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130+ Correlational Research Topics: Great Ideas For Students

The correlational research example title you decide to write will determine the uniqueness of your research paper. Choose a well-thought title that brings out the best of your expertise. Are you confused about which topic suits you? This article will let you know the best correlational research topics for students.

What is Correlation Research?

Correlational research involves looking at the affiliation between two or more study variables. The results of the study will have either a positive, negative, or zero correlation. More so, the research can either be quantitative or qualitative.

Now that you have the answer to “what are correlational studies,” we’ll focus on the various example topics students can use to write excellent papers.

Correlational Research Titles Examples for Highschool Students

Correlation topic examples for stem students, correlational research examples in education, correlational research questions in nursing, examples of correlational research topics in technology, correlational quantitative research topic examples in economics, correlational research topics in psychology, correlational research titles about business, correlational research sample title examples for statistics essays, correlational research examples for sociology research papers.

If you want your high school correlational research paper to stand out, go for creative and fun titles. Get a correlation research example below.

• How can you relate bullying and academic performance?
• Study habits vs academic grades
• Evaluating the link between student success and parents’ involvement
• Discuss test scores and study time
• Physical and mental health: The correlation
• Nutrition and study concentration
• The connection between good results and video games
• Clarifying the relationship between personality traits and subject preference
• The relationship between study time and poor grades
• The correlation between trainers’ support and students’ mental health
• The association between school bullying and absenteeism
• The effects of academic degrees on students’ career development
• Is there a correlation between teaching styles and students’ learning ability

These research topics for STEM students are game-changers. However, try any of the titles below regarding correlation in research.

The connection between:

• Food and drug efficacy
• Exercise and sleep
• Sleep patterns and heart rate
• Weather seasons and body immunity
• Wind speed and energy supply
• Rainfall extent and crop yields
• Respiratory health and air pollution
• Carbon emissions and global warming
• Stress and mental health
• Bridge capacity and preferred design
• Building quality and insulation capability
• Fuel efficiency and vehicle weight
• 19 th and 20 th Century approaches to stem subjects

As you learn more about the thesis statement about social media , keep a keen eye on each example of the correlational research paper we list below.

• The correlation between parental guidance and career decision
• Differences between student grades and career choice
• Teachers’ qualifications and students’ success in class
• The connection between teachers’ age and students’ performance
• Clarifying students’ workload and subject choice
• The link between teachers’ morale and students’ grades
• Discuss school location and performance metrics
• Clarifying the relationship between school curriculum and performance
• Relating school programs to students’ absenteeism
• Academic success vs teachers’ gender
• The association between parental income and school selection
• The effects of many subjects on students’ career choice
• The relationship between school grading and dropout rates

In addition to biochemistry topics and anatomy research paper topics , it also helps to know correlational research topics in nursing. Some of them include the following:

• Is there a relationship between sleep quality and post-surgery management?
• Is there a correlation between patient healing and the choice of drugs?
• Is there a link between physical activity levels and depression?
• Is there an association between nurse-patient communication and patient recovery?
• What is the correlation between age and child mortality in mothers?
• Is there a correlation between patient education and prompt recovery?
• What is the correlation between spirituality and the use of drugs?
• What is the link between patient adherence to drugs and age?
• What is the correlation between routine nursing and back pain?
• Is there a correlation between chemotherapy and fatigue?
• Is there a relationship between age and cholesterol levels?
• Is there a relationship between blood pressure and sleep disturbances?
• What is the link between drug use and organ failure?

A technology research-oriented paper should show your prowess in any area you tackle. Pick any example of a correlational research question from the list below for your research.

• Is there a relationship between screen time and eye strain?
• What is the link between video games and IQ levels?
• Is there a correlation between loneliness and tech dependence?
• What is the link between wireless technology and infertilities
• Is there a relationship between smartphone usage and sleep quality?
• Is there a correlation between academic performance and technology exposure?
• Is there a relationship between technology and physical activity levels?
• What is the correlation between self-esteem and technology?
• What is the link between technology and memory sharpness?
• What is the correlation between screen time and headaches?
• Is there a correlation between technology and anxiety?
• Is there a link between a sedentary lifestyle and technology?
• What is the correlation between tech dependence and communication skills?

The best example of correlational design in quantitative research will help you kickstart your research paper. In your paper, focus on discussing the relationship between the following:

• Inflation and unemployment rates
• Financial liberation and foreign aid
• Trade policies and foreign investors
• Income and nation’s well being
• Salary levels and education levels
• Urbanization and economic progress
• Economy growth rate and national budget
• Marital status and employed population
• Early retirements and the country’s growth
• Energy prices and economic growth
• Employee satisfaction and job retention
• Small-scale businesses and exploitative loans
• Educated population and nation’s economic levels

Depending on the preferred correlation method in research, your paper approach will vary. As you look at these social issues research topics , psychology correlational topics also come in handy.

Discuss the link between the following in your paper:

• Racism and population size
• Propaganda and marketing
• Cults and social class
• Bullying and skin color
• Child abuse and marriages
• Aging and hormones
• Leadership and communication
• Depression and discrimination
• Cognitive behavior therapy and age
• Eating disorders and genetics
• Attention and kids’ gender
• Speech disorder and tech dependence
• Perception and someone’s age

Business and economics research paper topics vary, but you should always go for the best. Here are some ideal topics for your correlation research paper in business.

Assess the link between:

• Remote employees and business growth
• Business ethic laws and productivity
• Language and business growth
• Foreign investments and cultural differences
• Monopoly and businesses closure
• Cultural practices and business survival
• Customer behaviors and products choice
• Advertising and business innovations
• Labor laws and taxation
• Technology and business trends
• Tourism and local economies
• Business sanctions and currency value
• Immigration and unemployment

You’ve probably encountered social media research topics and wondered whether you could get some focusing on statistics. Below examples will get you sorted.

Clarifying the relationship between:

• Rent costs and population
• COVID-19 vaccination and health budget
• Technology and data sample collection
• Education costs and income
• Education levels and job satisfaction
• Local trade volumes and dollar exchange rates
• Loans and small businesses’ growth rate
• Online and offline surveys
• Wage analysis and employee age
• National savings and employment rates
• Poverty and income inequality
• Trade and economic growth
• Interest rates and consumer borrowing behavior trends

In sociology, there are so many argumentative essay topics to write about. But when it comes to correlational topics, many students have a problem.

Write a sociology correlational research paper focusing on the association between:

• Social media and kids’ behaviors in school
• Food culture and modern lifestyle diseases
• Health equity and deaths
• Gender stereotypes and unemployment
• Women’s behaviors and mainstream media programs
• Age differences and abusive marriages
• Children’s obesity and social class
• Infertility and mental health among couples
• Bullying and past violence encounters in kids
• Genetically modified foods and lifestyle diseases
• Religious education and improving technology
• Social media and modern friendships
• Divorce and children education

Let’s now help you write your research paper on time. Whether it’s on sociology, economics, nursing or any other course, we are here for you. Our expert writers offer the best help on correlational research paper writing .

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150+ Correlational Research Topics: Best Ideas For Students

Welcome to our blog, Correlational Research Topics! Research about connections is important for understanding how changes in one thing can relate to changes in another. But it does not mean one thing causes the other. This blog will cover the basics of research on connections. 

This includes what connections mean and different types of connections. We’ll also discuss what impacts connections and why carefully picking research topics matters. Plus, we’ll give examples of connection research topics in different fields. We’ll show why they’re important and could make a difference.

Whether you’re a student looking for research ideas or want to know about connections in the real world, this blog aims to give helpful ideas and motivation for your journey into connection research. Let’s dive in to learn correlational research topics!

What is Correlational Research?

Table of Contents

Correlation research studies how changes in one thing relate to changes in another. It looks at how two things are connected and if they change together. For example, studying whether people’s income and their level of education are correlated. 

Correlation research does not prove cause and effect. It shows relationships between things but not why they are related. More studies are needed to determine if one thing causes the other. Correlation research helps reveal trends and patterns between variables.

How to Select Correlational Research Topics

Here are some simple tips for choosing a good topic for correlational research:

• Pick two things you think are related, like age and memory or exercise and mood.
• Ensure you can measure these things with numbers, like hours exercised per week or the number of words remembered.
• Don’t try to prove one thing causes another; just look at how they are related.
• Pick timely topics that matter right now.
• Look at past research to get ideas and find gaps to fill.
• Think about questions you have about how certain things are connected.
• Look through research databases to find studies on relationships you’re curious about.
• Choose things that naturally connect in the real world, not random things.

The main goal is to pick two things you can measure that somehow seem to relate to each other. Spend time thinking of ideas before settling on a topic.

150+ Correlational Research Topics For Students

Here are over 150 correlational research topics categorized into different fields for students:

• The correlation between self-esteem and educational achievement among high school students.
• Relationship between self-esteem and social media usage in college students.
• Correlation between personality traits and career success.
• Impact of parental attachment styles on romantic relationships in young adults.
• Relationship between stress levels and sleep quality among university students.
• Correlation between emotional intelligence and leadership effectiveness.
• The connection between involvement of parents and academic performance in elementary school children.
• Correlation between anxiety levels and academic performance in college students.
• Relationship between attachment styles and childhood trauma in adulthood.
• Correlation between mindfulness practices and stress reduction among college students.

• The correlation between teacher-student rapport and student engagement in the classroom.
• Relationship between homework completion rates and academic achievement.
• Correlation between classroom environment and student motivation.
• Impact of involvement of parents in education on student performance.
• Relationship between school climate and student behavior.
• Correlation between extracurricular activities and academic success.
• The relationship between teacher feedback and pupil learning outcomes.
• Correlation between technology usage and academic performance.
• Relationship between school resources and student achievement.
• Correlation between bullying experiences and academic performance.
• The correlation between the status of socioeconomic and access to healthcare.
• Relationship between family structure and juvenile delinquency rates.
• Correlation between media representation and cultural perceptions.
• Impact of community involvement on crime rates.
• Relationship between religion and political affiliation.
• Correlation between social support networks and mental health outcomes.
• Relationship between gender roles and career choices.
• Correlation between immigration rates and cultural assimilation.
• Relationship between income inequality and social mobility.
• Correlation between social media usage and social interaction patterns.
• The correlation between growth of GDP and unemployment rates.
• Relationship between inflation rates and consumer spending.
• Correlation between government spending and economic growth.
• Impact of trade policies on economic development.
• Relationship between interest rates and investment behavior.
• Correlation between income inequality and economic stability.
• Relationship between education levels and income disparity.
• Correlation between taxation policies and income distribution.
• Impact of globalization on income inequality.
• Relationship between poverty rates and access to healthcare.

Health and Medicine

• The correlation between exercise frequency and mental health outcomes.
• Relationship between diet quality and cardiovascular health.
• Correlation between habits of smoking and lung cancer rates.
• Impact of sleep duration on physical health.
• Relationship between anxiety levels and immune system function.
• Relationship between vaccination rates and disease prevalence.
• Correlation between air pollution and respiratory diseases.
• Impact of social support networks on recovery from illness.
• Relationship between alcohol consumption and liver health.

Environmental Science

• The correlation between deforestation and biodiversity loss.
• Relationship between greenhouse gas emissions and world temperatures.
• Correlation between water pollution levels and aquatic biodiversity.
• Impact of urbanization on air quality.
• Relationship between waste management practices and environmental sustainability.
• Correlation between agricultural practices and soil erosion rates.
• Relationship between renewable energy usage and carbon emissions.
• Correlation between climate change and natural disasters.
• Impact of plastic pollution on marine ecosystems.
• Relationship between population growth and resource depletion.

Business and Management

• The correlation between employee satisfaction and productivity.
• Relationship between leadership styles and team performance.
• Correlation between employee training programs and job satisfaction.
• Impact of organizational culture on employee turnover rates.
• Relationship between customer satisfaction and business profitability.
• Correlation between marketing strategies and customer retention.
• Relationship between the corporate social responsibility and brand reputation.
• Correlation between employee diversity and innovation.
• Impact of supply chain management practices on company performance.
• Relationship between economic indicators and stock market fluctuations.

Technology and Society

• The correlation between social media usage and loneliness feelings.
• Relationship between screen time and attention span in children.
• Correlation between video game usage and aggression levels.
• Impact of smartphone usage on sleep quality.
• Relationship between the online concerns of privacy and social media usage.
• Correlation between digital literacy skills and academic performance.
• Relationship between technology adoption rates and generational differences.
• Correlation between Internet access and economic development.
• Relationship between online shopping habits and environmental sustainability.
• Correlation between technology usage and mental health outcomes.

Sports and Exercise Science

• The correlation between physical activity levels and cardiovascular health.
• Relationship between nutrition habits and athletic performance.
• Correlation between training intensity and muscle growth.
• Impact of sleep quality on athletic recovery.
• Relationship between exercise frequency and mental well-being.
• Correlation between sports participation and academic performance.
• Relationship between injuries in sports and long-term health outcomes.
• Correlation between coaching styles and athlete motivation.
• Impact of sports specialization on injury risk.
• Relationship between exercise adherence and weight management.

Media and Communication

• The correlation between media consumption habits and political beliefs.
• Relationship between advertising exposure and consumer behavior.
• Correlation between news coverage and public opinion.
• Influence of social media influencers on buying decisions.
• The connection between critical thinking skills and media literacy.
• Correlation between television viewing habits and body image issues.
• Relationship between media representation and societal norms.
• Correlation between online communication and interpersonal relationships.
• Relationship between media exposure and aggression in children.
• Correlation between streaming services usage and traditional media consumption.

Arts and Culture

• The correlation between education in arts and academic achievement.
• Relationship between cultural experiences and empathy levels.
• Correlation between music preferences and personality traits.
• Impact of cultural diversity on creative industries.
• Relationship between art participation and mental health outcomes.
• Correlation between museum attendance and community engagement.
• Relationship between literature consumption and empathy development.
• Correlation between cultural events attendance and social cohesion.
• Impact of arts funding on community development.
• Relationship between artistic expression and emotional well-being.

Political Science

• The correlation between voter turnout and socioeconomic status.
• Relationship between political ideology and environmental policies.
• Correlation between campaign spending and election outcomes.
• Impact of political polarization on civic engagement.
• Relationship between media bias and public perception of political issues.
• Correlation between government transparency and public trust.
• Relationship between political party cooperation and attitudes towards immigration.
• Correlation between political rhetoric and hate crime rates.
• Relationship between political knowledge and participation in democratic processes.
• Correlation between lobbying efforts and policy outcomes.

