to give to each participant? You could use a correlational design—which is exactly what Professor Dunn did, too. She asked people how much of their income they spent on others or donated to charity, and later she asked them how happy they were. Do you think these two variables were related? Yes, they were! The more money people reported spending on others, the happier they were.

More Details about the Correlation

To find out how well two variables correspond, we can plot the relationship between the two scores on what is known as a scatterplot (Figure 1). In the scatterplot, each dot represents a data point. (In this case it’s individuals, but it could be some other unit.) Importantly, each dot provides us with two pieces of information—in this case, information about how good the person rated the past month ( x -axis) and how happy the person felt in the past month ( y -axis). Which variable is plotted on which axis does not matter.

For the example above, the direction of the association is positive. This means that people who perceived the past month as being good reported feeling more happy, whereas people who perceived the month as being bad reported feeling less happy.

In a scatterplot, the dots form a pattern that extends from the bottom left to the upper right (just as they do in Figure 1). The  r  value for a positive correlation is indicated by a positive number (although, the positive sign is usually omitted). Here, the  r  value is 0.81.

Figure 2 shows a  negative correlation,   the association between the average height of males in a country ( y -axis) and the pathogen prevalence, or commonness of disease, of that country ( x -axis). In this scatterplot, each dot represents a country. Notice how the dots extend from the top left to the bottom right. What does this mean in real-world terms? It means that people are shorter in parts of the world where there is more disease. The  r  value for a negative correlation is indicated by a negative number—that is, it has a minus (−) sign in front of it. Here, it is −0.83.

The strength of a correlation has to do with how well the two variables align. Recall that in Professor Dunn’s correlational study, spending on others positively correlated with happiness: the more money people reported spending on others, the happier they reported to be. At this point, you may be thinking to yourself, “I know a very generous person who gave away lots of money to other people but is miserable!” Or maybe you know of a very stingy person who is happy as can be. Yes, there might be exceptions. If an association has many exceptions, it is considered a weak correlation. If an association has few or no exceptions, it is considered a strong correlation. A strong correlation is one in which the two variables always, or almost always, go together. In the example of happiness and how good the month has been, the association is strong. The stronger a correlation is, the tighter the dots in the scatterplot will be arranged along a sloped line. [1]

Problems with correlation

If generosity and happiness are positively correlated, should we conclude that being generous causes happiness? Similarly, if height and pathogen prevalence are negatively correlated, should we conclude that disease causes shortness? From a correlation alone, we can’t be certain. For example, in the first case it may be that happiness causes generosity, or that generosity causes happiness. Or, a third variable might cause both happiness  and  generosity, creating the illusion of a direct link between the two. For example, wealth could be the third variable that causes both greater happiness and greater generosity. This is why correlation does not mean causation—an often repeated phrase among psychologists. [2]

Correlation Does Not Indicate Causation

Correlational research is useful because it allows us to discover the strength and direction of relationships that exist between two variables. However, correlation is limited because establishing the existence of a relationship tells us little about cause and effect . While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable , is actually causing the systematic movement in our variables of interest. In the  depression and negative thinking   example mentioned earlier, stress  is a confounding variable that could account for the relationship between the two variables.   

Even when we cannot point to clear confounding variables, we should not assume that a correlation between two variables implies that one variable causes changes in another. This can be frustrating when a cause-and-effect relationship seems clear and intuitive. Think back to our example about the relationship between depression and disturbance in normal sleep patterns.  It seems reasonable to assume that s leep disturbance might cause a higher score on a measure of depression, just as a high degree of depression might cause more disturbed sleep patterns , but if we were limited to  correlational research , we would be overstepping our bounds by making this assumption.  Both depression and sleep disturbance could be due to an underlying physiological disorder o r any to other third variable that you have not measured .

Unfortunately, people mistakenly make claims of causation as a function of correlations all the time.   While correlational research is invaluable in identifying relationships among variables, a major limitation is the inability to establish causality.  The correlational method does not involve manipulation of the variables of interest. In the previous example, the experimenter does not manipulate people’s depressive symptoms or sleep patterns.  Psychologists want to make statements about cause and effect, but the only way to do that is to conduct an experiment to answer a research question. The next section describes how  investigators use experimental methods in which the experimenter manipulates one or more variables of interest and observes their effects on other variables or outcomes under controlled conditions.

In this video, we discuss one of the best methods psychologists have for predicting behaviors: correlation. But does that mean that a behavior is absolutely going to happen? Let’s find out!

You can view the transcript for “#5 Correlation vs. Causation – Psy 101” here (opens in new window) .

Think It Over

Consider why correlational research is often used in the study of abnormal behavior. If correlational designs do not demonstrate causation, why do researchers make causal claims regarding their results? Are there instances when correlational results could demonstrate causation?

cause-and-effect relationship:  changes in one variable cause the changes in the other variable; can be determined only through an experimental research design

confirmation bias:  tendency to ignore evidence that disproves ideas or beliefs

confounding variable:  unanticipated outside factor that affects both variables of interest,\; often gives the false impression that changes in one variable causes changes in the other variable, when, in actuality, the outside factor causes changes in both variables

correlation: the relationship between two or more variables; when two variables are correlated, one variable changes as the other does

correlation coefficient:  number from -1 to +1, indicating the strength and direction of the relationship between variables, and usually represented by r

negative correlation:  two variables change in different directions, with one becoming larger as the other becomes smaller; a negative correlation is not the same thing as no correlation

positive correlation:  two variables change in the same direction, both becoming either larger or smaller

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Correlational Research in Psychology: Definition and How It Works

Categories Research Methods

Correlational research is a type of scientific investigation in which a researcher looks at the relationships between variables but does not vary, manipulate, or control them. It can be a useful research method for evaluating the direction and strength of the relationship between two or more different variables.

When examining how variables are related to one another, researchers may find that the relationship is positive or negative. Or they may also find that there is no relationship at all.

Table of Contents

How Does Correlational Research Work?

In correlational research, the researcher measures the values of the variables of interest and calculates a correlation coefficient, which quantifies the strength and direction of the relationship between the variables. 

The correlation coefficient ranges from -1.0 to +1.0, where -1.0 represents a perfect negative correlation, 0 represents no correlation, and +1.0 represents a perfect positive correlation. 

A negative correlation indicates that as the value of one variable increases, the value of the other variable decreases, while a positive correlation indicates that as the value of one variable increases, the value of the other variable also increases. A zero correlation indicates that there is no relationship between the variables.

Correlational Research vs. Experimental Research

Correlational research differs from experimental research in that it does not involve manipulating variables. Instead, it focuses on analyzing the relationship between two or more variables.

In other words, correlational research seeks to determine whether there is a relationship between two variables and, if so, the nature of that relationship. 

Experimental research, on the other hand, involves manipulating one or more variables to determine the effect on another variable. Because of this manipulation and control of variables, experimental research allows for causal conclusions to be drawn, while correlational research does not. 

Both types of research are important in understanding the world around us, but they serve different purposes and are used in different situations.

Types of Correlational Research

There are three main types of correlational studies:

Cohort Correlational Study 

This type of study involves following a cohort of participants over a period of time. This type of research can be useful for understanding how certain events might influence outcomes.

For example, researchers might study how exposure to a traumatic natural disaster influences the mental health of a group of people over time.

By examining the data collected from these individuals, researchers can determine whether there is a correlation between the two variables under investigation. This information can be used to develop strategies for preventing or treating certain conditions or illnesses.

Cross-Sectional Correlational Study

A cross-sectional design is a research method that examines a group of individuals at a single time. This type of study collects information from a diverse group of people, usually from different backgrounds and age groups, to gain insight into a particular phenomenon or issue.

The data collected from this type of study is used to analyze relationships between variables and identify patterns and trends within the group.

Cross-sectional studies can help identify potential risk factors for certain conditions or illnesses, and can also be used to evaluate the prevalence of certain behaviors, attitudes, or beliefs within a population.

Case-Control Correlational Study

A case-control correlational study is a type of research design that investigates the relationship between exposure and health outcomes. In this study, researchers identify a group of individuals with the health outcome of interest (cases) and another group of individuals without the health outcome (controls).

The researchers then compare the exposure history of the cases and controls to determine whether the exposure and health outcome correlate.

This type of study design is often used in epidemiology and can provide valuable information about potential risk factors for a particular disease or condition.

When to Use Correlational Research

There are a number of situations where researchers might opt to use a correlational study instead of some other research design.

Correlational research can be used to investigate a wide range of psychological phenomena, including the relationship between personality traits and academic performance, the association between sleep duration and mental health, and the correlation between parental involvement and child outcomes. 

To Generate Hypotheses

Correlational research can also be used to generate hypotheses for further research by identifying variables that are associated with each other.

To Investigate Variables Without Manipulating Them

Researchers should use correlational research when they want to investigate the relationship between two variables without manipulating them. This type of research is useful when the researcher cannot or should not manipulate one of the variables or when it is impossible to conduct an experiment due to ethical or practical concerns. 

To Identify Patterns

Correlational research allows researchers to identify patterns and relationships between variables, which can inform future research and help to develop theories. However, it is important to note that correlational research does not prove that one variable causes changes in the other.