Law and Justice

• The correlation between socioeconomic status and incarceration rates.
• Relationship between sentencing disparities and racial identity.
• Correlation between police presence and crime rates in urban areas.
• Impact of therapeutic programs of justices on recidivism rates.
• Relationship between access to legal representation and court outcomes.
• Correlation between mandatory sentencing laws and prison overcrowding.
• Relationship between drug policy enforcement and addiction rates.
• Correlation between control laws on guns and firearm-related deaths.
• Relationship between immigration policies and crime rates.
• Correlation between juvenile justice interventions and rehabilitation outcomes.

History and Anthropology

• The correlation between archaeological findings and historical narratives.
• Relationship between language diversity and cultural preservation.
• Correlation between migration patterns and cultural diffusion.
• Impact of colonialism on indigenous cultures.
• Relationship between cultural practices and social hierarchy.
• Correlation between climate change and human migration.
• Relationship between trade routes and cultural exchange.
• Correlation between artistic expressions and societal values.
• Relationship between religious beliefs and cultural traditions.
• Correlation between technological advancements and societal change.

Gender Studies

• The correlation between gender stereotypes and career choices.
• Relationship between media representation and gender norms.
• Correlation between gender wage gap and educational attainment.
• Impact of gender individuality on mental health outcomes.
• Relationship between gender roles and domestic responsibilities.
• Correlation between workplace discrimination and gender diversity.
• Relationship between feminism and political participation.
• Correlation between LGBTQ+ rights advocacy and social acceptance.
• Relationship between gender-based violence and cultural attitudes.
• Correlation between gender equity policies and workplace satisfaction.

Miscellaneous

• The correlation between pet ownership and mental health.
• Relationship between travel experiences and cultural awareness.
• Correlation between volunteering activities and life satisfaction.
• Impact of hobbies on stress management.
• Relationship between religious beliefs and charitable giving.
• Correlation between language proficiency and cognitive abilities.
• Relationship between parenting styles and child development results.
• Correlation between financial literacy and money management skills.
• Correlation between social network size and happiness levels.

These correlational research topics cover a wide range of areas and can inspire students looking to conduct correlational research in various fields.

Challenges and Limitations

Here are some simple challenges with correlational research:

• It can’t prove one thing causes another, only that things are related.
• Other factors could affect the relationship you see between the two things you’re studying.
• Hard to know which thing impacts the other or if they impact each other.
• Just because two things are correlated does not mean they have a strong relationship. The correlation could be weak.
• Uses observational data, so there is less control than in experiments.
• This might not apply to everyone, only the group studied.
• People may not be honest or accurate if they self-report data like in surveys.

In summary, correlational research can only show two things that relate in some way but can’t prove causation or account for other factors that might affect the relationship. The results may only apply to the sample studied, too. These are good limitations to be aware of.

Best Practices for Correlational Research

Here are some best practices for conducting quality correlational research:

• Use a large random sample representing the population you want to generalize to. This strengthens the external validity of your findings.
• Measure variables accurately and reliably using validated instruments. Poor measurement can obscure relationships.
• Collect data prospectively, if possible, rather than retrospectively. This avoids reliance on recollection.
• Use multiple data points over time (longitudinal data) rather than a single data collection. This provides more insight into relationships.
• Examine curvilinear relationships in addition to linear ones. The correlation may only occur at certain levels.
• Control statistically for potential third variables that may influence the relationship. This provides a clearer assessment of the relationship.
• Assess directionality and potential interactive or reciprocal relationships using path analysis or longitudinal data. This provides greater understanding.
• Use multiple regression techniques to model more complex relationships among many variables.
• Report effect sizes and confidence intervals, not just statistical significance. Effect size indicates practical importance.
• Cautiously interpret results and do not overstate causality claims. Correlation does not equal causation.
• Replicate findings using different samples to assess generalizability and consistency.

Following best practices strengthens correlational research’s rigor, analysis, and interpretation. Adhering to these can produce higher-quality studies.

Final Remarks

Studying correlational research topics can help us learn much about how different things are related. Psychology, education, and business students can pick topics to research and find interesting connections. They can learn if certain things appear to go up or down together. This can give useful information to help make decisions or create policies.

When students carefully choose a correlational research topic and study the data, they can add to what we know about real-world relationships. For example, they may find links between sleep and grades, exercise and mood, or class size and learning.

Doing correlational research allows students to spot patterns between things and practice research skills. As they choose their topics, students can find exciting areas to explore. Uncovering correlations teaches us more about the complicated links between things in the world around us. With simple hard work, students can use correlational research to reveal new insights.

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Statistics By Jim

Making statistics intuitive

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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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

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7.2 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 variable 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). 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 extraversion 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 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 7.2 “Results of a Hypothetical Study on Whether People Who Make Daily To-Do Lists Experience Less Stress Than People Who Do Not Make Such Lists” 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. It is how the study is conducted.

Figure 7.2 Results of a Hypothetical Study on Whether People Who Make Daily To-Do Lists Experience Less Stress Than People Who Do Not Make Such Lists

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.

Naturalistic Observation

Naturalistic observation is an approach to data collection that involves observing people’s behavior 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 is considered to be acceptable if the participants remain anonymous and the behavior 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 behavior 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 behavior” 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). 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 the United States and Japan covered 60 feet in about 12 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 behaviors 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 behaviors 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). 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 practiced 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.

Naturalistic observation has revealed that bowlers tend to smile when they turn away from the pins and toward their companions, suggesting that smiling is not purely an expression of happiness but also a form of social communication.

sieneke toering – bowling big lebowski style – CC BY-NC-ND 2.0.

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 behaviors. The observers then categorize participants individually in terms of which behavior they have engaged in and the number of times they engaged in each behavior. The observers might even record the duration of each behavior. The target behaviors must be defined in such a way that different observers code them in the same way. This is the issue of interrater reliability. Researchers are expected to demonstrate the interrater reliability of their coding procedure by having multiple raters code the same behaviors 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). 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). 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 college 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 college students, the healthier they were as older men. Pearson’s r was +.25.

This is an example of content analysis —a family of systematic approaches to measurement using complex archival data. Just as naturalistic observation requires specifying the behaviors 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 behavior 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 Behavioral Medicine, 4 , 1–39.

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.

Levine, R. V., & Norenzayan, A. (1999). The pace of life in 31 countries. Journal of Cross-Cultural Psychology, 30 , 178–205.

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.

Research Methods in Psychology Copyright © 2016 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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• 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.

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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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Correlational Research: What it is with Examples

Our minds can do some brilliant things. For example, it can memorize the jingle of a pizza truck. The louder the jingle, the closer the pizza truck is to us. Who taught us that? Nobody! We relied on our understanding and came to a conclusion. We don’t stop there, do we? If there are multiple pizza trucks in the area and each one has a different jingle, we would memorize it all and relate the jingle to its pizza truck.

This is what correlational research precisely is, establishing a relationship between two variables, “jingle” and “distance of the truck” in this particular example. The correlational study looks for variables that seem to interact with each other. When you see one variable changing, you have a fair idea of how the other variable will change.

What is Correlational research?

Correlational research is a type of non-experimental research method in which a researcher measures two variables and understands and assesses the statistical relationship between them with no influence from any extraneous variable. In statistical analysis, distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities.

Correlational Research Example

The correlation coefficient shows the correlation between two variables (A correlation coefficient is a statistical measure that calculates the strength of the relationship between two variables), a value measured between -1 and +1. When the correlation coefficient is close to +1, there is a positive correlation between the two variables. If the value is relative to -1, there is a negative correlation between the two variables. When the value is close to zero, then there is no relationship between the two variables.

Let us take an example to understand correlational research.

Consider hypothetically, a researcher is studying a correlation between cancer and marriage. In this study, there are two variables: disease and marriage. Let us say marriage has a negative association with cancer. This means that married people are less likely to develop cancer.

However, this doesn’t necessarily mean that marriage directly avoids cancer. In correlational research, it is not possible to establish the fact, what causes what. It is a misconception that a correlational study involves two quantitative variables. However, the reality is two variables are measured, but neither is changed. This is true independent of whether the variables are quantitative or categorical.

Types of correlational research

Mainly three types of correlational research have been identified:

1. Positive correlation: A positive relationship between two variables is when an increase in one variable leads to a rise in the other variable. A decrease in one variable will see a reduction in the other variable. For example, the amount of money a person has might positively correlate with the number of cars the person owns.

2. Negative correlation: A negative correlation is quite literally the opposite of a positive relationship. If there is an increase in one variable, the second variable will show a decrease, and vice versa.

For example, being educated might negatively correlate with the crime rate when an increase in one variable leads to a decrease in another and vice versa. If a country’s education level is improved, it can lower crime rates. Please note that this doesn’t mean that lack of education leads to crimes. It only means that a lack of education and crime is believed to have a common reason – poverty.

3. No correlation: There is no correlation between the two variables in this third type . A change in one variable may not necessarily see a difference in the other variable. For example, being a millionaire and happiness are not correlated. An increase in money doesn’t lead to happiness.

Characteristics of correlational research

Correlational research has three main characteristics. They are: 

• Non-experimental : The correlational study is non-experimental. It means that researchers need not manipulate variables with a scientific methodology to either agree or disagree with a hypothesis. The researcher only measures and observes the relationship between the variables without altering them or subjecting them to external conditioning.
• Backward-looking : Correlational research only looks back at historical data and observes events in the past. Researchers use it to measure and spot historical patterns between two variables. A correlational study may show a positive relationship between two variables, but this can change in the future.
• Dynamic : The patterns between two variables from correlational research are never constant and are always changing. Two variables having negative correlation research in the past can have a positive correlation relationship in the future due to various factors.

Data collection

The distinctive feature of correlational research is that the researcher can’t manipulate either of the variables involved. It doesn’t matter how or where the variables are measured. A researcher could observe participants in a closed environment or a public setting.

Researchers use two data collection methods to collect information in correlational research.

01. Naturalistic observation

Naturalistic observation is a way of data collection in which people’s behavioral targeting is observed in their natural environment, in which they typically exist. This method is a type of field research. It could mean a researcher might be observing people in a grocery store, at the cinema, playground, or in similar places.

Researchers who are usually involved in this type of data collection make observations as unobtrusively as possible so that the participants involved in the study are not aware that they are being observed else they might deviate from being their natural self.

Ethically this method is acceptable if the participants remain anonymous, and if the study is conducted in a public setting, a place where people would not normally expect complete privacy. As mentioned previously, taking an example of the grocery store where people can be observed while collecting an item from the aisle and putting in the shopping bags. This is ethically acceptable, which is why most researchers choose public settings for recording their observations. This data collection method could be both qualitative and quantitative . If you need to know more about qualitative data, you can explore our newly published blog, “ Examples of Qualitative Data in Education .”

02. Archival data

Another approach to correlational data is the use of archival data. Archival information is the data that has been previously collected by doing similar kinds of research . Archival data is usually made available through primary research .

In contrast to naturalistic observation, the information collected through archived data can be pretty straightforward. For example, counting the number of people named Richard in the various states of America based on social security records is relatively short.

Use the correlational research method to conduct a correlational study and measure the statistical relationship between two variables. Uncover the insights that matter the most. Use QuestionPro’s research platform to uncover complex insights that can propel your business to the forefront of your industry.

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Home » Correlational Research – Methods, Types and Examples

Correlational Research – Methods, Types and Examples

Table of Contents

Correlational Research

Correlational Research is a type of research that examines the statistical relationship between two or more variables without manipulating them. It is a non-experimental research design that seeks to establish the degree of association or correlation between two or more variables.

Types of Correlational Research

There are three types of correlational research:

Positive Correlation

A positive correlation occurs when two variables increase or decrease together. This means that as one variable increases, the other variable also tends to increase. Similarly, as one variable decreases, the other variable also tends to decrease. For example, there is a positive correlation between the amount of time spent studying and academic performance. The more time a student spends studying, the higher their academic performance is likely to be. Similarly, there is a positive correlation between a person’s age and their income level. As a person gets older, they tend to earn more money.

Negative Correlation

A negative correlation occurs when one variable increases while the other decreases. This means that as one variable increases, the other variable tends to decrease. Similarly, as one variable decreases, the other variable tends to increase. For example, there is a negative correlation between the number of hours spent watching TV and physical activity level. The more time a person spends watching TV, the less physically active they are likely to be. Similarly, there is a negative correlation between the amount of stress a person experiences and their overall happiness. As stress levels increase, happiness levels tend to decrease.

Zero Correlation

A zero correlation occurs when there is no relationship between two variables. This means that the variables are unrelated and do not affect each other. For example, there is zero correlation between a person’s shoe size and their IQ score. The size of a person’s feet has no relationship to their level of intelligence. Similarly, there is zero correlation between a person’s height and their favorite color. The two variables are unrelated to each other.

Correlational Research Methods

Correlational research can be conducted using different methods, including:

Surveys are a common method used in correlational research. Researchers collect data by asking participants to complete questionnaires or surveys that measure different variables of interest. Surveys are useful for exploring the relationships between variables such as personality traits, attitudes, and behaviors.

Observational Studies

Observational studies involve observing and recording the behavior of participants in natural settings. Researchers can use observational studies to examine the relationships between variables such as social interactions, group dynamics, and communication patterns.

Archival Data

Archival data involves using existing data sources such as historical records, census data, or medical records to explore the relationships between variables. Archival data is useful for investigating the relationships between variables that cannot be manipulated or controlled.

Experimental Design

While correlational research does not involve manipulating variables, researchers can use experimental design to establish cause-and-effect relationships between variables. Experimental design involves manipulating one variable while holding other variables constant to determine the effect on the dependent variable.

Meta-Analysis

Meta-analysis involves combining and analyzing the results of multiple studies to explore the relationships between variables across different contexts and populations. Meta-analysis is useful for identifying patterns and inconsistencies in the literature and can provide insights into the strength and direction of relationships between variables.

Data Analysis Methods

Correlational research data analysis methods depend on the type of data collected and the research questions being investigated. Here are some common data analysis methods used in correlational research:

Correlation Coefficient

A correlation coefficient is a statistical measure that quantifies the strength and direction of the relationship between two variables. The correlation coefficient ranges from -1 to +1, with -1 indicating a perfect negative correlation, +1 indicating a perfect positive correlation, and 0 indicating no correlation. Researchers use correlation coefficients to determine the degree to which two variables are related.

Scatterplots

A scatterplot is a graphical representation of the relationship between two variables. Each data point on the plot represents a single observation. The x-axis represents one variable, and the y-axis represents the other variable. The pattern of data points on the plot can provide insights into the strength and direction of the relationship between the two variables.

Regression Analysis

Regression analysis is a statistical method used to model the relationship between two or more variables. Researchers use regression analysis to predict the value of one variable based on the value of another variable. Regression analysis can help identify the strength and direction of the relationship between variables, as well as the degree to which one variable can be used to predict the other.

Factor Analysis

Factor analysis is a statistical method used to identify patterns among variables. Researchers use factor analysis to group variables into factors that are related to each other. Factor analysis can help identify underlying factors that influence the relationship between two variables.

Path Analysis

Path analysis is a statistical method used to model the relationship between multiple variables. Researchers use path analysis to test causal models and identify direct and indirect effects between variables.

Applications of Correlational Research

Correlational research has many practical applications in various fields, including:

• Psychology : Correlational research is commonly used in psychology to explore the relationships between variables such as personality traits, behaviors, and mental health outcomes. For example, researchers may use correlational research to examine the relationship between anxiety and depression, or the relationship between self-esteem and academic achievement.
• Education : Correlational research is useful in educational research to explore the relationships between variables such as teaching methods, student motivation, and academic performance. For example, researchers may use correlational research to examine the relationship between student engagement and academic success, or the relationship between teacher feedback and student learning outcomes.
• Business : Correlational research can be used in business to explore the relationships between variables such as consumer behavior, marketing strategies, and sales outcomes. For example, marketers may use correlational research to examine the relationship between advertising spending and sales revenue, or the relationship between customer satisfaction and brand loyalty.
• Medicine : Correlational research is useful in medical research to explore the relationships between variables such as risk factors, disease outcomes, and treatment effectiveness. For example, researchers may use correlational research to examine the relationship between smoking and lung cancer, or the relationship between exercise and heart health.
• Social Science : Correlational research is commonly used in social science research to explore the relationships between variables such as socioeconomic status, cultural factors, and social behavior. For example, researchers may use correlational research to examine the relationship between income and voting behavior, or the relationship between cultural values and attitudes towards immigration.

Examples of Correlational Research

• Psychology : Researchers might be interested in exploring the relationship between two variables, such as parental attachment and anxiety levels in young adults. The study could involve measuring levels of attachment and anxiety using established scales or questionnaires, and then analyzing the data to determine if there is a correlation between the two variables. This information could be useful in identifying potential risk factors for anxiety in young adults, and in developing interventions that could help improve attachment and reduce anxiety.
• Education : In a correlational study in education, researchers might investigate the relationship between two variables, such as teacher engagement and student motivation in a classroom setting. The study could involve measuring levels of teacher engagement and student motivation using established scales or questionnaires, and then analyzing the data to determine if there is a correlation between the two variables. This information could be useful in identifying strategies that teachers could use to improve student motivation and engagement in the classroom.
• Business : Researchers might explore the relationship between two variables, such as employee satisfaction and productivity levels in a company. The study could involve measuring levels of employee satisfaction and productivity using established scales or questionnaires, and then analyzing the data to determine if there is a correlation between the two variables. This information could be useful in identifying factors that could help increase productivity and improve job satisfaction among employees.
• Medicine : Researchers might examine the relationship between two variables, such as smoking and the risk of developing lung cancer. The study could involve collecting data on smoking habits and lung cancer diagnoses, and then analyzing the data to determine if there is a correlation between the two variables. This information could be useful in identifying risk factors for lung cancer and in developing interventions that could help reduce smoking rates.
• Sociology : Researchers might investigate the relationship between two variables, such as income levels and political attitudes. The study could involve measuring income levels and political attitudes using established scales or questionnaires, and then analyzing the data to determine if there is a correlation between the two variables. This information could be useful in understanding how socioeconomic factors can influence political beliefs and attitudes.

How to Conduct Correlational Research

Here are the general steps to conduct correlational research:

• Identify the Research Question : Start by identifying the research question that you want to explore. It should involve two or more variables that you want to investigate for a correlation.
• Choose the research method: Decide on the research method that will be most appropriate for your research question. The most common methods for correlational research are surveys, archival research, and naturalistic observation.
• Choose the Sample: Select the participants or data sources that you will use in your study. Your sample should be representative of the population you want to generalize the results to.
• Measure the variables: Choose the measures that will be used to assess the variables of interest. Ensure that the measures are reliable and valid.
• Collect the Data: Collect the data from your sample using the chosen research method. Be sure to maintain ethical standards and obtain informed consent from your participants.
• Analyze the data: Use statistical software to analyze the data and compute the correlation coefficient. This will help you determine the strength and direction of the correlation between the variables.
• Interpret the results: Interpret the results and draw conclusions based on the findings. Consider any limitations or alternative explanations for the results.
• Report the findings: Report the findings of your study in a research report or manuscript. Be sure to include the research question, methods, results, and conclusions.

Purpose of Correlational Research

The purpose of correlational research is to examine the relationship between two or more variables. Correlational research allows researchers to identify whether there is a relationship between variables, and if so, the strength and direction of that relationship. This information can be useful for predicting and explaining behavior, and for identifying potential risk factors or areas for intervention.

Correlational research can be used in a variety of fields, including psychology, education, medicine, business, and sociology. For example, in psychology, correlational research can be used to explore the relationship between personality traits and behavior, or between early life experiences and later mental health outcomes. In education, correlational research can be used to examine the relationship between teaching practices and student achievement. In medicine, correlational research can be used to investigate the relationship between lifestyle factors and disease outcomes.

Overall, the purpose of correlational research is to provide insight into the relationship between variables, which can be used to inform further research, interventions, or policy decisions.

When to use Correlational Research

Here are some situations when correlational research can be particularly useful:

• When experimental research is not possible or ethical: In some situations, it may not be possible or ethical to manipulate variables in an experimental design. In these cases, correlational research can be used to explore the relationship between variables without manipulating them.
• When exploring new areas of research: Correlational research can be useful when exploring new areas of research or when researchers are unsure of the direction of the relationship between variables. Correlational research can help identify potential areas for further investigation.
• When testing theories: Correlational research can be useful for testing theories about the relationship between variables. Researchers can use correlational research to examine the relationship between variables predicted by a theory, and to determine whether the theory is supported by the data.
• When making predictions: Correlational research can be used to make predictions about future behavior or outcomes. For example, if there is a strong positive correlation between education level and income, one could predict that individuals with higher levels of education will have higher incomes.
• When identifying risk factors: Correlational research can be useful for identifying potential risk factors for negative outcomes. For example, a study might find a positive correlation between drug use and depression, indicating that drug use could be a risk factor for depression.

Characteristics of Correlational Research

Here are some common characteristics of correlational research:

• Examines the relationship between two or more variables: Correlational research is designed to examine the relationship between two or more variables. It seeks to determine if there is a relationship between the variables, and if so, the strength and direction of that relationship.
• Non-experimental design: Correlational research is typically non-experimental in design, meaning that the researcher does not manipulate any variables. Instead, the researcher observes and measures the variables as they naturally occur.
• Cannot establish causation : Correlational research cannot establish causation, meaning that it cannot determine whether one variable causes changes in another variable. Instead, it only provides information about the relationship between the variables.
• Uses statistical analysis: Correlational research relies on statistical analysis to determine the strength and direction of the relationship between variables. This may include calculating correlation coefficients, regression analysis, or other statistical tests.
• Observes real-world phenomena : Correlational research is often used to observe real-world phenomena, such as the relationship between education and income or the relationship between stress and physical health.
• Can be conducted in a variety of fields : Correlational research can be conducted in a variety of fields, including psychology, sociology, education, and medicine.
• Can be conducted using different methods: Correlational research can be conducted using a variety of methods, including surveys, observational studies, and archival studies.

Advantages of Correlational Research

There are several advantages of using correlational research in a study:

• Allows for the exploration of relationships: Correlational research allows researchers to explore the relationships between variables in a natural setting without manipulating any variables. This can help identify possible relationships between variables that may not have been previously considered.
• Useful for predicting behavior: Correlational research can be useful for predicting future behavior. If a strong correlation is found between two variables, researchers can use this information to predict how changes in one variable may affect the other.
• Can be conducted in real-world settings: Correlational research can be conducted in real-world settings, which allows for the collection of data that is representative of real-world phenomena.
• Can be less expensive and time-consuming than experimental research: Correlational research is often less expensive and time-consuming than experimental research, as it does not involve manipulating variables or creating controlled conditions.
• Useful in identifying risk factors: Correlational research can be used to identify potential risk factors for negative outcomes. By identifying variables that are correlated with negative outcomes, researchers can develop interventions or policies to reduce the risk of negative outcomes.
• Useful in exploring new areas of research: Correlational research can be useful in exploring new areas of research, particularly when researchers are unsure of the direction of the relationship between variables. By conducting correlational research, researchers can identify potential areas for further investigation.

Limitation of Correlational Research

Correlational research also has several limitations that should be taken into account:

• Cannot establish causation: Correlational research cannot establish causation, meaning that it cannot determine whether one variable causes changes in another variable. This is because it is not possible to control all possible confounding variables that could affect the relationship between the variables being studied.
• Directionality problem: The directionality problem refers to the difficulty of determining which variable is influencing the other. For example, a correlation may exist between happiness and social support, but it is not clear whether social support causes happiness, or whether happy people are more likely to have social support.
• Third variable problem: The third variable problem refers to the possibility that a third variable, not included in the study, is responsible for the observed relationship between the two variables being studied.
• Limited generalizability: Correlational research is often limited in terms of its generalizability to other populations or settings. This is because the sample studied may not be representative of the larger population, or because the variables studied may behave differently in different contexts.
• Relies on self-reported data: Correlational research often relies on self-reported data, which can be subject to social desirability bias or other forms of response bias.
• Limited in explaining complex behaviors: Correlational research is limited in explaining complex behaviors that are influenced by multiple factors, such as personality traits, situational factors, and social context.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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

What is Correlational Research? (+ Design, Examples)

Appinio Research · 04.03.2024 · 30min read

Ever wondered how researchers explore connections between different factors without manipulating them? Correlational research offers a window into understanding the relationships between variables in the world around us. From examining the link between exercise habits and mental well-being to exploring patterns in consumer behavior, correlational studies help us uncover insights that shape our understanding of human behavior, inform decision-making, and drive innovation. In this guide, we'll dive into the fundamentals of correlational research, exploring its definition, importance, ethical considerations, and practical applications across various fields. Whether you're a student delving into research methods or a seasoned researcher seeking to expand your methodological toolkit, this guide will equip you with the knowledge and skills to conduct and interpret correlational studies effectively.

What is Correlational Research?

Correlational research is a methodological approach used in scientific inquiry to examine the relationship between two or more variables. Unlike experimental research , which seeks to establish cause-and-effect relationships through manipulation and control of variables, correlational research focuses on identifying and quantifying the degree to which variables are related to one another. This method allows researchers to investigate associations, patterns, and trends in naturalistic settings without imposing experimental manipulations.

Importance of Correlational Research

Correlational research plays a crucial role in advancing scientific knowledge across various disciplines. Its importance stems from several key factors:

• Exploratory Analysis :  Correlational studies provide a starting point for exploring potential relationships between variables. By identifying correlations, researchers can generate hypotheses and guide further investigation into causal mechanisms and underlying processes.
• Predictive Modeling :  Correlation coefficients can be used to predict the behavior or outcomes of one variable based on the values of another variable. This predictive ability has practical applications in fields such as economics, psychology, and epidemiology, where forecasting future trends or outcomes is essential.
• Diagnostic Purposes:  Correlational analyses can help identify patterns or associations that may indicate the presence of underlying conditions or risk factors. For example, correlations between certain biomarkers and disease outcomes can inform diagnostic criteria and screening protocols in healthcare.
• Theory Development:  Correlational research contributes to theory development by providing empirical evidence for proposed relationships between variables. Researchers can refine and validate theoretical models in their respective fields by systematically examining correlations across different contexts and populations.
• Ethical Considerations:  In situations where experimental manipulation is not feasible or ethical, correlational research offers an alternative approach to studying naturally occurring phenomena. This allows researchers to address research questions that may otherwise be inaccessible or impractical to investigate.

Correlational vs. Causation in Research

It's important to distinguish between correlation and causation in research. While correlational studies can identify relationships between variables, they cannot establish causal relationships on their own. Several factors contribute to this distinction:

• Directionality:  Correlation does not imply the direction of causation. A correlation between two variables does not indicate which variable is causing the other; it merely suggests that they are related in some way. Additional evidence, such as experimental manipulation or longitudinal studies , is needed to establish causality.
• Third Variables:  Correlations may be influenced by third variables, also known as confounding variables, that are not directly measured or controlled in the study. These third variables can create spurious correlations or obscure true causal relationships between the variables of interest.
• Temporal Sequence:  Causation requires a temporal sequence, with the cause preceding the effect in time. Correlational studies alone cannot establish the temporal order of events, making it difficult to determine whether one variable causes changes in another or vice versa.

Understanding the distinction between correlation and causation is critical for interpreting research findings accurately and drawing valid conclusions about the relationships between variables. While correlational research provides valuable insights into associations and patterns, establishing causation typically requires additional evidence from experimental studies or other research designs.

Key Concepts in Correlation

Understanding key concepts in correlation is essential for conducting meaningful research and interpreting results accurately.

Correlation Coefficient

The correlation coefficient is a statistical measure that quantifies the strength and direction of the relationship between two variables. It's denoted by the symbol  r  and ranges from -1 to +1.

• A correlation coefficient of  -1  indicates a perfect negative correlation, meaning that as one variable increases, the other decreases in a perfectly predictable manner.
• A coefficient of  +1  signifies a perfect positive correlation, where both variables increase or decrease together in perfect sync.
• A coefficient of  0  implies no correlation, indicating no systematic relationship between the variables.

Strength and Direction of Correlation

The strength of correlation refers to how closely the data points cluster around a straight line on the scatterplot. A correlation coefficient close to -1 or +1 indicates a strong relationship between the variables, while a coefficient close to 0 suggests a weak relationship.

• Strong correlation:  When the correlation coefficient approaches -1 or +1, it indicates a strong relationship between the variables. For example, a correlation coefficient of -0.9 suggests a strong negative relationship, while a coefficient of +0.8 indicates a strong positive relationship.
• Weak correlation:  A correlation coefficient close to 0 indicates a weak or negligible relationship between the variables. For instance, a coefficient of -0.1 or +0.1 suggests a weak correlation where the variables are minimally related.

The direction of correlation determines how the variables change relative to each other.

• Positive correlation:  When one variable increases, the other variable also tends to increase. Conversely, when one variable decreases, the other variable tends to decrease. This is represented by a positive correlation coefficient.
• Negative correlation:  In a negative correlation, as one variable increases, the other variable tends to decrease. Similarly, when one variable decreases, the other variable tends to increase. This relationship is indicated by a negative correlation coefficient.

Scatterplots

A scatterplot is a graphical representation of the relationship between two variables. Each data point on the plot represents the values of both variables for a single observation. By plotting the data points on a Cartesian plane, you can visualize patterns and trends in the relationship between the variables.