While correlational research has its limitations, it is still a valuable tool for researchers in many fields, including psychology, sociology, and education.

How to Collect Data in Correlational Research

Researchers can collect data for correlational research in a few different ways. To conduct correlational research, data can be collected using the following:

• Surveys : One method is through surveys, where participants are asked to self-report their behaviors or attitudes. This approach allows researchers to gather large amounts of data quickly and affordably.
• Naturalistic observation : Another method is through observation, where researchers observe and record behaviors in a natural or controlled setting. This method allows researchers to learn more about the behavior in question and better generalize the results to real-world settings.
• Archival, retrospective data : Additionally, researchers can collect data from archival sources, such as medical, school records, official records, or past polls. 

The key is to collect data from a large and representative sample to measure the relationship between two variables accurately.

Pros and Cons of Correlational Research

There are some advantages of using correlational research, but there are also some downsides to consider.

• One of the strengths of correlational research is its ability to identify patterns and relationships between variables that may be difficult or unethical to manipulate in an experimental study. 
• Correlational research can also be used to examine variables that are not under the control of the researcher , such as age, gender, or socioeconomic status. 
• Correlational research can be used to make predictions about future behavior or outcomes, which can be valuable in a variety of fields.
• Correlational research can be conducted quickly and inexpensively , making it a practical option for researchers with limited resources. 
• Correlational research is limited by its inability to establish causality between variables. Correlation does not imply causation, and it is possible that a third variable may be influencing both variables of interest, creating a spurious correlation. Therefore, it is important for researchers to use multiple methods of data collection and to be cautious when interpreting correlational findings.
• Correlational research relies heavily on self-reported data , which can be biased or inaccurate.
• Correlational research is limited in its ability to generalize findings to larger populations, as it only measures the relationship between two variables in a specific sample.

Frequently Asked Questions About Correlational Research

What are the main problems with correlational research.

Some of the main problems that can occur in correlational research include selection bias, confounding variables. and misclassification.

• Selecting participants based on their exposure to an event means that the sample might be biased since the selection was not randomized.
• Correlational studies may also be impacted by extraneous factors that researchers cannot control.
• Finally, there may be problems with how accurately data is recorded and classified, which can be particularly problematic in retrospective studies.

What are the variables in a correlational study?

In a correlational study, variables refer to any measurable factors being examined for their potential relationship or association with each other. These variables can be continuous (meaning they can take on a range of values) or categorical (meaning they fall into distinct categories or groups).

For example, in a study examining the correlation between exercise and mental health, the independent variable would be exercise frequency (measured in times per week), while the dependent variable would be mental health (measured using a standardized questionnaire).

What is the goal of correlational research?

The goal of correlational research is to examine the relationship between two or more variables. It involves analyzing data to determine if there is a statistically significant connection between the variables being studied.

Correlational research is useful for identifying patterns and making predictions but cannot establish causation. Instead, it helps researchers to better understand the nature of the relationship between variables and to generate hypotheses for further investigation.

How do you identify correlational research?

To identify correlational research, look for studies that measure two or more variables and analyze their relationship using statistical techniques. The results of correlational studies are typically presented in the form of correlation coefficients or scatterplots, which visually represent the degree of association between the variables being studied.

Correlational research can be useful for identifying potential causal relationships between variables but cannot establish causation on its own.

Curtis EA, Comiskey C, Dempsey O. Importance and use of correlational research . Nurse Researcher . 2016;23(6):20-25. doi10.7748/nr.2016.e1382

Lau F. Chapter 12 Methods for Correlational Studies . University of Victoria; 2017.

Mitchell TR. An evaluation of the validity of correlational research conducted in organizations . The Academy of Management Review . 1985;10(2):192. doi:10.5465/amr.1985.4277939

Seeram E. An overview of correlational research . Radiol Technol . 2019;91(2):176-179.

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Chapter 7: Nonexperimental Research

Correlational Research

Learning Objectives

• Define correlational research and give several examples.
• Explain why a researcher might choose to conduct correlational research rather than experimental research or another type of nonexperimental research.

What Is Correlational Research?

Correlational research is a type of nonexperimental research in which the researcher measures two variables and assesses the statistical relationship (i.e., the correlation) between them with little or no effort to control extraneous variables. There are essentially two reasons that researchers interested in statistical relationships between variables would choose to conduct a correlational study rather than an experiment. The first is that they do not believe that the statistical relationship is a causal one. For example, a researcher might evaluate the validity of a brief extraversion test by administering it to a large group of participants along with a longer extraversion test that has already been shown to be valid. This researcher might then check to see whether participants’ scores on the brief test are strongly correlated with their scores on the longer one. Neither test score is thought to cause the other, so there is no independent variable to manipulate. In fact, the terms  independent variable  and dependent variabl e  do not apply to this kind of research.

The other reason that researchers would choose to use a correlational study rather than an experiment is that the statistical relationship of interest is thought to be causal, but the researcher  cannot  manipulate the independent variable because it is impossible, impractical, or unethical. For example, Allen Kanner and his colleagues thought that the number of “daily hassles” (e.g., rude salespeople, heavy traffic) that people experience affects the number of physical and psychological symptoms they have (Kanner, Coyne, Schaefer, & Lazarus, 1981). [1] But because they could not  manipulate  the number of daily hassles their participants experienced, they had to settle for  measuring  the number of daily hassles—along with the number of symptoms—using self-report questionnaires. Although the strong positive relationship they found between these two variables is consistent with their idea that hassles cause symptoms, it is also consistent with the idea that symptoms cause hassles or that some third variable (e.g., neuroticism) causes both.

A common misconception among beginning researchers is that correlational research must involve two quantitative variables, such as scores on two extroversion tests or the number of hassles and number of symptoms people have experienced. However, the defining feature of correlational research is that the two variables are measured—neither one is manipulated—and this is true regardless of whether the variables are quantitative or categorical. Imagine, for example, that a researcher administers the Rosenberg Self-Esteem Scale to 50 American university students and 50 Japanese university students. Although this “feels” like a between-subjects experiment, it is a correlational study because the researcher did not manipulate the students’ nationalities. The same is true of the study by Cacioppo and Petty comparing professors and factory workers in terms of their need for cognition. It is a correlational study because the researchers did not manipulate the participants’ occupations.

Figure 7.2 shows data from a hypothetical study on the relationship between whether people make a daily list of things to do (a “to-do list”) and stress. Notice that it is unclear whether this design is an experiment or a correlational study because it is unclear whether the independent variable was manipulated. If the researcher randomly assigned some participants to make daily to-do lists and others not to, then it is an experiment. If the researcher simply asked participants whether they made daily to-do lists, then it is a correlational study. The distinction is important because if the study was an experiment, then it could be concluded that making the daily to-do lists reduced participants’ stress. But if it was a correlational study, it could only be concluded that these variables are related. Perhaps being stressed has a negative effect on people’s ability to plan ahead (the directionality problem). Or perhaps people who are more conscientious are more likely to make to-do lists and less likely to be stressed (the third-variable problem). The crucial point is that what defines a study as experimental or correlational is not the variables being studied, nor whether the variables are quantitative or categorical, nor the type of graph or statistics used to analyze the data. It is  how  the study is conducted.

Data Collection in Correlational Research

Again, the defining feature of correlational research is that neither variable is manipulated. It does not matter how or where the variables are measured. A researcher could have participants come to a laboratory to complete a computerized backward digit span task and a computerized risky decision-making task and then assess the relationship between participants’ scores on the two tasks. Or a researcher could go to a shopping mall to ask people about their attitudes toward the environment and their shopping habits and then assess the relationship between these two variables. Both of these studies would be correlational because no independent variable is manipulated. However, because some approaches to data collection are strongly associated with correlational research, it makes sense to discuss them here. The two we will focus on are naturalistic observation and archival data. A third, survey research, is discussed in its own chapter, Chapter 9.

Naturalistic Observation

Naturalistic observation  is an approach to data collection that involves observing people’s behaviour in the environment in which it typically occurs. Thus naturalistic observation is a type of field research (as opposed to a type of laboratory research). It could involve observing shoppers in a grocery store, children on a school playground, or psychiatric inpatients in their wards. Researchers engaged in naturalistic observation usually make their observations as unobtrusively as possible so that participants are often not aware that they are being studied. Ethically, this method is considered to be acceptable if the participants remain anonymous and the behaviour occurs in a public setting where people would not normally have an expectation of privacy. Grocery shoppers putting items into their shopping carts, for example, are engaged in public behaviour that is easily observable by store employees and other shoppers. For this reason, most researchers would consider it ethically acceptable to observe them for a study. On the other hand, one of the arguments against the ethicality of the naturalistic observation of “bathroom behaviour” discussed earlier in the book is that people have a reasonable expectation of privacy even in a public restroom and that this expectation was violated.