• Interpretation:  When examining a scatterplot, observe the pattern of data points. If the points cluster around a straight line, it indicates a strong correlation. However, if the points are scattered randomly, it suggests a weak or no correlation.
• Outliers:  Identify any outliers or data points that deviate significantly from the overall pattern. Outliers can influence the correlation coefficient and may warrant further investigation to determine their impact on the relationship between variables.
• Line of Best Fit:  In some cases, you may draw a line of best fit through the data points to visually represent the overall trend in the relationship. This line can help illustrate the direction and strength of the correlation between the variables.

Understanding these key concepts will enable you to interpret correlation coefficients accurately and draw meaningful conclusions from your data.

How to Design a Correlational Study?

When embarking on a correlational study, careful planning and consideration are crucial to ensure the validity and reliability of your research findings.

Research Question Formulation

Formulating clear and focused research questions is the cornerstone of any successful correlational study. Your research questions should articulate the variables you intend to investigate and the nature of the relationship you seek to explore. When formulating your research questions:

• Be Specific:  Clearly define the variables you are interested in studying and the population to which your findings will apply.
• Be Testable:  Ensure that your research questions are empirically testable using correlational methods. Avoid vague or overly broad questions that are difficult to operationalize.
• Consider Prior Research:  Review existing literature to identify gaps or unanswered questions in your area of interest. Your research questions should build upon prior knowledge and contribute to advancing the field.

For example, if you're interested in examining the relationship between sleep duration and academic performance among college students, your research question might be: "Is there a significant correlation between the number of hours of sleep per night and GPA among undergraduate students?"

Participant Selection

Selecting an appropriate sample of participants is critical to ensuring the generalizability and validity of your findings. Consider the following factors when selecting participants for your correlational study:

• Population Characteristics:  Identify the population of interest for your study and ensure that your sample reflects the demographics and characteristics of this population.
• Sampling Method:  Choose a sampling method that is appropriate for your research question and accessible, given your resources and constraints. Standard sampling methods include random sampling, stratified sampling, and convenience sampling.
• Sample Size:   Determine the appropriate sample size based on factors such as the effect size you expect to detect, the desired level of statistical power, and practical considerations such as time and budget constraints.

For example, suppose you're studying the relationship between exercise habits and mental health outcomes in adults aged 18-65. In that case, you might use stratified random sampling to ensure representation from different age groups within the population.

Variables Identification

Identifying and operationalizing the variables of interest is essential for conducting a rigorous correlational study. When identifying variables for your research:

• Independent and Dependent Variables:  Clearly distinguish between independent variables (factors that are hypothesized to influence the outcome) and dependent variables (the outcomes or behaviors of interest).
• Control Variables:  Identify any potential confounding variables or extraneous factors that may influence the relationship between your independent and dependent variables. These variables should be controlled for in your analysis.
• Measurement Scales:  Determine the appropriate measurement scales for your variables (e.g., nominal, ordinal, interval, or ratio) and select valid and reliable measures for assessing each construct.

For instance, if you're investigating the relationship between socioeconomic status (SES) and academic achievement, SES would be your independent variable, while academic achievement would be your dependent variable. You might measure SES using a composite index based on factors such as income, education level, and occupation.

Data Collection Methods

Selecting appropriate data collection methods is essential for obtaining reliable and valid data for your correlational study. When choosing data collection methods:

• Quantitative vs. Qualitative :  Determine whether quantitative or qualitative methods are best suited to your research question and objectives. Correlational studies typically involve quantitative data collection methods like surveys, questionnaires, or archival data analysis.
• Instrument Selection:  Choose measurement instruments that are valid, reliable, and appropriate for your variables of interest. Pilot test your instruments to ensure clarity and comprehension among your target population.
• Data Collection Procedures :  Develop clear and standardized procedures for data collection to minimize bias and ensure consistency across participants and time points.

For example, if you're examining the relationship between smartphone use and sleep quality among adolescents, you might administer a self-report questionnaire assessing smartphone usage patterns and sleep quality indicators such as sleep duration and sleep disturbances.

Crafting a well-designed correlational study is essential for yielding meaningful insights into the relationships between variables. By meticulously formulating research questions , selecting appropriate participants, identifying relevant variables, and employing effective data collection methods, researchers can ensure the validity and reliability of their findings.

With Appinio , conducting correlational research becomes even more seamless and efficient. Our intuitive platform empowers researchers to gather real-time consumer insights in minutes, enabling them to make informed decisions with confidence.

Experience the power of Appinio and unlock valuable insights for your research endeavors. Schedule a demo today and revolutionize the way you conduct correlational studies!

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How to Analyze Correlational Data?

Once you have collected your data in a correlational study, the next crucial step is to analyze it effectively to draw meaningful conclusions about the relationship between variables.

How to Calculate Correlation Coefficients?

The correlation coefficient is a numerical measure that quantifies the strength and direction of the relationship between two variables. There are different types of correlation coefficients, including Pearson's correlation coefficient (for linear relationships), Spearman's rank correlation coefficient (for ordinal data ), and Kendall's tau (for non-parametric data). Here, we'll focus on calculating Pearson's correlation coefficient (r), which is commonly used for interval or ratio-level data.

To calculate Pearson's correlation coefficient (r), you can use statistical software such as SPSS, R, or Excel. However, if you prefer to calculate it manually, you can use the following formula:

r = Σ((X - X̄)(Y - Ȳ)) / ((n - 1) * (s_X * s_Y))

• X  and  Y  are the scores of the two variables,
• X̄  and  Ȳ  are the means of X and Y, respectively,
• n  is the number of data points,
• s_X  and  s_Y  are the standard deviations of X and Y, respectively.

Interpreting Correlation Results

Once you have calculated the correlation coefficient (r), it's essential to interpret the results correctly. When interpreting correlation results:

• Magnitude:  The absolute value of the correlation coefficient (r) indicates the strength of the relationship between the variables. A coefficient close to 1 or -1 suggests a strong correlation, while a coefficient close to 0 indicates a weak or no correlation.
• Direction:  The sign of the correlation coefficient (positive or negative) indicates the direction of the relationship between the variables. A positive correlation coefficient indicates a positive relationship (as one variable increases, the other tends to increase), while a negative correlation coefficient indicates a negative relationship (as one variable increases, the other tends to decrease).
• Statistical Significance :  Assess the statistical significance of the correlation coefficient to determine whether the observed relationship is likely to be due to chance. This is typically done using hypothesis testing, where you compare the calculated correlation coefficient to a critical value based on the sample size and desired level of significance (e.g.,  α =0.05).

Statistical Significance

Determining the statistical significance of the correlation coefficient involves conducting hypothesis testing to assess whether the observed correlation is likely to occur by chance. The most common approach is to use a significance level (alpha,  α ) of 0.05, which corresponds to a 5% chance of obtaining the observed correlation coefficient if there is no true relationship between the variables.

To test the null hypothesis that the correlation coefficient is zero (i.e., no correlation), you can use inferential statistics such as the t-test or z-test. If the calculated p-value is less than the chosen significance level (e.g.,  p <0.05), you can reject the null hypothesis and conclude that the correlation coefficient is statistically significant.

Remember that statistical significance does not necessarily imply practical significance or the strength of the relationship. Even a statistically significant correlation with a small effect size may not be meaningful in practical terms.

By understanding how to calculate correlation coefficients, interpret correlation results, and assess statistical significance, you can effectively analyze correlational data and draw accurate conclusions about the relationships between variables in your study.

Correlational Research Limitations

As with any research methodology, correlational studies have inherent considerations and limitations that researchers must acknowledge and address to ensure the validity and reliability of their findings.

Third Variables

One of the primary considerations in correlational research is the presence of third variables, also known as confounding variables. These are extraneous factors that may influence or confound the observed relationship between the variables under study. Failing to account for third variables can lead to spurious correlations or erroneous conclusions about causality.

For example, consider a correlational study examining the relationship between ice cream consumption and drowning incidents. While these variables may exhibit a positive correlation during the summer months, the true causal factor is likely to be a third variable—such as hot weather—that influences both ice cream consumption and swimming activities, thereby increasing the risk of drowning.

To address the influence of third variables, researchers can employ various strategies, such as statistical control techniques, experimental designs (when feasible), and careful operationalization of variables.

Causal Inferences

Correlation does not imply causation—a fundamental principle in correlational research. While correlational studies can identify relationships between variables, they cannot determine causality. This is because correlation merely describes the degree to which two variables co-vary; it does not establish a cause-and-effect relationship between them.

For example, consider a correlational study that finds a positive relationship between the frequency of exercise and self-reported happiness. While it may be tempting to conclude that exercise causes happiness, it's equally plausible that happier individuals are more likely to exercise regularly. Without experimental manipulation and control over potential confounding variables, causal inferences cannot be made.

To strengthen causal inferences in correlational research, researchers can employ longitudinal designs, experimental methods (when ethical and feasible), and theoretical frameworks to guide their interpretations.

Sample Size and Representativeness

The size and representativeness of the sample are critical considerations in correlational research. A small or non-representative sample may limit the generalizability of findings and increase the risk of sampling bias .

For example, if a correlational study examines the relationship between socioeconomic status (SES) and educational attainment using a sample composed primarily of high-income individuals, the findings may not accurately reflect the broader population's experiences. Similarly, an undersized sample may lack the statistical power to detect meaningful correlations or relationships.

To mitigate these issues, researchers should aim for adequate sample sizes based on power analyses, employ random or stratified sampling techniques to enhance representativeness and consider the demographic characteristics of the target population when interpreting findings.

Ensure your survey delivers accurate insights by using our Sample Size Calculator . With customizable options for margin of error, confidence level, and standard deviation, you can determine the optimal sample size to ensure representative results. Make confident decisions backed by robust data.

Reliability and Validity

Ensuring the reliability and validity of measures is paramount in correlational research. Reliability refers to the consistency and stability of measurement over time, whereas validity pertains to the accuracy and appropriateness of measurement in capturing the intended constructs.

For example, suppose a correlational study utilizes self-report measures of depression and anxiety. In that case, it's essential to assess the measures' reliability (e.g., internal consistency, test-retest reliability) and validity (e.g., content validity, criterion validity) to ensure that they accurately reflect participants' mental health status.

To enhance reliability and validity in correlational research, researchers can employ established measurement scales, pilot-test instruments, use multiple measures of the same construct, and assess convergent and discriminant validity.

By addressing these considerations and limitations, researchers can enhance the robustness and credibility of their correlational studies and make more informed interpretations of their findings.

Correlational Research Examples and Applications

Correlational research is widely used across various disciplines to explore relationships between variables and gain insights into complex phenomena. We'll examine examples and applications of correlational studies, highlighting their practical significance and impact on understanding human behavior and societal trends across various industries and use cases.

Psychological Correlational Studies

In psychology, correlational studies play a crucial role in understanding various aspects of human behavior, cognition, and mental health. Researchers use correlational methods to investigate relationships between psychological variables and identify factors that may contribute to or predict specific outcomes.

For example, a psychological correlational study might examine the relationship between self-esteem and depression symptoms among adolescents. By administering self-report measures of self-esteem and depression to a sample of teenagers and calculating the correlation coefficient between the two variables, researchers can assess whether lower self-esteem is associated with higher levels of depression symptoms.

Other examples of psychological correlational studies include investigating the relationship between:

• Parenting styles and academic achievement in children
• Personality traits and job performance in the workplace
• Stress levels and coping strategies among college students

These studies provide valuable insights into the factors influencing human behavior and mental well-being, informing interventions and treatment approaches in clinical and counseling settings.

Business Correlational Studies

Correlational research is also widely utilized in the business and management fields to explore relationships between organizational variables and outcomes. By examining correlations between different factors within an organization, researchers can identify patterns and trends that may impact performance, productivity, and profitability.

For example, a business correlational study might investigate the relationship between employee satisfaction and customer loyalty in a retail setting. By surveying employees to assess their job satisfaction levels and analyzing customer feedback and purchase behavior, researchers can determine whether higher employee satisfaction is correlated with increased customer loyalty and retention.

Other examples of business correlational studies include examining the relationship between:

• Leadership styles and employee motivation
• Organizational culture and innovation
• Marketing strategies and brand perception

These studies provide valuable insights for organizations seeking to optimize their operations, improve employee engagement, and enhance customer satisfaction.

Marketing Correlational Studies

In marketing, correlational studies are instrumental in understanding consumer behavior, identifying market trends, and optimizing marketing strategies. By examining correlations between various marketing variables, researchers can uncover insights that drive effective advertising campaigns, product development, and brand management.

For example, a marketing correlational study might explore the relationship between social media engagement and brand loyalty among millennials. By collecting data on millennials' social media usage, brand interactions, and purchase behaviors, researchers can analyze whether higher levels of social media engagement correlate with increased brand loyalty and advocacy.

Another example of a marketing correlational study could focus on investigating the relationship between pricing strategies and customer satisfaction in the retail sector. By analyzing data on pricing fluctuations, customer feedback , and sales performance, researchers can assess whether pricing strategies such as discounts or promotions impact customer satisfaction and repeat purchase behavior.

Other potential areas of inquiry in marketing correlational studies include examining the relationship between:

• Product features and consumer preferences
• Advertising expenditures and brand awareness
• Online reviews and purchase intent

These studies provide valuable insights for marketers seeking to optimize their strategies, allocate resources effectively, and build strong relationships with consumers in an increasingly competitive marketplace. By leveraging correlational methods, marketers can make data-driven decisions that drive business growth and enhance customer satisfaction.

Correlational Research Ethical Considerations

Ethical considerations are paramount in all stages of the research process, including correlational studies. Researchers must adhere to ethical guidelines to ensure the rights, well-being, and privacy of participants are protected. Key ethical considerations to keep in mind include:

• Informed Consent:  Obtain informed consent from participants before collecting any data. Clearly explain the purpose of the study, the procedures involved, and any potential risks or benefits. Participants should have the right to withdraw from the study at any time without consequence.
• Confidentiality:  Safeguard the confidentiality of participants' data. Ensure that any personal or sensitive information collected during the study is kept confidential and is only accessible to authorized individuals. Use anonymization techniques when reporting findings to protect participants' privacy.
• Voluntary Participation:  Ensure that participation in the study is voluntary and not coerced. Participants should not feel pressured to take part in the study or feel that they will suffer negative consequences for declining to participate.
• Avoiding Harm:  Take measures to minimize any potential physical, psychological, or emotional harm to participants. This includes avoiding deceptive practices, providing appropriate debriefing procedures (if necessary), and offering access to support services if participants experience distress.
• Deception:  If deception is necessary for the study, it must be justified and minimized. Deception should be disclosed to participants as soon as possible after data collection, and any potential risks associated with the deception should be mitigated.
• Researcher Integrity:  Maintain integrity and honesty throughout the research process. Avoid falsifying data, manipulating results, or engaging in any other unethical practices that could compromise the integrity of the study.
• Respect for Diversity:  Respect participants' cultural, social, and individual differences. Ensure that research protocols are culturally sensitive and inclusive, and that participants from diverse backgrounds are represented and treated with respect.
• Institutional Review:  Obtain ethical approval from institutional review boards or ethics committees before commencing the study. Adhere to the guidelines and regulations set forth by the relevant governing bodies and professional organizations.