Researchers Robert Levine and Ara Norenzayan used naturalistic observation to study differences in the “pace of life” across countries (Levine & Norenzayan, 1999). [2] One of their measures involved observing pedestrians in a large city to see how long it took them to walk 60 feet. They found that people in some countries walked reliably faster than people in other countries. For example, people in Canada and Sweden covered 60 feet in just under 13 seconds on average, while people in Brazil and Romania took close to 17 seconds.

Because naturalistic observation takes place in the complex and even chaotic “real world,” there are two closely related issues that researchers must deal with before collecting data. The first is sampling. When, where, and under what conditions will the observations be made, and who exactly will be observed? Levine and Norenzayan described their sampling process as follows:

“Male and female walking speed over a distance of 60 feet was measured in at least two locations in main downtown areas in each city. Measurements were taken during main business hours on clear summer days. All locations were flat, unobstructed, had broad sidewalks, and were sufficiently uncrowded to allow pedestrians to move at potentially maximum speeds. To control for the effects of socializing, only pedestrians walking alone were used. Children, individuals with obvious physical handicaps, and window-shoppers were not timed. Thirty-five men and 35 women were timed in most cities.” (p. 186)

Precise specification of the sampling process in this way makes data collection manageable for the observers, and it also provides some control over important extraneous variables. For example, by making their observations on clear summer days in all countries, Levine and Norenzayan controlled for effects of the weather on people’s walking speeds.

The second issue is measurement. What specific behaviours will be observed? In Levine and Norenzayan’s study, measurement was relatively straightforward. They simply measured out a 60-foot distance along a city sidewalk and then used a stopwatch to time participants as they walked over that distance. Often, however, the behaviours of interest are not so obvious or objective. For example, researchers Robert Kraut and Robert Johnston wanted to study bowlers’ reactions to their shots, both when they were facing the pins and then when they turned toward their companions (Kraut & Johnston, 1979). [3] But what “reactions” should they observe? Based on previous research and their own pilot testing, Kraut and Johnston created a list of reactions that included “closed smile,” “open smile,” “laugh,” “neutral face,” “look down,” “look away,” and “face cover” (covering one’s face with one’s hands). The observers committed this list to memory and then practised by coding the reactions of bowlers who had been videotaped. During the actual study, the observers spoke into an audio recorder, describing the reactions they observed. Among the most interesting results of this study was that bowlers rarely smiled while they still faced the pins. They were much more likely to smile after they turned toward their companions, suggesting that smiling is not purely an expression of happiness but also a form of social communication.

When the observations require a judgment on the part of the observers—as in Kraut and Johnston’s study—this process is often described as  coding . Coding generally requires clearly defining a set of target behaviours. The observers then categorize participants individually in terms of which behaviour they have engaged in and the number of times they engaged in each behaviour. The observers might even record the duration of each behaviour. The target behaviours must be defined in such a way that different observers code them in the same way. This difficulty with coding is the issue of interrater reliability, as mentioned in Chapter 5. Researchers are expected to demonstrate the interrater reliability of their coding procedure by having multiple raters code the same behaviours independently and then showing that the different observers are in close agreement. Kraut and Johnston, for example, video recorded a subset of their participants’ reactions and had two observers independently code them. The two observers showed that they agreed on the reactions that were exhibited 97% of the time, indicating good interrater reliability.

Archival Data

Another approach to correlational research is the use of  archival data , which are data that have already been collected for some other purpose. An example is a study by Brett Pelham and his colleagues on “implicit egotism”—the tendency for people to prefer people, places, and things that are similar to themselves (Pelham, Carvallo, & Jones, 2005). [4] In one study, they examined Social Security records to show that women with the names Virginia, Georgia, Louise, and Florence were especially likely to have moved to the states of Virginia, Georgia, Louisiana, and Florida, respectively.

As with naturalistic observation, measurement can be more or less straightforward when working with archival data. For example, counting the number of people named Virginia who live in various states based on Social Security records is relatively straightforward. But consider a study by Christopher Peterson and his colleagues on the relationship between optimism and health using data that had been collected many years before for a study on adult development (Peterson, Seligman, & Vaillant, 1988). [5] In the 1940s, healthy male college students had completed an open-ended questionnaire about difficult wartime experiences. In the late 1980s, Peterson and his colleagues reviewed the men’s questionnaire responses to obtain a measure of explanatory style—their habitual ways of explaining bad events that happen to them. More pessimistic people tend to blame themselves and expect long-term negative consequences that affect many aspects of their lives, while more optimistic people tend to blame outside forces and expect limited negative consequences. To obtain a measure of explanatory style for each participant, the researchers used a procedure in which all negative events mentioned in the questionnaire responses, and any causal explanations for them, were identified and written on index cards. These were given to a separate group of raters who rated each explanation in terms of three separate dimensions of optimism-pessimism. These ratings were then averaged to produce an explanatory style score for each participant. The researchers then assessed the statistical relationship between the men’s explanatory style as undergraduate students and archival measures of their health at approximately 60 years of age. The primary result was that the more optimistic the men were as undergraduate students, the healthier they were as older men. Pearson’s  r  was +.25.

This method is an example of  content analysis —a family of systematic approaches to measurement using complex archival data. Just as naturalistic observation requires specifying the behaviours of interest and then noting them as they occur, content analysis requires specifying keywords, phrases, or ideas and then finding all occurrences of them in the data. These occurrences can then be counted, timed (e.g., the amount of time devoted to entertainment topics on the nightly news show), or analyzed in a variety of other ways.

Key Takeaways

• Correlational research involves measuring two variables and assessing the relationship between them, with no manipulation of an independent variable.
• Correlational research is not defined by where or how the data are collected. However, some approaches to data collection are strongly associated with correlational research. These include naturalistic observation (in which researchers observe people’s behaviour in the context in which it normally occurs) and the use of archival data that were already collected for some other purpose.

Discussion: For each of the following, decide whether it is most likely that the study described is experimental or correlational and explain why.

• An educational researcher compares the academic performance of students from the “rich” side of town with that of students from the “poor” side of town.
• A cognitive psychologist compares the ability of people to recall words that they were instructed to “read” with their ability to recall words that they were instructed to “imagine.”
• A manager studies the correlation between new employees’ college grade point averages and their first-year performance reports.
• An automotive engineer installs different stick shifts in a new car prototype, each time asking several people to rate how comfortable the stick shift feels.
• A food scientist studies the relationship between the temperature inside people’s refrigerators and the amount of bacteria on their food.
• A social psychologist tells some research participants that they need to hurry over to the next building to complete a study. She tells others that they can take their time. Then she observes whether they stop to help a research assistant who is pretending to be hurt.
• Kanner, A. D., Coyne, J. C., Schaefer, C., & Lazarus, R. S. (1981). Comparison of two modes of stress measurement: Daily hassles and uplifts versus major life events. Journal of Behavioural Medicine, 4 , 1–39. ↵
• Levine, R. V., & Norenzayan, A. (1999). The pace of life in 31 countries. Journal of Cross-Cultural Psychology, 30 , 178–205. ↵
• Kraut, R. E., & Johnston, R. E. (1979). Social and emotional messages of smiling: An ethological approach. Journal of Personality and Social Psychology, 37 , 1539–1553. ↵
• Pelham, B. W., Carvallo, M., & Jones, J. T. (2005). Implicit egotism. Current Directions in Psychological Science, 14 , 106–110. ↵
• Peterson, C., Seligman, M. E. P., & Vaillant, G. E. (1988). Pessimistic explanatory style is a risk factor for physical illness: A thirty-five year longitudinal study. Journal of Personality and Social Psychology, 55 , 23–27. ↵

An approach to data collection that involves observing people’s behaviour in the environment in which it typically occurs.

A judgment on part of the observers by clearly defining a set of target behaviours.

Data that have already been collected for some other purpose.

A family of systematic approaches to measurement using complex archival data.

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Correlation Studies in Psychology Research

Determining the relationship between two or more variables.

Verywell / Brianna Gilmartin

• Characteristics

Potential Pitfalls

Frequently asked questions.

A correlational study is a type of research design that looks at the relationships between two or more variables. Correlational studies are non-experimental, which means that the experimenter does not manipulate or control any of the variables.

A correlation refers to a relationship between two variables. Correlations can be strong or weak and positive or negative. Sometimes, there is no correlation.

There are three possible outcomes of a correlation study: a positive correlation, a negative correlation, or no correlation. Researchers can present the results using a numerical value called the correlation coefficient, a measure of the correlation strength. It can range from –1.00 (negative) to +1.00 (positive). A correlation coefficient of 0 indicates no correlation.

• Positive correlations : Both variables increase or decrease at the same time. A correlation coefficient close to +1.00 indicates a strong positive correlation.
• Negative correlations : As the amount of one variable increases, the other decreases (and vice versa). A correlation coefficient close to -1.00 indicates a strong negative correlation.
• No correlation : There is no relationship between the two variables. A correlation coefficient of 0 indicates no correlation.

Characteristics of a Correlational Study

Correlational studies are often used in psychology, as well as other fields like medicine. Correlational research is a preliminary way to gather information about a topic. The method is also useful if researchers are unable to perform an experiment.

Researchers use correlations to see if a relationship between two or more variables exists, but the variables themselves are not under the control of the researchers.

While correlational research can demonstrate a relationship between variables, it cannot prove that changing one variable will change another. In other words, correlational studies cannot prove cause-and-effect relationships.

When you encounter research that refers to a "link" or an "association" between two things, they are most likely talking about a correlational study.

Types of Correlational Research

There are three types of correlational research: naturalistic observation, the survey method, and archival research. Each type has its own purpose, as well as its pros and cons.

Naturalistic Observation

The naturalistic observation method involves observing and recording variables of interest in a natural setting without interference or manipulation.  

Can inspire ideas for further research

Option if lab experiment not available

Variables are viewed in natural setting

Can be time-consuming and expensive

Extraneous variables can't be controlled

No scientific control of variables

Subjects might behave differently if aware of being observed

This method is well-suited to studies where researchers want to see how variables behave in their natural setting or state.   Inspiration can then be drawn from the observations to inform future avenues of research.

In some cases, it might be the only method available to researchers; for example, if lab experimentation would be precluded by access, resources, or ethics. It might be preferable to not being able to conduct research at all, but the method can be costly and usually takes a lot of time.  

Naturalistic observation presents several challenges for researchers. For one, it does not allow them to control or influence the variables in any way nor can they change any possible external variables.

However, this does not mean that researchers will get reliable data from watching the variables, or that the information they gather will be free from bias.

For example, study subjects might act differently if they know that they are being watched. The researchers might not be aware that the behavior that they are observing is not necessarily the subject's natural state (i.e., how they would act if they did not know they were being watched).

Researchers also need to be aware of their biases, which can affect the observation and interpretation of a subject's behavior.  

Surveys and questionnaires are some of the most common methods used for psychological research. The survey method involves having a  random sample  of participants complete a survey, test, or questionnaire related to the variables of interest.   Random sampling is vital to the generalizability of a survey's results.

Cheap, easy, and fast

Can collect large amounts of data in a short amount of time

Results can be affected by poor survey questions

Results can be affected by unrepresentative sample

Outcomes can be affected by participants

If researchers need to gather a large amount of data in a short period of time, a survey is likely to be the fastest, easiest, and cheapest option.  

It's also a flexible method because it lets researchers create data-gathering tools that will help ensure they get the information they need (survey responses) from all the sources they want to use (a random sample of participants taking the survey).

Survey data might be cost-efficient and easy to get, but it has its downsides. For one, the data is not always reliable—particularly if the survey questions are poorly written or the overall design or delivery is weak.   Data is also affected by specific faults, such as unrepresented or underrepresented samples .

The use of surveys relies on participants to provide useful data. Researchers need to be aware of the specific factors related to the people taking the survey that will affect its outcome.

For example, some people might struggle to understand the questions. A person might answer a particular way to try to please the researchers or to try to control how the researchers perceive them (such as trying to make themselves "look better").

Sometimes, respondents might not even realize that their answers are incorrect or misleading because of mistaken memories .

Archival Research

Many areas of psychological research benefit from analyzing studies that were conducted long ago by other researchers, as well as reviewing historical records and case studies.

For example, in an experiment known as  "The Irritable Heart ," researchers used digitalized records containing information on American Civil War veterans to learn more about post-traumatic stress disorder (PTSD).

Large amount of data

Can be less expensive

Researchers cannot change participant behavior

Can be unreliable

Information might be missing

No control over data collection methods

Using records, databases, and libraries that are publicly accessible or accessible through their institution can help researchers who might not have a lot of money to support their research efforts.

Free and low-cost resources are available to researchers at all levels through academic institutions, museums, and data repositories around the world.

Another potential benefit is that these sources often provide an enormous amount of data that was collected over a very long period of time, which can give researchers a way to view trends, relationships, and outcomes related to their research.

While the inability to change variables can be a disadvantage of some methods, it can be a benefit of archival research. That said, using historical records or information that was collected a long time ago also presents challenges. For one, important information might be missing or incomplete and some aspects of older studies might not be useful to researchers in a modern context.

A primary issue with archival research is reliability. When reviewing old research, little information might be available about who conducted the research, how a study was designed, who participated in the research, as well as how data was collected and interpreted.

Researchers can also be presented with ethical quandaries—for example, should modern researchers use data from studies that were conducted unethically or with questionable ethics?

You've probably heard the phrase, "correlation does not equal causation." This means that while correlational research can suggest that there is a relationship between two variables, it cannot prove that one variable will change another.

For example, researchers might perform a correlational study that suggests there is a relationship between academic success and a person's self-esteem. However, the study cannot show that academic success changes a person's self-esteem.

To determine why the relationship exists, researchers would need to consider and experiment with other variables, such as the subject's social relationships, cognitive abilities, personality, and socioeconomic status.

The difference between a correlational study and an experimental study involves the manipulation of variables. Researchers do not manipulate variables in a correlational study, but they do control and systematically vary the independent variables in an experimental study. Correlational studies allow researchers to detect the presence and strength of a relationship between variables, while experimental studies allow researchers to look for cause and effect relationships.

If the study involves the systematic manipulation of the levels of a variable, it is an experimental study. If researchers are measuring what is already present without actually changing the variables, then is a correlational study.

The variables in a correlational study are what the researcher measures. Once measured, researchers can then use statistical analysis to determine the existence, strength, and direction of the relationship. However, while correlational studies can say that variable X and variable Y have a relationship, it does not mean that X causes Y.

The goal of correlational research is often to look for relationships, describe these relationships, and then make predictions. Such research can also often serve as a jumping off point for future experimental research. 

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Schneider FW. Applied Social Psychology . 2nd ed. SAGE; 2012:50-53.

Curtis EA, Comiskey C, Dempsey O. Importance and use of correlational research .  Nurse Researcher . 2016;23(6):20-25. doi:10.7748/nr.2016.e1382

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Pizarro J, Silver RC, Prause J. Physical and mental health costs of traumatic war experiences among civil war veterans .  Arch Gen Psychiatry . 2006;63(2):193. doi:10.1001/archpsyc.63.2.193

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

Correlation in Psychology: Meaning, Types, Examples & coefficient

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

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

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

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

On This Page:

Correlation means association – more precisely, it measures the extent to which two variables are related. There are three possible results of a correlational study: a positive correlation, a negative correlation, and no correlation.

• A positive correlation is a relationship between two variables in which both variables move in the same direction. Therefore, one variable increases as the other variable increases, or one variable decreases while the other decreases. An example of a positive correlation would be height and weight. Taller people tend to be heavier.

• A negative correlation is a relationship between two variables in which an increase in one variable is associated with a decrease in the other. An example of a negative correlation would be the height above sea level and temperature. As you climb the mountain (increase in height), it gets colder (decrease in temperature).

• A zero correlation exists when there is no relationship between two variables. For example, there is no relationship between the amount of tea drunk and the level of intelligence.

Scatter Plots

A correlation can be expressed visually. This is done by drawing a scatter plot (also known as a scattergram, scatter graph, scatter chart, or scatter diagram).

A scatter plot is a graphical display that shows the relationships or associations between two numerical variables (or co-variables), which are represented as points (or dots) for each pair of scores.

A scatter plot indicates the strength and direction of the correlation between the co-variables.

When you draw a scatter plot, it doesn’t matter which variable goes on the x-axis and which goes on the y-axis.

Remember, in correlations, we always deal with paired scores, so the values of the two variables taken together will be used to make the diagram.

Decide which variable goes on each axis and then simply put a cross at the point where the two values coincide.

Uses of Correlations

• If there is a relationship between two variables, we can make predictions about one from another.
• Concurrent validity (correlation between a new measure and an established measure).

Reliability

• Test-retest reliability (are measures consistent?).
• Inter-rater reliability (are observers consistent?).

Theory verification

• Predictive validity.

Correlation Coefficients

Instead of drawing a scatter plot, a correlation can be expressed numerically as a coefficient, ranging from -1 to +1. When working with continuous variables, the correlation coefficient to use is Pearson’s r.

The correlation coefficient ( r ) indicates the extent to which the pairs of numbers for these two variables lie on a straight line. Values over zero indicate a positive correlation, while values under zero indicate a negative correlation.

A correlation of –1 indicates a perfect negative correlation, meaning that as one variable goes up, the other goes down. A correlation of +1 indicates a perfect positive correlation, meaning that as one variable goes up, the other goes up.

There is no rule for determining what correlation size is considered strong, moderate, or weak. The interpretation of the coefficient depends on the topic of study.

When studying things that are difficult to measure, we should expect the correlation coefficients to be lower (e.g., above 0.4 to be relatively strong). When we are studying things that are easier to measure, such as socioeconomic status, we expect higher correlations (e.g., above 0.75 to be relatively strong).)

In these kinds of studies, we rarely see correlations above 0.6. For this kind of data, we generally consider correlations above 0.4 to be relatively strong; correlations between 0.2 and 0.4 are moderate, and those below 0.2 are considered weak.

When we are studying things that are more easily countable, we expect higher correlations. For example, with demographic data, we generally consider correlations above 0.75 to be relatively strong; correlations between 0.45 and 0.75 are moderate, and those below 0.45 are considered weak.