Adhering to these ethical considerations ensures that correlational research is conducted responsibly and ethically, promoting trust and integrity in the scientific community.

Correlational Research Best Practices and Tips

Conducting a successful correlational study requires careful planning, attention to detail, and adherence to best practices in research methodology. Here are some tips and best practices to help you conduct your correlational research effectively:

• Clearly Define Variables:  Clearly define the variables you are studying and operationalize them into measurable constructs. Ensure that your variables are accurately and consistently measured to avoid ambiguity and ensure reliability.
• Use Valid and Reliable Measures:  Select measurement instruments that are valid and reliable for assessing your variables of interest. Pilot test your measures to ensure clarity, comprehension, and appropriateness for your target population.
• Consider Potential Confounding Variables:  Identify and control for potential confounding variables that could influence the relationship between your variables of interest. Consider including control variables in your analysis to isolate the effects of interest.
• Ensure Adequate Sample Size:  Determine the appropriate sample size based on power analyses and considerations of statistical power. Larger sample sizes increase the reliability and generalizability of your findings.
• Random Sampling:  Whenever possible, use random sampling techniques to ensure that your sample is representative of the population you are studying. If random sampling is not feasible, carefully consider the characteristics of your sample and the extent to which findings can be generalized.
• Statistical Analysis :  Choose appropriate statistical techniques for analyzing your data, taking into account the nature of your variables and research questions. Consult with a statistician if necessary to ensure the validity and accuracy of your analyses.
• Transparent Reporting:  Transparently report your methods, procedures, and findings in accordance with best practices in research reporting. Clearly articulate your research questions, methods, results, and interpretations to facilitate reproducibility and transparency.
• Peer Review:  Seek feedback from colleagues, mentors, or peer reviewers throughout the research process. Peer review helps identify potential flaws or biases in your study design, analysis, and interpretation, improving your research's overall quality and credibility.

By following these best practices and tips, you can conduct your correlational research with rigor, integrity, and confidence, leading to valuable insights and contributions to your field.

Conclusion for Correlational Research

Correlational research serves as a powerful tool for uncovering connections between variables in the world around us. By examining the relationships between different factors, researchers can gain valuable insights into human behavior, health outcomes, market trends, and more. While correlational studies cannot establish causation on their own, they provide a crucial foundation for generating hypotheses, predicting outcomes, and informing decision-making in various fields. Understanding the principles and practices of correlational research empowers researchers to explore complex phenomena, advance scientific knowledge, and address real-world challenges. Moreover, embracing ethical considerations and best practices in correlational research ensures the integrity, validity, and reliability of study findings. By prioritizing informed consent, confidentiality, and participant well-being, researchers can conduct studies that uphold ethical standards and contribute meaningfully to the body of knowledge. Incorporating transparent reporting, peer review, and continuous learning further enhances the quality and credibility of correlational research. Ultimately, by leveraging correlational methods responsibly and ethically, researchers can unlock new insights, drive innovation, and make a positive impact on society.

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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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532 Correlational Research Topics in Psychology

What occurs to you while thinking of a research paper in psychology? We imagine in-depth interviews, surveys, and experiments. These research methods open up the mind of a particular person or a group of people. However, nowadays, psychology has shifted away from employing manual analysis techniques to establishing the fundamental patterns of human behavior. How so? Researchers use data analysis, statistics, and mathematical modeling. Thus, the correlational study is the most understandable and illustrative method.

This article features more than five hundred examples of correlational research topics in psychology for college students. You will learn the basics and primary purposes of this method.

• 🧠 Top 15 Psychology Topics
• 📄 Research in Psychology: the Basics
• 😞 Anxiety, Stress, Depression
• 🤤 Addiction & Eating Disorders
• ⚖️ Research Topics in Psychology
• 🚴 Sports & Health Psychology
• 💢 Violence & Sexual Abuse
• 🧑‍💼 Workplace & Gender Issues Topics
• 🧑‍🤝‍🧑 Correlational Research Topics on Gender
• 🏫 Education & Learning Topics
• 💒 Marriage, Relationships, Parenting

🖇 References

🧠 top 15 correlational research topics in psychology.

• Do our actions define our personality?
• Stress and motivation: Can they coexist?
• How does parents’ IQ level relate to their children’s IQ?
• Which behavioral patterns are the most frequent in children with ASD?
• Success in studies Vs. Career success.
• What role does self-awareness play in changing unhealthy behavior?
• How has the world pandemic changed the stress levels of the population?
• Results of early treatment of mental health.
• Does yoga contribute to stress resilience?
• Social media addiction in young people hurts their college grades.
• Archival research: How often does bullying cause suicidal thoughts?
• Do anti-discrimination education lower children’s abilities to categorize other people?
• Sexual abuse experience and depression probability.
• Are hate crimes more frequent in highly religious societies?
• A laugh can enhance cognitive skills.

📄 Correlational Research in Psychology: the Basics

A correlation study can end in three possible ways that researchers illustrate in a correlation coefficient:

• A Positive correlation happens when both variables simultaneously grow or fall. The coefficient is about +1.00.
• A negative correlation occurs when one variable grows and the other drops. The coefficient is about -1.00.
• A zero correlation happens when there is no relationship between variables. The coefficient is close to 0.

The visual representation of a correlation is called a scatter diagram or scatter chart.

There are three methods for you to perform correlational research in psychology.

We will help you find the most suitable correlational research topic in psychology. Depending on your purposes and methods, the same topics can be used as quantitative research topics.

😞 Correlational Topics on Anxiety, Stress, & Depression

• The rise of depression among college students.
• The impact of Covid-19 on anxiety and depression.
• Post-traumatic stress disorder after Covid-19.
• Recognizing depression in the early stage.
• Importance of stress management at college.
• Anxiety Disorder Studies and Therapy.
• Ways to overcome anxiety during a lockdown.
• The difference between anxiety and depression.
• Mindfulness as a tool to manage stress and anxiety.
• Anxiety effect on young adults and their academic performance.
• The Efficacy of Iron Supplementation to Reduce Vulnerability to Anxiety.
• Effects of post-traumatic stress on the body.
• Anxiety and its effects on teenagers’ self-esteem.
• The rise of social anxiety disorder after Covid-19.
• Depression in adolescents and its contribution to teenage suicides.
• Positive thinking as a treatment for depression.
• COVID-19 and Anxiety Levels Among Nursing Students.
• Prevention of anxiety and emotional burnout on campus.
• Reasons and effects of depression on young females.
• The effects of perfectionism on anxiety and stress.
• Comparison of stress rates among teenagers and adults.
• Social Anxiety Disorder in Teenagers.
• Hypnotherapy as a treatment for stress and anxiety.
• Financial stress and its impact on high school and college students.
• Ways to help someone with depression or anxiety.
• Anxiety and stress as the distractors of an athlete’s attention.
• Overweight and Mental Wellbeing Association.
• Stress as the reason for the lack of energy among teenagers.
• Ways to treat depression after sexual assault.
• Coping with stress and anxiety by yourself.
• The main causes of students’ emotional burnout.
• Musical Effects on Agitation in Dementia.
• The new sources of stress and anxiety in modern society.
• The positive effects of blogging about anxiety and depression.
• Influence of procrastination on academic performance and stress.
• The importance of mental health awareness on campus.
• Post-Traumatic Stress Disorder.
• Coping styles and mechanisms among teenagers.
• Substance use as a form of coping with stress and anxiety.
• Study-life balance as prevention for depression.
• How media creates social anxiety in younger generations?
• Depression as a growing global problem.
• Detrimental Effects of Stress.
• Types of anxiety medication and its effects on teenagers.
• The social effects of generalized anxiety on students.
• The factors that cause depression stigmatization in society.
• The stress of college students with financial debt.
• Depressive Disorder Treatment Discrepancies.
• Importance of self-care for stress management.

🤤 Correlational Research Topics on Addictions & Eating Disorders

• The impacts of social media on eating disorders.
• The relation between drug addiction and crimes among college students.
• Social media addiction among teenagers and its impact.
• The genetic risk factors in eating disorders.
• Drug Addiction and Treatment Program Evaluation.
• Hidden impacts of alcohol abuse among young adults.
• The prevention and treatment of anorexia in teenage girls.
• Stereotypes around eating disorders.
• The impact of patriarchal values on the rise of eating disorders.
• Public and Private Goods: Heroin Addiction Treatment.
• Food addiction among teenagers and possible treatment.
• The rise of marijuana consumption among young adults.
• The effects of substance abuse on mental health.
• Understanding the emotional connections teenagers have with food.
• Malnutrition Secondary to Eating Disorders.
• Health risks among teenagers dealing with eating disorders.
• Effects of video game addiction on a teenager’s mental health.
• Eating disorders from a perspective of developmental psychology.
• The influence of family and culture on teenagers with bulimia.
• Harm Reduction Addiction Treatment in Los Angeles.
• The effects of smoking cigarettes and vaping on mental health.
• Cell phone addiction as a global problem.
• Effects of parental alcoholism on teenagers.
• The analysis of anti-smoking policies in college.
• Positive Body Image and Eating Disorders.
• Effects of substance abuse in teenage depression.
• Early symptoms of binge eating disorder.
• Importance of awareness-raising classes on eating disorders among adolescents.
• Video Games: Benefits and Addiction.
• Self-injurious behavior among girls with eating disorders.
• Effects of bullying in the advancement of drug addiction.
• The connection between depression and eating disorders.
• The urge to eat in fast food restaurants among students.
• The War on Drugs: Legalization of Marijuana.
• Factors contributing to alcoholism among young adults.
• Effects of body shame on the rise of eating disorders.
• Orthorexia and its influence on young women’s self-esteem.
• The SBIRT Method for Alcohol Misuse Screening and Treatment.
• Eating disorders among female athletes.
• The main signals of substance abuse among teenagers.
• Effects of alcohol on students’ academic performance.
• Illicit Drug Use in Palm Beach County.
• Detrimental effects of pornography addiction on mental health.
• Debunking the stigma around eating disorders.
• The micro-trend of selfie addiction and its effects.
• Effects of Nicotine on Medication.
• Mobile games addiction among children and teenagers.
• Violence and addiction in video games.
• The role of the family in the treatment of eating disorders.
• The importance of halving metabolism when dealing with anorexia.

⚖️ Controversial Correlational Research Topics in Psychology

Adhd, bipolar disorder, schizophrenia.

• How can one tell the difference between a hypomanic episode and ADHD?
• What are the connections between ADHD and non-psychiatric disorders?
• Is there a comorbidity between ADHD and post-traumatic disorder?
• How do genetic factors influence the risk of ADHD?
• Treatment of Schizophrenia Spectrum Disorders.
• How do lifestyle changes affect the treatment and management of ADHD?
• Is there a relationship between cognitive behavioral therapy and ADHD response?
• In the case of ADHD, how may hyperactive and inattentive behaviors combine?
• Can autism specter disorder be intervened with ADHD?
• Bipolar Disorder: Pathology, Diagnosis, and Treatment.
• Is there a relationship between ADD-influenced impulsivity and other conduct disorders?
• What causes schizophrenia in children?
• What are the signs of schizophrenia in a child?
• How do opposing moods correlate in the case of bipolar disorder?
• Is there a relationship between gene pool and economic factors in cases of bipolar disorder?
• Children With Bipolar Disorder.
• What is the interdependence between manic episodes and outbursts of psychomotor activity?
• Is there a relationship between a patient’s mania and sleep deprivation?
• What are the connections between the patient’s social responsibilities and hypomania?
• How does bipolar disorder relate to other psychiatric conditions, such as anxiety?
• Humanistic Approach to Emotional Dysfunction.
• Dependence of a child’s development on a parent with bipolar disorder?
• How does early ADHD connect with other antisocial tendencies?
• What is the codependence between ADHD and substance abuse?
• Describe the connection between inattentiveness and reactive attachment disorder.
• Does ADHD influence the patient’s cognitive tempo?
• Treatment Plan For the Patient With Hyperactivity Disorder.
• Establish the connection between schizophrenia and social problems.
• How do environmental factors affect the risk of schizophrenia?
• How does a parent suffering from schizophrenia affect their children?
• How do such complications as poor nutrition may influence the schizophrenia diagnosis?
• Psychodiagnostics in Schizophrenia Case.
• What is the connection between age and schizophrenia?
• How do such disorders as OCD relate to schizophrenia?
• What is the connection between neurological soft signs and symptoms of schizophrenia?
• How does disorganized thinking affect social anxiety and withdrawal?
• Disease Models and Social Learning Therapy.
• Is there a correlation between hormonal cycles and the occurrence of schizophrenia?
• How does schizophrenic disorder affect IQ and neurocognition in general?
• Is there an interdependence between delusions and hallucinations in the case of schizophrenia?
• How can one compare schizophrenic symptoms among teens and adults?
• Usher Syndrome and Mental Illness Relationship.
• How is a schizophrenic disorder linked with suicidal thoughts?
• Is there a connection between social disorders and victimization practices?
• How do mind-altering substances relate to schizophrenic disorder?
• How do rapid cycling episodes relate to the bipolar disorder experience?
• Strategies for Students With ADHD.
• What is the relation between lithium intake and suicide rate reduction among those with bipolar disorder?
• How do deaths from natural causes connect with bipolar disorder?
• In which way can family-focused therapy influence the treatment of manic depression?