Correlation vs. Causation

Causation means that one variable (often called the predictor variable or independent variable) causes the other (often called the outcome variable or dependent variable).

Experiments can be conducted to establish causation. An experiment isolates and manipulates the independent variable to observe its effect on the dependent variable and controls the environment in order that extraneous variables may be eliminated.

A correlation between variables, however, does not automatically mean that the change in one variable is the cause of the change in the values of the other variable. A correlation only shows if there is a relationship between variables.

While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable , is actually causing the systematic movement in our variables of interest.

Correlation does not always prove causation, as a third variable may be involved. For example, being a patient in a hospital is correlated with dying, but this does not mean that one event causes the other, as another third variable might be involved (such as diet and level of exercise).

“Correlation is not causation” means that just because two variables are related it does not necessarily mean that one causes the other.

A correlation identifies variables and looks for a relationship between them. An experiment tests the effect that an independent variable has upon a dependent variable but a correlation looks for a relationship between two variables.

This means that the experiment can predict cause and effect (causation) but a correlation can only predict a relationship, as another extraneous variable may be involved that it not known about.

1. Correlation allows the researcher to investigate naturally occurring variables that may be unethical or impractical to test experimentally. For example, it would be unethical to conduct an experiment on whether smoking causes lung cancer.

2 . Correlation allows the researcher to clearly and easily see if there is a relationship between variables. This can then be displayed in a graphical form.

Limitations

1 . Correlation is not and cannot be taken to imply causation. Even if there is a very strong association between two variables, we cannot assume that one causes the other.

For example, suppose we found a positive correlation between watching violence on T.V. and violent behavior in adolescence.

It could be that the cause of both these is a third (extraneous) variable – for example, growing up in a violent home – and that both the watching of T.V. and the violent behavior is the outcome of this.

2 . Correlation does not allow us to go beyond the given data. For example, suppose it was found that there was an association between time spent on homework (1/2 hour to 3 hours) and the number of G.C.S.E. passes (1 to 6).

It would not be legitimate to infer from this that spending 6 hours on homework would likely generate 12 G.C.S.E. passes.

How do you know if a study is correlational?

A study is considered correlational if it examines the relationship between two or more variables without manipulating them. In other words, the study does not involve the manipulation of an independent variable to see how it affects a dependent variable.

One way to identify a correlational study is to look for language that suggests a relationship between variables rather than cause and effect.

For example, the study may use phrases like “associated with,” “related to,” or “predicts” when describing the variables being studied.

Another way to identify a correlational study is to look for information about how the variables were measured. Correlational studies typically involve measuring variables using self-report surveys, questionnaires, or other measures of naturally occurring behavior.

Finally, a correlational study may include statistical analyses such as correlation coefficients or regression analyses to examine the strength and direction of the relationship between variables.

Why is a correlational study used?

Correlational studies are particularly useful when it is not possible or ethical to manipulate one of the variables.

For example, it would not be ethical to manipulate someone’s age or gender. However, researchers may still want to understand how these variables relate to outcomes such as health or behavior.

Additionally, correlational studies can be used to generate hypotheses and guide further research.

If a correlational study finds a significant relationship between two variables, this can suggest a possible causal relationship that can be further explored in future research.

What is the goal of correlational research?

The ultimate goal of correlational research is to increase our understanding of how different variables are related and to identify patterns in those relationships.

This information can then be used to generate hypotheses and guide further research aimed at establishing causality.

Correlational Methods vs. Experimental Methods

Andrew gellert.

Correlational methodologies and experimental ones are the two approaches to doing research. Experimental studies allow the researcher to control the variables in the study, while correlational ones involve just looking at the data that already exists. Experimental studies allow the researcher to draw conclusions about one variable causing changes in another.

Explore this article

• Research Variables
• Correlational Methods
• Experimental Methods
• Differences and Overlap

1 Research Variables

In a basic study, the researcher has two variables of interest -- the dependent variable and the independent variable -- and the researcher believes that the independent variable affects the dependent variable. For example, in a study about how using fertilizer increases the amount of wheat grown on a farm, the amount of fertilizer used is the independent variable that affects the amount of wheat that grows, which is the dependent variable.

2 Correlational Methods

In a correlation study, the researcher or research team does not have control over the variables in the study. The researcher simply measures the data that she finds in the world. This allows her to see if the two variables are correlated -- whether changes in one are associated with changes in the other. Experimenters in such a study collect existing data, such as economic data from governments, and analyze it using statistical tools. Sometimes, the results of correlation studies can inspire hypothesis that can be tested with a more specific experimental one.

3 Experimental Methods

In a controlled experiment, the research team has control over the independent variable and other aspects of the experiment. This allows the researchers to make conclusions about whether the independent variable really affects the dependent variable, as opposed to the variables changing at the same time through coincidence. The researcher can also eliminate the effects of other variables. For example, the researcher can add precise amounts of fertilizer to different areas of the same wheat field, and measure the differences in wheat yield, as the other other factors, such as rainfall, sun exposure and soil make-up, are the same.

4 Differences and Overlap

The strength of the experimental treatment is that it isolates the relationship between the independent and dependent variable. In a correlation study, there might be other influences on the variables that make it hard to measure how strong the relationship between the two really is. Experimental studies are often more expensive and difficult to run. Correlation studies can explore a relationship to see if it is worth the later expense of a controlled experiment, as well as study a larger data set than may be feasible in an experiment. Some researchers will use both methods in a study, conducting an experiment and then carrying out correlation analysis on the results.

• 1 NCES Kids' Zone: Variables

About the Author

Andrew Gellert is a graduate student who has written science, business, finance and economics articles for four years. He was also the editor of his own section of his college's newspaper, "The Cowl," and has published in his undergraduate economics department's newsletter.

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Lau F, Kuziemsky C, editors. Handbook of eHealth Evaluation: An Evidence-based Approach [Internet]. Victoria (BC): University of Victoria; 2017 Feb 27.

Handbook of eHealth Evaluation: An Evidence-based Approach [Internet].

Chapter 12 methods for correlational studies.

Francis Lau .

12.1. Introduction

Correlational studies aim to find out if there are differences in the characteristics of a population depending on whether or not its subjects have been exposed to an event of interest in the naturalistic setting. In eHealth, correlational studies are often used to determine whether the use of an eHealth system is associated with a particular set of user characteristics and/or quality of care patterns ( Friedman & Wyatt, 2006 ). An example is a computerized provider order entry ( cpoe ) study to differentiate the background, usage and performance between clinical users and non-users of the cpoe system after its implementation in a hospital.

Correlational studies are different from comparative studies in that the evaluator does not control the allocation of subjects into comparison groups or assignment of the intervention to specific groups. Instead, the evaluator defines a set of variables including an outcome of interest then tests for hypothesized relations among these variables. The outcome is known as the dependent variable and the variables being tested for association are the independent variables. Correlational studies are similar to comparative studies in that they take on an objectivist view where the variables can be defined, measured and analyzed for the presence of hypothesized relations. As such, correlational studies face the same challenges as comparative studies in terms of their internal and external validity. Of particular importance are the issues of design choices, selection bias, confounders, and reporting consistency.

In this chapter we describe the basic types of correlational studies seen in the eHealth literature and their methodological considerations. Also included are three case examples to show how these studies are done.

12.2. Types of Correlational Studies

Correlational studies, better known as observational studies in epidemiology, are used to examine event exposure, disease prevalence and risk factors in a population ( Elwood, 2007 ). In eHealth, the exposure typically refers to the use of an eHealth system by a population of subjects in a given setting. These subjects may be patients, providers or organizations identified through a set of variables that are thought to differ in their measured values depending on whether or not the subjects were “exposed” to the eHealth system.

There are three basic types of correlational studies that are used in eHealth evaluation: cohort, cross-sectional, and case-control studies ( Vandenbroucke et al., 2014 ). These are described below.

• Cohort studies – A sample of subjects is observed over time where those exposed and not exposed to the eHealth system are compared for differences in one or more predefined outcomes, such as adverse event rates. Cohort studies may be prospective in nature where subjects are followed for a time period into the future or retrospective for a period into the past. The comparisons are typically made at the beginning of the study as baseline measures, then repeated over time at predefined intervals for differences and trends. Some cohort studies involve only a single group of subjects. Their focus is to describe the characteristics of subjects based on a set of variables, such as the pattern of ehr use by providers and their quality of care in an organization over a given time period.
• Cross-sectional studies – These are considered a type of cohort study where only one comparison is made between exposed and unexposed subjects. They provide a snapshot of the outcome and the associated characteristics of the cohort at a specific point in time.
• Case-control studies – Subjects in a sample that are exposed to the eHealth system are matched with those not exposed but otherwise similar in composition, then compared for differences in some predefined outcomes. Case-control studies are retrospective in nature where subjects already exposed to the event are selected then matched with unexposed subjects, using historical cases to ensure they have similar characteristics.

A cross-sectional survey is a type of cross-sectional study where the data source is drawn from postal questionnaires and interviews. This topic will be covered in the chapter on methods for survey studies.

12.3. Methodological Considerations

While correlational studies are considered less rigorous than rct s, they are the preferred designs when it is neither feasible nor ethical to conduct experimental trials. Key methodological issues arise in terms of: (a) design options, (b) biases and confounders, (c) controlling for confounding effects, (d) adherence to good practices, and (e) reporting consistency. These issues are discussed below.

12.3.1. Design Options

There are growing populations with multiple chronic conditions and healthcare interventions. They have made it difficult to design rct s with sufficient sample size and long-term follow-up to account for all the variability this phenomenon entails. Also rct s are intended to test the efficacy of an intervention in a restricted sample of subjects under ideal settings. They have limited generalizability to the population at large in routine settings ( Fleurence, Naci, & Jansen, 2010 ). As such, correlational studies, especially those involving the use of routinely collected ehr data from the general population, have become viable alternatives to rct s. There are advantages and disadvantages to each of the three design options presented above. They are listed below.

• Cohort studies – These studies typically follow the cohorts over time, which allow one to examine causal relationships between exposure and one or more outcomes. They also allow one to measure change in exposure and outcomes over time. However, these studies can be costly and time-consuming to conduct if the outcomes are rare or occur in the future. With prospective cohorts they can be prone to dropout. With retrospective cohorts accurate historical records are required which may not be available or complete ( Levin, 2003a ).
• Case-control studies – These studies are suited to examine infrequent or rare outcomes since they are selected at the outset to ensure sufficient cases. Yet the selection of exposed and matching cases can be problematic, as not all relevant characteristics are known. Moreover, the cases may not be representative of the population of interest. The focus on exposed cases that occur infrequently may overestimate their risks ( Levin, 2003b ).
• Cross-sectional studies – These studies are easier and quicker to conduct than others as they involve a one-time effort over a short period using a sample from the population of interest. They can be used to generate hypotheses and examine multiple outcomes and characteristics at the same time with no loss to follow-up. On the other hand, these studies only give a snapshot of the situation at one time point, making it difficult for causal inference of the exposure and outcomes. The results might be different had another time period been chosen ( Levin, 2006 ).

12.3.2. Biases and Confounders

Shamliyan, Kane, and Dickinson (2010) conducted a systematic review on tools used to assess the quality of observational studies. Despite the large number of quality scales and checklists found in the literature, they concluded that the universal concerns are in the areas of selection bias, confounding, and misclassification. These concerns, also mentioned by Vandenbroucke and colleagues (2014) in their reporting guidelines for observational studies, are summarized below.

• Selection bias – When subjects are selected through their exposure to the event rather than by random or concealed allocation, there is a risk that the subjects are not comparable due to the presence of systematic differences in their baseline characteristics. For example, a correlational study that examines the association between ehr use and quality of care may have younger providers with more computer savvy in the exposed group because they use ehr more and with more facility than those in the unexposed group. It is also possible to have sicker patients in the exposed group since they require more frequent ehr use than unexposed patients who may be healthier and have less need for the ehr . This is sometimes referred to as response bias, where the characteristics of subjects agreed to be in the study are different from those who declined to take part.
• Confounding – Extraneous factors that influence the outcome but are also associated with the exposure are said to have a confounding effect. One such type is confounding by indication where sicker patients are both more likely to receive treatments and also more likely to have adverse outcomes. For example, a study of cds alerts and adverse drug events may find a positive but spurious association due to the inclusion of sicker patients with multiple conditions and medications, which increases their chance of adverse events regardless of cds alerts.
• Misclassification – When there are systematic differences in the completeness or accuracy of the data recorded on the subjects, there is a risk of misclassification in their exposures or outcomes. This is also known as information or detection bias. An example is where sicker patients may have more complete ehr data because they received more tests, treatments and outcome tracking than those who are healthier and require less attention. As such, the exposure and outcomes of sicker patients may be overestimated.

It is important to note that bias and confounding are not synonymous. Bias is caused by finding the wrong association from flawed information or subject selection. Confounding is factually correct with respect to the relationship found, but is incorrect in its interpretation due to an extraneous factor that is associated with both the exposure and outcome.

12.3.3. Controlling for Confounding Effects

There are three common methods to control for confounding effects. These are by matching, stratification, and modelling. They are described below ( Higgins & Green, 2011 ).

• Matching – The selection of subjects with similar characteristics so that they are comparable; the matching can be done at the individual subject level where each exposed subject is matched with one or more unexposed subjects as controls. It can also be done at the group level with equal numbers of exposed and unexposed subjects. Another way to match subjects is by propensity score, that is, a measure derived from a set of characteristics in the subjects. An example is the retrospective cohort study by Zhou, Leith, Li, and Tom (2015) to examine the association between caregiver phr use and healthcare utilization by pediatric patients. In that study, a propensity score-matching algorithm was used to match phr -registered children to non-registered children. The matching model used registration as the outcome variable and all child and caregiver characteristics as the independent variables.
• Stratification – Subjects are categorized into subgroups based on a set of characteristics such as age and sex then analyzed for the effect within each subgroup. An example is the retrospective cohort study by Staes et al. (2008) , examining the impact of computerized alerts on the quality of outpatient lab monitoring for transplant patients. In that study, the before/after comparison of the timeliness of reporting and clinician responses was stratified by the type of test (creatinine, cyclosporine A, and tacrolimus) and report source (hospital laboratory or other labs).
• Modelling – The use of statistical models to compute adjusted effects while accounting for relevant characteristics such as age and sex differences among subjects. An example is the retrospective cohort study by Beck and colleagues (2012) to compare documentation consistency and care plan improvement before and after the implementation of an electronic asthma-specific history and physical template. In that study, before/after group characteristics were compared for differences using t -tests for continuous variables and χ 2 statistics for categorical variables. Logistic regression was used to adjust for group differences in age, gender, insurance, albuterol use at admission, and previous hospitalization.

12.3.4. Adherence to Good Practices in Prospective Observational Studies

The ispor Good Research Practices Task Force published a set of recommendations in designing, conducting and reporting prospective observational studies for comparative effectiveness research ( Berger et al., 2012 ) that are relevant to eHealth evaluation. Their key recommendations are listed below.

• Key policy questions should be defined to allow inferences to be drawn.
• Hypothesis testing protocol design to include the hypothesis/questions, treatment groups and outcomes, measured and unmeasured confounders, primary analyses, and required sample size.
• Rationale for prospective observational study design over others (e.g., rct ) is based on question, feasibility, intervention characteristics and ability to answer the question versus cost and timeliness.
• Study design choice is able to address potential biases and confounders through the use of inception cohorts, multiple comparator groups, matching designs and unaffected outcomes.
• Explanation of study design and analytic choices is transparent.
• Study execution is carried out in ways that ensure relevance and reasonable follow-up is not different from the usual practice.
• Study registration takes place on publicly available sites prior to its initiation.

12.3.5. The Need for Reporting Consistency

Vandenbroucke et al. (2014) published an expanded version of the Strengthening the Reporting of Observational Studies in Epidemiology ( strobe ) statement to improve the reporting of observational studies that can be applied in eHealth evaluation. It is made up of 22 items, of which 18 are common to cohort, case-control and cross-sectional studies, with four being specific to each of the three designs. The 22 reporting items are listed below (for details refer to the cited reference).

• Title and abstract – one item that covers the type of design used, and a summary of what was done and found.
• Introduction – two items on study background/rationale, objectives and/or hypotheses.
• Methods – nine items on design, setting, participants, variables, data sources/measurement, bias, study size, quantitative variables and statistical methods used.
• Results – five items on participants, descriptive, outcome data, main results and other analyses.
• Discussion – four items on key results, limitations, interpretation and generalizability.
• Other information – one item on funding source.

The four items specific to study design relate to the reporting of participants, statistical methods, descriptive results and outcome data. They are briefly described below for the three types of designs.

• Cohort studies – Participant eligibility criteria and sources, methods of selection, follow-up and handling dropouts, description of follow-up time and duration, and number of outcome events or summary measures over time. For matched studies include matching criteria and number of exposed and unexposed subjects.
• Cross-sectional studies – Participant eligibility criteria, sources and methods of selection, analytical methods accounting for sampling strategy as needed, and number of outcome events or summary measures.
• Case-control studies – Participant eligibility criteria, sources and methods of case/control selection with rationale for choices, methods of matching cases/controls, and number of exposures by category or summary measures of exposures. For matched studies include matching criteria and number of controls per case.

12.4. Case Examples

12.4.1. cohort study of automated immunosuppressive care.

Park and colleagues (2010) conducted a retrospective cohort study to examine the association between the use of a cds (clinical decision support) system in post-liver transplant immunosuppressive care and the rates of rejection episode and drug toxicity. The study is summarized below.