Racism, Discrimination, Hate Crimes

• How does racism affect mental health?
• Racism in the 20th and 21st century: the difference.
• How does racism work in world medicine?
• Racism in the USA and South Africa: fundamental contrasts.
• Racial Discrimination and Educational Gap.
• How does social experience affect racial bias?
• Combating racism and the oppression of whites: the correlation.
• How does the brain deal with racism?
• Does anti-racism lead to a split in society?
• Modern racism versus past racism: similarities and differences.
• The Myth of Multiculturalism in Canada.
• How do social networks influence the fight against racism?
• What significant similarities do racial and gender biases have?
• How does racism affect the quality of life?
• The difference between internalized racism and interpersonal racism.
• Racial Happiness and Anti-Racism.
• Men and Women: wage gap in the world.
• What is the contradiction between discrimination and intolerance?
• Women and men: inequality in the labor market.
• How does the digital economy affect gender inequality?
• Mental Illness in Black Community in South Africa.
• Young workers or age-related: who faces discrimination the most?
• Employment without discrimination: myth or reality?
• The contrast between direct and indirect discrimination.
• Comparison of age discrimination with gender discrimination.
• Healthcare Disparities for African Americans.
• “Black Lives Matter” and “MeToo:” Interrelation.
• The discrepancy between traditional male and female roles.
• Is the level of discrimination in the world growing or falling?
• People’s reactions to sexist and racist jokes.
• Discrimination Against Girls in Canada.
• What is the contradiction between same-sex and traditional marriages?
• Correlation between LGBT and Hate Crimes.
• How does racism affect Hate Crimes?
• Hate Crimes in the West and East: the ratio.
• The Black Lives Matter Movement: Aims and Outcomes.
• How has the COVID-19 pandemic affected Hate Crimes?
• What are the main factors affecting the increase in Hate Crimes?
• The influence of the media on prejudice against social minorities.
• Xenophobia and hate crimes: the connection.
• Racial Inequality, Poverty, and Gentrification in Durham, North Carolina.
• How do US laws affect the situation with hate crimes?
• Upbringing vs. social environment: a more significant influence on racism.
• On what grounds do Hate Crimes occur more often: religion or race?
• Do political or social changes have the most impact on Hate Crimes?
• Asking About Sexual Orientation of Patients in Healthcare.
• The impact of political intimidation on the Hate Crimes situation.
• Terrorism and Hate Crimes: similarities.
• The history of changing women’s inequality.

🚴 Sports & Health Psychology Correlational Topics

• A coach and a psychologist: Whose help is more effective?
• Can a coach replace a psychologist?
• Features of children-athletes of preschool and school age.
• The influence of sports on the manifestation of aggression.
• The Impact of Human Resources Management on Healthcare Quality.
• Why is the cooperation of a coach and a psychologist ineffective?
• What is the difference between fear and anxiety in sports activities?
• How to help an athlete cope with pre-start apathy?
• How to set up an athlete for the next performance after a defeat?
• Patient, Family, or Population Health Problem Solution.
• Is friendship possible in sports?
• Can sports help to cope with aggression?
• Common signs of a successful and unsuccessful athlete.
• Satisfaction with the process or result in sports: which is better?
• Why People Exercise.
• Correlation between loss and acquisition of personal resources in sports.
• Is it helpful to fear in sports or not?
• External and internal expression of fear: difference.
• Crises of competition and training process: interrelation.
• Exercising at Home vs. Exercising at the Gym.
• The ratio of life expectancy in the 21st and 20th centuries.
• Are cancer or heart disease the most common causes of death in the United States?
• Does psychological death affect biological death?
• The prevalence of hospices in the United States and Britain.
• Healthcare Types Accessible to Any Individual.
• The impact of palliative care on a patient’s perception of death.
• Is sports motivation or team building the best strategy for solving problems in sports?
• Does moderate daily exercise make a difference in the hygiene of old age?
• Is relaxation the central link of autogenic training?
• Advantages of Physical Exercise for Good Health.
• Physical fitness – an external manifestation of the level of physical activity?
• The relationship between mental and physiological stress in sports.
• Does sport have a significant influence on personality formation?
• Team and individual sports: interrelation and difference.
• Common Health Traditions of Cultural Heritage.
• Sports and physical education: what is the most significant influence on the psychological state of a person?
• Do professional athletes experience fears?
• An Individual’s Passion: The Ideas of Mental Health Care.
• At what age is it advisable for an athlete to start working with a psychologist?
• How does sport ensure the mental health of young athletes?
• How to set up a child for the training process?
• What are the features of the manifestation of fear in sports?
• Health Care Coverage in the USA.
• How to combine academic activities at school and training?
• How to form an interest in sports among children of 8-10 years old?
• What should a coach do to prevent emotional burnout?
• How to develop attention in athletes?
• How to form moral behavior in an athlete?

💢 Correlational Research Topics on Violence & Sexual Abuse

• Is violence an attempt to gain power over others?
• Prevalence of sexual abuse among women and girls.
• Sexual contact without consent and date rape: similarities.
• The ratio of rape among heterosexuals and bisexuals.
• Victimology: Definition of the Concept.
• Physical or mental violence: relationships.
• Do external factors provoke impulsive aggression?
• Are employees often silent or report harassment to management?
• The proper reaction to aggression: showing calmness or rebuffing?
• Nursing Practice and Violence Reporting.
• Micro and Macro causes of violence: correlation.
• Do victims of sexual criminals trust the legal system?
• Sexual assaults on whites and blacks: degree of punishment.
• Is childhood sexual abuse commonplace in the US?
• Domestic Violence and COVID-19 Relation.
• Correlation between PTSD and depression in sexual violence survivors.
• Sexual dysfunction and fertility problems: Connections.
• Are psychological or neurological processes most underlying aggression?
• Does sexual violence cause addiction?
• Gender-Based Violence Against Women and Girls.
• Does the lack of empathy affect aggression manifestation?
• Relationship between cases of elimination and limitation of violence.
• Correlation between physical punishment and sexual abuse of children.
• Violence against children by the States and the USA: scales.
• Violence in Mass Communication and Behavior.
• The root causes of violence against women and men.
• Is envy often the cause of bullying in collectives?
• Degree of the relationship between violence and force.
• Cases of violence before COVID-19 and after.
• Media Violence Effect and Desensitization of Children.
• Violence against women and gender inequality: relationships.
• Are the vast majority of abusers men?
• Can psychological violence turn into physical violence?
• Is psychological violence more common in the family or at work?
• Are abusers depressed people?
• Child Abuse in the United States.
• Are all people capable of violence, or only some?
• What is the probability that someone who committed violence will do it again?
• The boundaries between permissible self-defense and crime.
• Consequences of violence against women and girls.
• Sexual violence and sexual harassment: differences.
• Health Determinants Among Sexually Active High-Risk Adolescents.
• Do strangers commit most sexual assaults?
• Is sexual violence a crime of passion?
• Is the victim of the crime irreversibly damaged?
• The relationship between shared feelings and the consequences experienced by survivors.
• Sexual violence against men and women: the ratio of crimes.
• Sexual violence among primary school children and adolescents: connections.

🧑‍💼 Correlational Research Topics on Workplace Psychology

• Interpersonal relationships and dynamics of working culture: Impacts.
• The relation between industrial and organizational psychology.
• Content and process theories of motivation: the ratio.
• Is money the only motivating factor in work?
• Material or non-material motivation: which is better?
• The Effects of Workplace Conflict on Nurses’ Working Environment.
• Is Maslow’s theory relevant in the workplace?
• Are theories of motivation based on employee needs concepts?
• Are individual needs the primary motivator of leaders’ ideas?
• Distinctive characteristics of moral and material encouragement.
• When Work Is Punishment?
• Do the majority of managers have leadership qualities?
• Liberal and democratic leadership style: similarities.
• Does the democratic style determine subordinates’ professional growth?
• Advantages of combining leadership and management skills.
• Workplace Incivility in Healthcare Facilities.
• What is the relationship between power and leadership?
• The working environment and staff turnover: interrelations.
• Does absenteeism come from a decrease in labor efficiency?
• The dismissal threat and the loyalty: degree of influence.
• Promoting a Healthy Work Environment.
• Is low labor efficiency associated with criticism of results?
• Do timing techniques help to identify standard time sinks?
• The motive power and urgency of the needs: connections.
• The organization of adequate rest and concentration: no correlation.
• Professional Burnout of Medical Workers in Ghana.
• Principle of materialization and a helpful review of tasks: Relationships.
• Chronophages then and today: comparison and difference.
• Importance and urgency: comparison of task evaluation criteria.
• The success of professional activity depends on the psyche’s mood.
• Work-Life Balance and Workplace Stress Management.
• Intellectual biorhythm and changes in professional abilities: Interrelations.
• Does self-management require self-awareness?
• Self-management determines the ability to cope with stress.
• Balance of effort and results in modern realities.
• Gender-Based Discrimination in the Workplace.
• Does perfectionism limit a specialist?

🧑‍🤝‍🧑 Correlational Research Topics in Psychology of Gender

• The relationship between gender and technology.
• What is the impact of sex differences on cognitive functions?
• How does gender influence dreams and aspirations?
• What effect does gender identity have on socialization?
• Gender Roles in the Context of Religion.
• How do gender stereotypes affect people’s perception of their gender?
• The relationship between sex and gender.
• What is the impact of gender identity on children’s mental well-being?
• Sex differences and experiences of pain.
• Gender Discrimination in Nursing.
• Gender roles and the battlefield.
• Gender identity concerning the idea of woman.
• How do gender differences influence the style of parenting?
• Sex differences about substance use disorders.
• Gender Discrimination after the Reemergence of the Taliban in Afghanistan.
• How does gender analysis affect science?
• How do gender differences impact self-esteem?
• The relationship between gender identity and linguistic style.
• How does gender influence job satisfaction?
• Gender Equality at the Heart of Development.
• How do sex differences impact depression?
• Gender influences on adolescent development.
• The influence of adults’ gender stereotypes on children.
• How do sex differences in school relate to gender identity?
• The relationship between gender and emotion.
• Gender differences in terms of negotiation outcomes.
• The relationship between gender identity and coping strategies.
• Sex differences and attitudes towards love.
• Social Change and the Environment.
• The importance of gender in personality psychology.
• What is the effect of sex differences on leadership qualities?
• Sex differences in mortality rates.
• Gender Stereotypes of Superheroes.
• How do gender stereotypes impact school performance?
• The impact of sex differences on influence tactics.
• How does sex impact the immune response?

🏫 Education, Learning, Memory Correlational Research Topics

• How might memory limitations hamper learning opportunities?
• What is the correlation between low education and memory decline?
• Working memory’s role in childhood education.
• The relationship between brain activity and education.
• Comparing Human Memory to the Working of a Computer.
• The effects of collaboration on learning outcomes.
• The effects of education on immediate memory.
• The improvement of memory functions through education programs.
• How does the teacher-student relationship affect student engagement?
• Amnesic: Symptoms and Treatment.
• Cognitively normal people and their memory functioning.
• Individual differences and their role in learning.
• How are rates of memory decline connected to education?
• The effects of problem-based learning on medical education.
• The Significance of Ethics and Ethical Education in Daily Life.
• Children with visual impairments and their ways of learning.
• The relationship between students’ temperament and learning behaviors.
• How does the modern-age education system affect memory?
• Problem behaviors and the role of students’ gender differences.
• Transformational Leadership in Nursing Education.
• The speed of information processing and its effects on memory.
• The relationship between language and semantic memory.
• The practices of memory in history education.
• Episodic memory in contemporary educational psychology.
• Patient Education and Healthcare Professionals’ Role.
• How does retrieval contribute to learning?
• The correlation between active learning and enhanced memory.
• People with learning disabilities and memory difficulties.
• The impact of emotion on learning.
• Health Education and Promotion in Community.
• Short-term memory’s role in language learning.
• The relationship between personality characteristics and learning outcomes.
• How does drug abuse impact memory?
• The function of gesture concerning learning.
• Customized Education Effect on Readmissions of Patients with Congestive Heart Failure.
• How can music improve memory?
• The role of relationships in students’ academic success.
• The implications of learning under stress for educational settings.
• The pandemic’s impact on academic motivation.
• Mathematical Skills in Early Childhood Education.
• Digital storytelling and its effects on visual memory.
• Working memory in the context of science education.
• Working memory and learning difficulties.
• The effects of bilingualism on memory.
• Online Social Networks in Education.
• Sensory learning styles and their impact on learning.
• How did the COVID-19 crisis affect doctoral students?
• What effect do teachers’ beliefs and values have on their teaching?
• The relationship between problem-solving and motivation to learn.
• Technology in Education: Mixed-Methods Design.

💒 Marriage & Relationships Correlational Topics

• The impact of previous relationships on marriage.
• How do romantic relationships affect health?
• The connection between sexual satisfaction and marital satisfaction.
• The correlation between individual strength and relationship satisfaction.
• Same-Sex Marriage Research Paper.
• Same-sex marriage and people’s understanding of same-sex relationships.
• The relationship between gender role attitudes and marriage expectations.
• How does marriage change people’s relationships with their parents?
• Relationship education and a couple’s communication skills.
• Same-Sex Marriages Justification and Human Rights.
• The relationship between marriage and drug use.
• The impact of rape in marriage on mental health.
• Emotional intelligence and marital satisfaction.
• Gender role attitudes and division of household labor in marriage.
• Millennials Say Marriage Ideal but Parenthood the Priority.
• How does a transition from cohabitation to marriage affect a relationship?
• The impact of religion on marriage.
• A spouse’s gender and their assessment of marital satisfaction.
• The correlation between wedding spending and marriage duration.
• Gay Marriage: Disputes and the Ethical Dilemma.
• The effects of sexual timing on relationships
• How does a marriage influence ego development?
• The lack of argumentative skills and marriage quality.
• The connection between marriage and individualism.
• Marriage and Family in America.
• What is the effect of economic factors on relationship quality?
• How is marriage strength affected by relationship enrichment programs?
• The relationship between child marriage and domestic violence.
• Neuroticism and marital satisfaction.
• Relationships: Importance and Impacts.
• Household income and relationship satisfaction.
• The relationship between a wife’s employment and marriage quality.
• What is the correlation between relationship quality and sleep quality?
• The impact fathers have on their daughters’ romantic relationships.
• How does forgiveness in marriage affect relationship quality?
• The Family Discipline Issues.
• The number of marriageable men and nuptiality.
• The relationship between a healthy marriage and health outcomes.
• How does marriage change people’s views on relationships?
• The relationship between work stress and marriage quality.
• The effect of personality on marriage.
• Work-Family Conflict and Its Impacts on Parties.
• People’s childhood experiences and their marriage quality.
• How does a marriage relationship change during a couple’s transition to parenthood?
• Adult children of divorce and their view on marriage.
• What is the effect of children on a marriage relationship?
• The Golden Era of the American Family.
• Stigma experiences and relationship quality in same-sex couples.
• The relationship between losing parents and marrying early.

Correlational Research Topics on Parenting Psychology

• Relations between child development stages theories and styles of parenting?
• What is the impact of media on children’s and adolescents’ behavior?
• How are children’s mental capacities affected by the roles of school teachers?
• Is there a relation between baby sign language and further linguistic cognition?
• Single Mothers, Poverty, and Mental Health Issues.
• How to reduce the risk of a child developing schizophrenia?
• Can bad parenting be a cause of mental illnesses in a child?
• How does puberty affect a teenager’s social behavior?
• What is the relation between spanking a child and children’s violent behavior?
• What is the relation between the gender of a single parent and parenting style?
• How does single male parenting affect a male child?
• How does female single parenting affect a male child?
• How does female single parenting affect a female child?
• Human Development in Childhood.
• Dynamics between adopted children and gay parents?
• What are the disciplinary strategies of authoritarian parenting?
• What is the relation between parenting style and the socialization of children?
• What is the relation between authoritarian and authoritative parenting styles?
• Childhood Obesity as an Urgent Problem of Epidemiology.
• How does authoritarian parenting affect the emergence of obsessive-compulsive syndrome in children in adulthood?
• In which way does authoritarian parenting affect children’s self-esteem?
• What is the connection between children’s obedience and level of happiness?
• The connection between culture and parenting style: Transfer of values.
• Malnutrition in Children Under Five Years of Age.
• What two parental dimensions form the basis for the parenting styles?
• What are parent/child separation effects on children’s emotional stability?
• How can a lack of care in a family affect a child’s psyche?
• How may the phenomenon of learned helplessness manifest itself in a child’s development?
• Benefits of Sex Education for Teenagers.
• How does the malicious parent syndrome influence a child’s morale and cognition?
• How does the self-centered behavior of a parent reflect in a child’s development?
• Is there a correlation between a parent’s unresolved trauma and emotional abuse in parenting?
• How does gaslighting relate to children’s self-esteem?
• Teen Pregnancy and Self Awareness in Mississippi.
• How does emotional development relate to cognitive skills?
• Are social and language development mutually connected?
• What are the signals of a child being emotionally manipulated?
• Can a parent’s behavior lead a child to be emotionally manipulative?
• Teenage Pregnancy Research Paper.
• How does an uninvolved style of parenting affect children’s development?
• Can parental abuse cause a child to have a social disorder?
• Name the connections between an infant’s and a toddler’s stages of development.
• How does child development relate to increasing autonomy?
• Why Should Children Have a Pet During Childhood?
• How does the behavioral model of development relate to Freudian theories?
• Can cognitive advancement lead to asynchronous development?
• How are mood swings connected to emotional development?
• Can cognitive development influence egotistic tendencies in a child?
• Children’s Psychological Qualitative Research Methodology.
• How does the attachment theory relate to a child’s social development?
• Quantitative Psychology Designs Research Methods to Test Complex Issues – American Psychological Association
• What Is a Correlational Study? – Verywell Mind
• Correlation Definitions, Examples & Interpretation – SymplyPsyhology
• Stress relief is within reach – American Psychological Association
• Anxiety Disorders – National Institut of Mental Health
• A Parent’s Role – Psychology Today
• What Is Educational Psychology? – Verywell Mind
• APS Backgrounder Series: Psychological Science and COVID-19: Pandemic Effects on Marriage and Relationships – Association for Psychological Science
• Psychology in the Workplace – Boss Foundation
• I/O Psychology Provides Workplace Solutions – American Psychological Association

AP® Psychology

Correlational study examples: ap® psychology crash course.

• The Albert Team
• Last Updated On: March 1, 2022

Do you remember what a correlational study is? Knowing the main types of psychology research is a key point for the Advanced Placement (AP) Psychology exam as it makes up for 8-10% of the content in the multiple choice and free response questions. However, understanding the characteristics, advantages and disadvantages of each research method is only half of mastering this subject. The other half is understanding in concrete and practical terms how the research methods have been applied to studies in different fields of psychology. In this AP® Psychology crash course review, we will see three correlational study examples that have contributed to the history of psychology, changing the way we perceive our nature, our personality, and our health.

Review: What is a Correlational Study and why is it Important?

Psychology is a science, and like any other, its knowledge must be scientifically obtained, verified and validated. For this, psychologists conduct three types of research:

• Experimental research – the most empirical type of research, where variables can be manipulated in laboratory conditions and different situations can be studied and compared to establish relations of cause and effect between variables.
• Clinical research – done through case studies under the premise that certain individual characteristics can be generalized to the rest of the population.
• Correlational research – seeks the relationship between two variables. The necessary data is gathered through surveys (questionnaires and interviews), archival research (past studies that present the data) and naturalistic observation (observation of the phenomena as they naturally happen, without intervening). The data is then statistically analyzed to verify the relationship between the variables.

The correlation between the variables is shown through a value that goes from -1.00 to +1.00. This value is called the correlational coefficient . When the correlational coefficient is close to +1.00, there is a positive correlation between the variables. In other words, an increase in X accompanies an increase in Y. When the correlational coefficient is close to -1.00, there is a negative correlation between the variables or an increase in X is followed by a decrease in Y. And when the correlational coefficient is close to 0.00 there is no relationship between the variables. The closer the value is to +1.00 or -1.00, the strongest the relationship is. We will see real examples of this later on this post.

Now, the most important thing to remember about correlational studies is that correlation does not imply causation . For example, let’s say that “marriage” has a negative correlation with “cancer,” meaning that people who are married are less likely to develop cancer throughout their lives than those who remain single. This doesn’t necessarily mean that one causes the other or that marriage directly avoids cancer. Maybe one variable does cause the other, but even if it does, in correlational studies it is not possible to determine the direction of causation or what is causing what. And it could also be that a third unknown variable is what causes the correlation. Keep this in mind as we see the correlational study examples.

You might be wondering: if correlational studies only show this – correlations – why are they important in the first place if you could just conduct an experiment manipulating the relevant variables and getting to more solid conclusions?

Indeed, the disadvantages of correlational studies are that they cannot establish causal relationships nor direction of causal influence, there is no control of the variables, they don’t explain behavior, and they could result in illusory correlations. Illusory correlation is when there is a perceived relationship between variables that does not exist, like “a higher ice cream consumption leads to higher crime rate.”

On the other hand, one of the main advantages of a correlational study is that it is a useful way to describe and analyze data especially in cases where experimental research would lead to ethical issues. Take for instance a research that aims to investigate the relationship between child abuse and coping abilities later in adulthood. You obviously can’t take a random group of healthy children and expose them to abusive or traumatic situations to compare it with a control group. In the earlier stages of psychology, researchers could get away with teaching a phobia to a baby or leading participants to think they had electrocuted someone to death and get away with it in the name of science. Such practices are no longer acceptable, and correlational studies play an important role in developing knowledge in psychology.

Other advantages are that correlational studies are usually less expensive and easier to conduct than experiments and they allow for general predictions. They can also represent the first steps in a new field of research, leading to further studies and advances.

Now that you’ve reviewed the main concepts of correlational studies and why they matter, let’s see three important research examples in different fields of psychology and understand how all of this comes to life!

Study #1: Biological Basis of Behavior – A Debate on Nature Versus Nurture

We can easily think of how our genetics influence physical traits like height, hair and eye color. But have you ever considered that your genetics might also play a big role on psychological traits like personality and interests? In 1990 psychologists Thomas Bouchard, David Lykken, and their associates investigated the influence our genes have in psychological attributes. This was hard research to accept at the time considering that for the past fifty years, psychology was mainly focused on behaviorism and how the environment determines behavior. Bouchard and Lykken’s study brought the debate of nature versus nurture back to the spotlight, determined to clarify the genes’ and the environment’s role in who we are.

For this, Bouchard and Lykken conducted a study with monozygotic twins (identical twins) who had been separated at birth and raised in different environments and compared the results with identical twins who had been raised together. Note that this is a study in which one couldn’t simply replicate the situation in laboratory conditions, so a correlational study was the best way to analyze the data of real individuals in this situation.

Bouchard and Lykken gathered a huge amount of data from each pair of twins. They used a variety of personality trait scales, aptitude and occupational interest inventories, intelligence tests, family environment scales and interviews. At the end of the first part of the research, Bouchard and Lykken had information concerning the twins’ physiological traits, intelligence, personality, psychological interests and social attitudes. Next, Bouchard and Lykken analyzed the correlation between the twins in all these fields.

The results were surprising. If the environment were responsible for individual differences, identical twins reared together should be more similar than identical twins reared apart. However, that was not what the results showed. Both categories of twins had a very similar correlational coefficient that neared +1.00. This means that regardless of having being raised in the same or different environments, each person was very similar to his twin in all traits.

Based on this we can say that genetic factors strongly influence human behavior in a variety of ways, both physiological and psychological. This could be seen as a problematic conclusion since we like to put so much importance on environmental factors like education and parenting as if that alone determined who we grow to be, what interests we develop, what careers we choose and so on. However, it is not the case for giving up on all our efforts in life thinking that eventually the genes will just take over and determine our fate.

Bouchard and Lykken emphasize that although intelligence is mainly determined by genetic factors, it can still be enhanced by experiences. Approximately 70% of intelligence is genetically determined, which means there is still 30% that can be worked on or ignored in the environment, either at home with parents or at school with teachers and mentors.

The same can be applied to the other traits. For example, even if your genes hold a natural strength towards communication skills, none of it will matter if you don’t get an opportunity in your environment to make that skill emerge and develop. Recent research on identical twins shows that the older the twins, the more similar they are. Another way to say this is that the more experiences you have, the more your genes can be expressed.

As human beings, we are determined by a combination of genetic and environmental influences. We are nature and nurture. Genes don’t mean destiny, but that doesn’t mean we can ignore their influences on our physiological and psychological characteristics. Let’s truly understand the components of our behavior and overcome the genes versus environment dichotomy.

Study #2: Personality – Who is in Control of Your Life?

Do you think your actions are what matter the most for the outcome of your life? Or do you think that external forces like fate and luck have a major influence in the paths you take? This kind of personal belief, called  locus of control , is associated with all sorts of behaviors we show in different areas of life. The locus of control and its influence on behavior was first studied by the influential psychologist and behaviorist  Julian Rotter in 1966.

Rotter proposed that the way individuals interpret what happens to them and where they put the responsibility for the events in their lives is an important part of the personality that can be used to predict tendencies in certain behaviors. When a person attributes the consequences of their behavior to factors such as luck, fate, and other greater forces, this person believes in an external locus of control . On the other hand, a person that identifies the consequences of her behavior to her own actions believes in an  internal locus of control .

To measure locus of control, Rotter developed a scale called I-E Scale, where “I” stands for “Internal” and “E” for “External.” The scale contains many pairs of statements, and the participant must choose the one that best fits his beliefs. A few examples of the pair of statements are “Many of the unhappy things in people’s lives are partly due to bad luck” versus “People’s misfortunes result from the mistakes they make,” and “Becoming a success is a matter of hard work; luck has little or nothing to do with it” versus “Getting a good job depends mainly on being in the right place at the right time.”

After measuring the locus of control of a relevant quantity of participants, Rotter analyzed the correlation between internal or external locus of control and behaviors such as gambling, persuasion, smoking and achievement motivation. His findings demonstrated that:

• External individuals are more likely to gamble on risky bets while internal individuals prefer “sure things” and moderate odds on the long run.

• Internal individuals are more efficient on persuading peers to change their attitudes and more resistant to manipulation than external individuals.

• Because an internal locus of control is related to self-control, smokers tend to be significantly more external oriented. Those who successfully quit smoking are more internally oriented.

• Internal individuals are more motivated to achieve success than those who believe their lives are ruled by forces outside of their control. Examples of achievements included plans to attend college and time spent on homework.

So translating into terms of correlational studies, there was, for example, a strong correlation between “internal locus of control” and “achievement motivation,” as the correlation coefficient between these two variables neared +1.00.

Furthermore, Rotter identified three sources for the development of an external or internal locus of control: cultural differences, socioeconomic differences, and parenting style. In conclusion, Rotter proposed that locus of control is an important component of personality that explains the differences in behavior between two people who are faced with the same situation. This belief determines the way we interpret the consequences of our behavior and influences the actions we take in our lives.

Study #3: Motivation and Emotion – The Effects of Stress on Our Health

Nowadays it’s almost common sense that stress has an impact on our health, but this was not always an easily accepted idea. In 1967, Thomas Holmes and Richard Rahe  studied the correlation between stress and illness. This was a psychosomatic  research because it studied the connection between psychological factors and physical problems.

Since it wouldn’t be ethical to put people under stressful situations to study whether or not they developed more health problems than a comfortable control group, this research was made using the correlational method. First, Holmes and Rahe designed a scale to measure stress in a variety of life situations, which included both happy and unhappy events, like Christmas and death of a spouse. This was because, according to Holmes and Rahe, stress happens in any situation where there is a need for psychological readjustment. This scale was called the Social Readjustment Rating Scale (SRRS). After having a huge amount of participants answer the scale, Holmes and Rahe studied the correlations between high levels of stress and illnesses.

As you may have already predicted, a strong positive correlation between stress and illness was found. The participants who had had a low level of stress in the past six months reported an average of 1.4 illnesses for the same period. A medium level of stress had an average of 1.9 illnesses and a high level of stress, 2.1 illnesses.

However, we also know that stress is only one component that influences health, and the connection between stress and illness is way more complex than a correlational study can show. Aware of that, Holmes and Rahe cited other factors that must be taken into consideration to help predict psychosomatic problems. They are:

• Your experience with stressful events

• Your coping skills

• The strength of your immune system

• Your way of dealing with health problems when they occur

Psychologists and doctors now recognize that the vast majority of illnesses are influenced by psychological factors, either at their development or in the way they are treated. This puts an end to Descartes’ classical view of split mind and body. Humans are complex beings, who must be understood and treated in their wholeness for an efficient prevention of illness and promotion of health.

So what do you think of each of these correlational study examples? They are in different areas of psychology (Biological Bases of Behavior, Personality, and Motivation and Emotion), so you can encounter this type of research in many questions of the AP® Psychology exam. How do you understand the influence of genetics on your behavior? Is your locus of control more internal or external? What examples of psychosomatic problems have you seen in your day to day experience? Share in the comments below!

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Correlational Research – Steps & Examples

Published by Carmen Troy at August 14th, 2021 , Revised On August 29, 2023

In correlational  research design , a researcher measures the association between two or more variables or sets of scores. A researcher doesn’t have control over the  variables .

Example:  Relationship between income and age.

Types of Correlations

Based on the number of variables

Based on the direction of change of variables

When to Use Correlation Design?

Correlation research design is used when experimental studies are difficult to design. 

Example: You want to know the impact of tobacco on people’s health and the extent of their addiction. You can’t distribute tobacco among your participants to understand its effect and addiction level. Instead of it, you can collect information from the people who are already addicted to tobacco and affected by it.

It is used to identify the association between two or more variables.

Example: You want to find out whether there is a correlation between the increasing population and poverty among the people. You don’t think that an increasing population leads to unemployment, but identifying a relationship can help you find a better answer to your study.

Example: You want to find out whether high income causes obesity. However, you don’t see any relationship. However, you can still find out the association between the lifestyle, age, and eating patterns of the people to make predictions of your research question.