• Setting – A liver transplant program in the United States that had implemented an automated cds system to manage immunosuppressive therapy for its post-liver transplant recipients after discharge. The system consolidated all clinical information to expedite immunosuppressive review, ordering, and follow-up with recipients. Prior to automation, a paper charting system was used that involved manually tracking lab tests, transcribing results into a paper spreadsheet, finding physicians to review results and orders, and contacting recipients to notify them of changes.
• Subjects – The study population included recipients of liver transplants between 2004 and 2008 who received outpatient immunosuppressive therapy that included tacrolimus medications.
• Design – A retrospective cohort study with a before/after design to compare recipients managed by the paper charting system against those managed by the cds system for up to one year after discharge.
• Measures – The outcome variables were the percentages of recipients with at least one rejection and/or tacrolimus toxicity episode during the one-year follow-up period. The independent variables included recipient, intraoperative, donor and postoperative characteristics, and use of paper charting or cds . Examples of recipient variables were age, gender, body mass index, presence of diabetes and hypertension, and pre-transplant lab results. Examples of intraoperative data were blood type match, type of transplant and volume of blood transfused. Examples of donor data included percentage of fat in the liver. Examples of post-transplantation data included the type of immunosuppressive induction therapy and the management method.
• Analysis – Mean, standard deviation and t -tests were computed for continuous variables after checking for normal distribution. Percentages and Fisher’s exact test were computed for categorical variables. Autoregressive integrated moving average analysis was done to determine change in outcomes over time. Logistic regression with variables thought to be clinically relevant was used to identify significant univariable and multivariable factors associated with the outcomes. P values of less than 0.05 were considered significant.
• Findings – Overall, the cds system was associated with significantly fewer episodes of rejection and tacrolimus toxicity. The integrated moving average analysis showed a significant decrease in outcome rates after the cds system was implemented compared with paper charting. Multivariable analysis showed the cds system had lower odds of a rejection episode than paper charting ( or 0.20; p < 0.01) and lower odds of tacrolimus toxicity ( or 0.5; p < 0.01). Other significant non-system related factors included the use of specific drugs, the percentage of fat in the donor liver and the volume of packed red cells transfused.

12.4.2. Cross-sectional Analysis of EHR Documentation and Care Quality

Linder, Schnipper, and Middleton (2012) conducted a cross-sectional study to examine the association between the type of ehr documentation used by physicians and the quality of care provided. The study is summarized below.

• Setting – An integrated primary care practice-based research network affiliated with an academic centre in the United States. The network uses an in-house ehr system with decision support for preventive services, chronic care management, and medication monitoring and alerts. The ehr data include problem and medication lists, coded allergies and lab tests.
• Subjects – Physicians and patients from 10 primary care practices that were part of an rct to examine the use of a decision support tool to manage patients with coronary artery disease and diabetes ( cad/DM ). Eligible patients were those with cad/DM in their ehr problem list prior to the rct start date.
• Design – A nine-month retrospective cross-sectional analysis of ehr data collected from the rct . Three physician documentation styles were defined based on 188,554 visit notes in the ehr : (a) dictation, (b) structured documentation, and (c) free text note. Physicians were divided into three groups based on their predominant style defined as more than 25% of their notes composed by a given method.
• Measures – The outcome variables were 15 ehr -based cad/DM quality measures assessed 30 days after primary care visits. They covered quality of documentation, medication use, lab testing, physiologic measures, and vaccinations. Measures collected prior to the day of visit were eligible and considered fulfilled with the presence of coded ehr data on vital signs, medications, allergies, problem lists, lab tests, and vaccinations. Independent variables on physicians and patients were included as covariates. For physicians, they included age, gender, training level, proportion of cad/DM patients in their panel, total patient visits, and self-reported experience with the ehr . For patients, they included socio-demographic factors, the number of clinic visits and hospitalizations, the number of problems and medications in the ehr , and whether their physician was in the intervention group.
• Analysis – Baseline characteristics of physicians and patients were compared using descriptive statistics. Continuous variables were compared using anova . For categorical variables, Fisher’s exact test was used for physician variables and χ 2 test for patient variables. Multivariate logistic regression models were used for each quality measure to adjust for patient and physician clustering and potential confounders. Bonferroni procedure was used to account for multiple comparisons for the 15 quality measures.
• Findings – During the study period, 234 physicians documented 18,569 visits from 7,000 cad/DM patients. Of these physicians, 146 (62%) typed free-text notes, 68 (25%) used structured documentation, and 20 (9%) dictated notes. After adjusting for cluster effect, physicians who dictated their notes had the worst quality of care in all 15 measures. In particular, physicians who dictated notes were significantly worse in three of 15 measures (antiplatelet medication, tobacco use, diabetic eye exam); physicians who used structured documentation were better in three measures (blood pressure, body mass, diabetic foot exam); and those who used free-text were better in one measure (influenza vaccination). In summary, physicians who dictated notes had worse quality of care than those with structured documentation.

12.4.3. Case-control Comparison of Internet Portal Use

Nielsen, Halamka, and Kinkel (2012) conducted a case-control study to evaluate whether there was an association between active Internet patient portal use by Multiple Sclerosis ( ms ) patients and medical resource utilization. Patient predictors and barriers to portal use were also identified. The study is summarized below.

• Setting – An academic ms centre in the United States with an in-house Internet patient portal site that was accessed by ms patients to schedule clinic appointments, request prescription refills and referrals, view test results, upload personal health information, and communicate with providers via secure e-mails.
• Subjects – 240 adult ms patients actively followed during 2008 and 2009 were randomly selected from the ehr ; 120 of these patients had submitted at least one message during that period and were defined as portal users. Another 120 patients who did not enrol in the portal or send any message were selected as non-users for comparison.
• Design – A retrospective case-control study facilitated through a chart review comparing portal users against non-users from the same period. Patient demographic and clinical information was extracted from the ehr , while portal usage, including feature access type and frequency and e-mail message content, were provided by it staff.
• Measures – Patient variables included age, gender, race, insurance type, employment status, number of medical problems, disease duration, psychiatric history, number of medications, and physical disability scores. Provider variables included prescription type and frequency. Portal usage variables included feature access type and frequency for test results, appointments, prescription requests and logins, and categorized messaging contents.
• Analysis – Comparison of patient demographic, clinical and medical resource utilization data from users and non-users were made using descriptive statistics, Wilcoxon rank sum test, Fisher’s exact test and χ 2 test. Multivariate logistic regression was used to identify patient predictors and barriers to portal use. Provider prescribing habits against patient’s psychiatric history and portal use were examined by two-way analysis of variance. All statistical tests used p value of 0.05 with no adjustment made for multiple comparisons. A logistic multivariate regression model was created to predict portal use based on patient demographics, clinical condition, socio-economic status, and physical disability metrics.
• Findings – Portal users were mostly young professionals with little physical disability. The most frequently used feature was secure patient-provider messaging, often for medication requests or refills, and self-reported side effects. Predictors and barriers of portal use were the number of medications prescribed ( or 1.69, p < 0.0001), Caucasian ethnicity ( or 5.04, p = 0.007), arm and hand disability ( or 0.23, p = 0.01), and impaired vision ( or 0.31, p = 0.01). For medical resource utilization, portal users had more frequent clinic visits, medication use and prescriptions from centre staff providers. Patients with a history of psychiatric disease were prescribed more ms medications than those without any history ( p < 0.0001). In summary, ms patients used the Internet more than the general population, but physical disability limited their access and need to be addressed.

12.4.4. Limitations

A general limitation of a correlational study is that it can determine association between exposure and outcomes but cannot predict causation. The more specific limitations of the three case examples cited by the authors are listed below.

• Automated immunosuppressive care – Baseline differences existed between groups with unknown effects; possible other unmeasured confounders; possible Hawthorne effects from focus on immunosuppressive care.
• ehr documentation and care quality – Small sample size; only three documentation styles were considered (e.g., scribe and voice recognition software were excluded) and unsure if they were stable during study period; quality measures specific to cad/DM conditions only; complex methods of adjusting for clustering and confounding that did not account for unmeasured confounders; the level of physician training (e.g., attending versus residents) not adjusted.
• Internet portal use – Small sample size not representative of the study population; referral centre site could over-represent complex patients requiring advanced care; all patients had health insurance.

12.5. Summary

In this chapter we described cohort, case-control and cross-sectional studies as three types of correlational studies used in eHealth evaluation. The methodological issues addressed include bias and confounding, controlling for confounders, adherence to good practices and consistency in reporting. Three case examples were included to show how eHealth correlational studies are done.

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This publication is licensed under a Creative Commons License, Attribution-Noncommercial 4.0 International License (CC BY-NC 4.0): see https://creativecommons.org/licenses/by-nc/4.0/

• Cite this Page Lau F. Chapter 12 Methods for Correlational Studies. In: Lau F, Kuziemsky C, editors. Handbook of eHealth Evaluation: An Evidence-based Approach [Internet]. Victoria (BC): University of Victoria; 2017 Feb 27.
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• Introduction
• Types of Correlational Studies
• Methodological Considerations
• Case Examples

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Difference Between Correlational and Experimental-Research

Non-experimental research methods like correlational research are used to look at correlations between two or more variables. Positive or negative correlations suggest that as one measure rises, the other either rises or falls. To study the cause-and-effect relationship between various variables, experimental research manages one or more of them. Researchers can accurately see how changing one variable influences the other through this manipulation. The reason and effect of data variance can frequently be determined with the greatest certainty through this kind of investigation.

What is Correlational Research?

A technique of non-experimental research named correlational study is used to explore correlations among two or more variables. Both positive and negative correlations indicate that as one variable changes, the other changes as well or both. In experimental research, one or more variables are changed to study their cause-and-effect relationship. This manipulation allows researchers to exactly see how changes to one variable influence the other. The source and effect of data variance are often best understood through this kind of research.

What is Experimental Research?

A concept or theory is verified through observation and the change of variables in research methods. In order to make inferences and verify the concept, data has to be collected and the results must be evaluated. It’s vital to the scientific investigation because it enables researchers to comprehend how various elements affect the result of a specific experiment. It is often used in physics, biology, psychology, and many other types of research.

Similarities Between Correlational & Experimental Research

To identify relationships between variables and make conclusions about cause and effect, both correlational and experimental research are used. Experiments are used by both types to test theories and gather data. The main difference is that in a correlational study, the researcher does not control the variables; instead, they are used to investigate the relationship between variables. In contrast, an experiment changes one variable while keeping the others constant to find out how the change affects the other variables.

Types of Correlational Research

 The three forms of correlational study are categorized by their own combination of traits as follows

• Positive Correlational Analysis (PCA) Using the most correlated variables, positive correlational research is an important technique to determine whether changing one of the variables will also change the other. For example, increasing employee wages may result in higher product costs.
• Zero Correlational Analysis (ZCA) There is no connection between the variables in zero correlational analysis. Similar experiments combine many parameters that are not logically related in a process known as zero correlational research. In this case, a change in one of the factors might not even result in an equal or opposite change in the other variable. Zero correlational studies allow for the different confusing causal linkages and their causes. Money and stamina can be factors in a study with no link even though they are linearly different.
• Negative Correlational Analysis (NCRA) A study method known as negative correlational research involves two numerically opposing qualities, where a rise in one variable results in a fall in the other. Prices fall when the cost of goods or services rises, and vice versa; this is an example of a negative correlation.

Types of Experimental Research

  The experimental research is also of three primary types:

• Pre-experimental Research: A research study could use a pre-experimental research design when studying a group or groups after implementing research factors of cause and effect. The pre-experimental design will help the researcher in deciding whether more examination is needed for the groups under observation.
• True Experimental Research: A true experimental research design relies on statistical analysis to prove or disprove a researcher’s hypothesis. It is one of the most accurate forms of research because it provides specific scientific evidence. Furthermore, out of all the types of experimental designs, only a true experimental design can establish a cause-effect relationship within a group.
• Quasi-experimental Research: The word “quasi” means similarity. A quasi-experimental design is similar to a true experimental design. However, the difference between the two is the assignment of the control group. In this research design, an independent variable is manipulated, but the participants of a group are not randomly assigned. This type of research design is used in field settings where random assignment is either irrelevant or not required.

Difference Between Correlational & Experimental Research

Correlational studies focus on studying the variables in a mostly natural setting, identifying them, and establishing relationships between them. However, these relationships cannot imply that there is a cause-and-effect connection between either of these variables. Experiments single out certain independent variables and influence them to determine the cause-and-effect between them and dependent variables. That is the main difference between correlational and experimental studies. Each has its uses, depending on the circumstances and the scope of every individual research.

Difference Between Correlational and Experimental-Research – FAQs

What relationship exists in correlational research.

A correlation has direction and can be either positive or negative.

What is a real-life example of correlation?

The variable time spent watching TV and the variable exam score has a negative correlation. As time spent watching TV increases, exam scores decrease.

“Will seeds soaked in sugar water sprout sooner than seeds soaked in plain water?” 

The independent variable is the type of water sugar or plain, and the dependent variable is the time it takes for the seeds to sprout.

Are correlational studies more accurate than experiments?

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

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• What Is Convergent Validity? | Definition & Examples

What Is Convergent Validity? | Definition & Examples

Published on September 18, 2024 by Emily Heffernan, PhD .

Convergent validity is one way to demonstrate the validity of a test—whether it’s measuring the thing it’s supposed to. Specifically, convergent validity evaluates whether a test matches other tests of similar constructs.

If two tests are measuring the same thing, their results should be strongly correlated. This strong correlation indicates convergent validity, which in turn provides evidence of construct validity .

They compare the results of their form to a survey that measures emotional regulation, as they expect this construct to closely relate to distress tolerance.

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Table of contents

What is convergent validity, convergent validity example, convergent vs discriminant validity, how to measure convergent validity, frequently asked questions about convergent validity.

Convergent validity is whether a test or instrument provides results similar to a test that measures the same or a similar construct. A construct is simply any phenomenon that cannot be directly measured. Fields like psychology often involve the study of constructs, such as emotion or intelligence.

If a test is measuring what it’s supposed to, its results should match (or converge with) those of other tests that measure related constructs. Researchers can therefore validate a new measure by comparing it to other, theoretically related measures. If the results of both measures are strongly correlated, convergent validity has been established.

Convergent validity is one form of evidence for construct validity —an overall assessment of whether a test is measuring what it’s supposed to. Other types of validity include face validity , content validity , criterion validity , and discriminant validity.

Consider the following example of how convergent validity might be assessed in psychology research.

The researcher believes that willingness to try new foods should be related to openness to new experiences. They therefore compare the results of their survey to an existing scale of openness.

Convergent validity assesses whether a test or instrument provides results that are close to other measures of a similar construct. Discriminant validity (also referred to as divergent validity ) assesses whether a test provides results that differ from tests of unrelated constructs.

It’s important to make sure a test is measuring what it’s supposed to, but it’s also helpful to ensure that a test isn’t measuring unrelated phenomena. A test that is sufficiently sensitive to a certain construct shouldn’t be providing information about something unrelated.

Consider the following example to better understand how convergent and discriminant validity can be determined.

To assess this test’s convergent validity , you should compare its results to a test of a similar construct. Someone’s self-control should be related to their ability to inhibit behavior, so you could determine convergent validity by computing the correlation between the self-control test and an existing measure of behavioral inhibition.

If both tests have similar results (i.e., are strongly correlated), you can conclude that your test has convergent validity.

To determine discriminant validity , you would instead want to compare your test to a test of something completely different. There should be no overlap between someone’s creativity and their self-control, so comparing your test results to a creativity test would allow you to assess discriminant validity.

Establishing both convergent and discriminant validity is important in ensuring the construct validity of a measure.

Determining convergent validity involves several steps.

Step 1: Choose a similar test

To properly measure convergent validity, you must select a second test to compare your measure to. This second test should measure a construct that is theoretically related to whatever your own test is measuring.

Properly selecting this second test requires a strong understanding of the literature related to your construct. Consider how your construct relates to other topics, and use this information to select an appropriate test to compare to.

Step 2: Calculate the correlation between both tests

Once you’ve chosen a test to compare to, you must demonstrate that it provides similar results to your own. To do this, you can compute the correlation between the results of your test and the related one.

Pearson’s correlation is frequently used to compare two measures, provided its assumptions are met. This test provides a correlation coefficient, denoted r , that falls between -1 and 1. It is interpreted as follows:

• r = −1: perfect negative correlation between two variables
• r = 0: no correlation between two variables
• r = 1: perfect positive correlation between two variables

What is considered a “strong” correlation varies between fields. In addition to the strength of the correlation, you must also consider its statistical significance (generally, whether p < .05).

Consider, for example, a measure of self-control. Self-control is inversely related to impulsivity: someone with high self-control should have low impulsivity. We could therefore test the convergent validity of the self-control measure with a measure of impulsivity.

Convergent validity and discriminant validity (or divergent validity ) are both forms of construct validity . They are both used to determine whether a test is measuring the thing it’s supposed to.

However, each form of validity tells you something slightly different about a test:

• Convergent validity indicates whether the results of a test correspond to other measures of a similar construct. In theory, there should be a high correlation between two tests that measure the same thing.
• Discriminant validity instead measures whether a test is similar to measures of a different construct. There should be a low correlation between two tests that measure different things.

If a test is measuring what it is supposed to, it should correspond to other tests that measure the same thing while differing from tests that measure other things. To assess these two qualities, you must determine both convergent and discriminant validity.

Convergent validity and concurrent validity both indicate how well a test score and another variable compare to one another.

Convergent validity indicates how well one measure corresponds to other measures of the same or similar constructs. These measures do not have to be obtained at the same time.

Concurrent validity instead assesses how well a measure aligns with a benchmark or “gold-standard,” which can be a ground truth or another validated measure. Both measurements should be taken at the same time.

A construct is a phenomenon that cannot be directly measured, such as intelligence, anxiety, or happiness. Researchers must instead approximate constructs using related, measurable variables.

The process of defining how a construct will be measured is called operationalization. Constructs are common in psychology and other social sciences.

To evaluate how well a construct measures what it’s supposed to, researchers determine construct validity . Face validity , content validity , criterion validity , convergent validity , and discriminant validity all provide evidence of construct validity.

Is this article helpful?

Emily Heffernan, PhD

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