Does your Research Methodology Have the Following?

• Great Research/Sources
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If not, we can help. Our panel of experts makes sure to keep the 3 pillars of Research Methodology strong.

How to Conduct Correlation Research?

Step 1: select the problem.

You can select the issues according to the requirement of your research. There are three common types of problems as follows;

• Is there any relationship between the two variables?
• How well does a variable predict another variable?
• What could be the association between a large number of variables and what predictions you can make?

Step 2: Select the Sample

You need to  select the sample  carefully and randomly if necessary. Your sample size should not be more than 30.

Step 3: Collect the Data

There are  various types of data collection methods  used in correlational research. The most common methods used for data collection are as follows:

Surveys  are the most frequently used method for collecting data. It helps find the association between variables based on the participants’ responses selected for the study. You can carry out the surveys online, face-to-face, and on the phone. 

Example: You want to find out the association between poverty and unemployment. You need to distribute a questionnaire about the sources of income and expenses among the participants. You can analyse the information obtained to identify whether unemployment leads to poverty.

Naturalistic Observation

In the naturalistic observation method, you need to collect the participants’ data by observing them in their natural surroundings. You can consider it as a type of field research. You can observe people and gather information from them in various public places such as stores, malls, parks, playgrounds, etc. The participants are not informed about the research. However, you need to ensure the anonymity of the participants. It includes both qualitative and quantitative data.

Example: You want to find out the correlation between the price hike of vegetables and whether changes. You need to visit the market and talk to vegetable vendors to collect the required information.  You can categorise the information according to the price, whether change effects and challenges the vendors/farmers face during such periods.

Archival Data

Archival data is a type of data or information that already exists. Instead of collecting new data, you can use the existing data in your research if it fulfills your research requirements. Generally, previous studies or theories, records, documents, and transcripts are used as the primary source of information. This type of research is also called retrospective research.

Example: Suppose you want to find out the relation between exercise and weight loss. You can use various scholarly journals, health records, and scientific studies and discoveries based on people’s age and gender. You can identify whether exercise leads to significant weight loss among people of various ages and gender.

What is Causation?

The association between cause and effect is called  causation . You can identify the correlation between the two variables, but they may not influence each other. It can be considered as the limitation of correlation research.

Example: You’ve found that people who exercise regularly lost maximum weight. However, it doesn’t prove that people who don’t use will gain weight. There could be many other possible variables, such as a healthy diet, age, stress, gender, and health condition, impacting people’s weight. You can’t find out the causation of your research problem. Still, you can collect and analyse data to support the theory. You can only predict the possibilities of the method, phenomena, or problem you are studying.

Frequently Asked Questions

How to describe correlational research.

Correlational research examines the relationship between two or more variables. It doesn’t imply causation but measures the strength and direction of association. Statistical analysis determines if changes in one variable correspond to changes in another, helping understand patterns and predict outcomes.

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Content analysis is used to identify specific words, patterns, concepts, themes, phrases, or sentences within the content in the recorded communication.

Descriptive research is carried out to describe current issues, programs, and provides information about the issue through surveys and various fact-finding methods.

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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.

1 ISPOR – International Society for Pharmacoeconomics and Outcomes Research

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• Berger M. L., Dreyer N., Anderson F., Towse A., Sedrakyan A., Normand S.L. Prospective observational studies to address comparative effectiveness: The ispor good research practices task force report. Value in Health. 2012; 15 (2):217–230. Retrieved from http://www ​.sciencedirect ​.com/science/article ​/pii/S1098301512000071 . [ PubMed : 22433752 ]
• Elwood M. Critical appraisal of epidemiological studies and clinical studies. 3rd ed. Oxford: Oxford University Press; 2007.
• Fleurence R. L., Naci H., Jansen J.P. The critical role of observational evidence in comparative effectiveness research. Health Affairs. 2010; 29 (10):1826–1833. [ PubMed : 20921482 ]
• Friedman C. P., Wyatt J.C. Evaluation methods in biomedical informatics. 2nd ed. New York: Springer Science + Business Media, Inc; 2006.
• Higgins J. P. T., Green S., editors. Cochrane handbook for systematic reviews of interventions. London: The Cochrane Collaboration; 2011. (Version 5.1.0, updated March 2011) Retrieved from http://handbook ​.cochrane.org/
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• Nielsen A. S., Halamka J. D., Kinkel R.P. Internet portal use in an academic multiple sclerosis center. Journal of the American Medical Informatics Association. 2012; 19 (1):128–133. [ PMC free article : PMC3240754 ] [ PubMed : 21571744 ]
• Park E. S., Peccoud M. R., Wicks K. A., Halldorson J. B., Carithers R. L. Jr., Reyes J. D., Perkins J.D. Use of an automated clinical management system improves outpatient immunosuppressive care following liver transplantation. Journal of the American Medical Informatics Association. 2010; 17 (4):396–402. [ PMC free article : PMC2995663 ] [ PubMed : 20595306 ]
• Shamliyan T., Kane R. L., Dickinson S. A systematic review of tools used to assess the quality of observational studies that examine incidence or prevalence and risk factors for diseases. Journal of Clinical Epidemiology. 2010; 63 (10):1061–1070. [ PubMed : 20728045 ]
• Staes C. J., Evans R. S., Rocha B. H. S. C., Sorensen J. B., Huff S. M., Arata J., Narus S.P. Computerized alerts improve outpatient laboratory monitoring of transplant patients. Journal of the American Medical Informatics Association. 2008; 15 (3):324–332. [ PMC free article : PMC2410008 ] [ PubMed : 18308982 ]
• Vandenbroucke J. P., von Elm E., Altman D. G., Gotzsche P. C., Mulrow C. D., Pocock S. J., Egger M. for the strobe Initiative. Strengthening the reporting of observational studies in epidemiology ( strobe ): explanation and elaboration. International Journal of Surgery. 2014; 12 (12):1500–1524. Retrieved from http://www.sciencedirect.com/science/article/pii/ s174391911400212x . [ PubMed : 25046751 ]
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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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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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• Scollon, C. N. (2020). Research designs. In R. Biswas-Diener & E. Diener (Eds), Noba textbook series: Psychology. Champaign, IL: DEF publishers. Retrieved from http://noba.to/acxb2thy ↵

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• 10 Research Question Examples to Guide Your Research Project

10 Research Question Examples to Guide your Research Project

Published on October 30, 2022 by Shona McCombes . Revised on October 19, 2023.

The research question is one of the most important parts of your research paper , thesis or dissertation . It’s important to spend some time assessing and refining your question before you get started.

The exact form of your question will depend on a few things, such as the length of your project, the type of research you’re conducting, the topic , and the research problem . However, all research questions should be focused, specific, and relevant to a timely social or scholarly issue.

Once you’ve read our guide on how to write a research question , you can use these examples to craft your own.

Note that the design of your research question can depend on what method you are pursuing. Here are a few options for qualitative, quantitative, and statistical research questions.

Other interesting articles

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

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You probably noticed that the less you sleep, the more tired you are. You may have also observed that the more you rehearse a skill like writing, the better you get at it. These are simple observations in life that set the foundations of correlational research. Although these observations need to be tested scientifically for them to become facts, these examples are the basis of correlational studies. 

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What type of research method is correlational?

Are confounding variables an issue in naturalistic observation correlations? 

Are variables manipulated in correlational research? 

If a correlation of 0.2 were found, this would be considered a               correlation.

Is it easy to establish the reliability and validity of archival research? 

What does the correlation coefficient tell us? 

The Children's Health Foundation Paediatric Asthma Registry was used to observe the relationship between asthma and its prevalence in children. What type of correlational research would the study carry out? 

Can you establish cause-and-effect in correlational research? 

If a correlation of 0.53 were found, this would be considered a                   correlation.

Researchers went to a supermarket (natural setting) to observe how many people buy ice cream on a hot day. What type of correlational research is this? 

If a correlation of 0.99 were found, this would be considered a ____ correlation.

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• In this explanation, you will find a presentation of correlational studies in psychology .
• The different types of correlational studies will be presented.
• Moving on, you will learn about interpreting correlational studies' results.
• You will also learn why correlational studies do not let researchers establish cause and effect.
• Lastly, the correlational study advantages and disadvantages of psychology will be discussed.

Correlational Study Psychology

Correlational analyses are widely used in psychological research. Correlation research is based on observations between variables ; this means there is no experimental manipulation involved.

Correlational research aims to observe whether or not two variables are related and, if so, how strong the association is.

Correlational studies are a non-experimental research method and a statistical analysis used to understand the linear relationship or association between two variables.

The steps that researchers take when designing a correlational study are the following:

• Stating the research question.
• Identifying the variables.
• Writing of hypothesis statements.
• Conducting the research and gathering data.
• Analysing the data.

Types of Correlational Studies

Three types of correlation studies exist, and we will describe them in detail below, with examples. Further, the different study types will be evaluated, presenting the strengths and weaknesses of each one.

Correlational Studies: Naturalistic observation

In naturalistic observation correlation studies, researchers record observations of variables in a natural setting; this is a non-experimental method in which no variables are manipulated.

An example of this type of correlational research is researchers going to a supermarket (natural setting) and observing how many people buy ice cream on a hot day.

A strength of naturalistic observational research is that it allows researchers to observe participants in a natural setting. This makes it more likely that participants will show their real behaviour, increasing the results' validity. In laboratory settings, for example, participants may not behave as genuinely due to the setting itself.

However, some limitations should be considered, such as the difficulty in limiting confounding factors, which can affect and reduce the study's validity.

Correlational Studies: Survey method

The survey method uses surveys and questionnaires to measure the researchers' variables.

An example would be using questionnaires to determine the highest level of education and socioeconomic status.

The research aim may be to determine if there is a relationship between the level of education and the individual's income.

The advantages of this research method are that it is relatively inexpensive, does not take too much time, and can recruit many participants in a short time. The method usually uses random samples for recruitment, so the research results are more generalisable than other sampling methods.

However, respondents may answer in a socially desirable manner rather than honestly, which reduces the validity of the results.

Correlational Studies: Archival research

Archival research is a type of correlational research that uses secondary data, such as previous research, case studies, historical documents, and medical registries, to measure variables.

Using the Children's Health Foundation Paediatric Asthma Registry to observe the relationship between asthma and prevalence in children is an example of archival research.

The advantage of correlational archival research is that it can be cheaper than alternative methods. Data is readily available, and researchers can obtain data that may no longer be collected, such as documents from historical periods.

Nevertheless, the disadvantages of archival research should be considered. While conducting archival research, the researcher has no control over data collection methods, making it difficult to determine if the data is reliable and valid. Another issue is some data may be missing that is needed for the research.

Correlational Studies: Interpretations

In the statistical analysis of correlation data, a correlation coefficient is calculated.

The correlation coefficient ( r ) is a measure that determines the strength of the relationship between the two variables.

The correlation coefficient ( r ) values can range from +1 to -1.

A positive number indicates a positive relationship between the variables; if one variable increases, the other is also expected to increase.

A negative coefficient indicates a negative relationship between the variables. If one variable increases, the other is expected to decrease.

A coefficient of 0 indicates no relationship between the two variables.

The value of the correlation coefficient determines the strength of the correlation data:

• When r = 0, then there is no correlation.
• When r is between 0.1- 0.39, there is a w eak correlation.
• When r is between 0.4 - 0.69, there is a m oderate correlation.
• When r is between 0.7 and 0.99, there is a strong correlation.
• When r equals 1, then there is a perfect correlation.

Scatter plots are typically used to show the relationship between variables by plotting the data when reporting correlation data. Scatterplots allow us to visually see the strength of the correlation and the direction between the variables.

If the data points are close to the gradient line and have a positive gradient, this indicates a positive relationship. If the gradient is negative, the association is negative.

Correlational Study Cause and Effect

One of the main ideas researchers need to remember when conducting correlational research is that researchers can't infer causation in correlational studies.

Let's say that a research group tests whether there is a relationship between autism and organic food sales. To test this, they gather existing data from governmental databases. And indeed, they find that in the last ten years, autism diagnosis has increased, and so have organic food sales. There is a positive relationship between the variables.

The research does not imply that autism diagnosis makes people buy organic food, nor does it mean that organic food sales cause autism. In this example, it may be obvious, but in real research, researchers need to be careful about making such inferences.

It is possible that, in some cases, one variable does indeed cause the other one. Further experimental research needs to be conducted to support or disprove it in such cases.

Example of Correlational Research

Researching the relationship between variables has been in the spotlight of psychological research for decades.

Examples include studies looking into the relationship between alcohol consumption and unemployment, the relationship between academic performance and career success, or the relationship between income levels and crime.

A correlation study will start by defining the research question. For example, a study may examine the relationship between self-esteem and social anxiety. Based on previous findings, researchers may hypothesise that there is an existing negative correlation between the two.

The negative correlation would suggest that as self-esteem increases, social-anxiety decreases, or vice versa.

Researchers then decide which inventories or questionnaires will be used to measure the two variables. After this, the correlational statistical test will be calculated.

The statistical analysis may provide a significant result in which the correlation coefficient is -0.78, allowing the researchers to conclude that there is indeed a negative association between self-esteem and social anxiety.

An important thing to note in correlational research is that a negative correlation means a specific variable will increase/ decrease. Any of the variables can increase or decrease. The only thing we can be sure of is that as one increases, the other will decrease.

The researchers may plot their data on a scatterplot, so they and readers can visualise the results.

Regarding the causality effect, it is tempting to suggest that low self-esteem makes individuals experience social anxiety. And although this could be the case, it cannot be established with a correlational test.

Correlational Study Advantages and Disadvantages Psychology

In this section, correlational studies' advantages and disadvantages are critically reviewed.

One of the main advantages of correlational research is that it is quick and easy to conduct. It does not require great statistical knowledge for researchers to be able to use it.

Furthermore, correlations can be tested for existing data, which can inspire future research and be helpful when the researcher may have limited access to the phenomenon, e.g. if it's based on past events.

One of the main disadvantages of correlational research is it can't establish whether variables are causally related.

Cause and effect mean that although research can establish a relationship between two variables, it cannot infer whether one of the variables causes a change in the other or vice versa.

Since correlational studies only measure the co-variables, other potential confounding factors are not considered. The confounding variables may be a better explanatory factor for the study's outcome, making it difficult to determine the validity of the results.

Correlational Studies - Key takeaways

• Correlation studies are a non-experimental research method used to understand the linear relationship/ association between two variables.
• Three types of correlational studies are naturalistic observational studies, surveys, and archival correlational studies.
• In the statistical analysis of correlational data, a correlation coefficient is calculated; it tells researchers about the strength and direction of a relationship between two variables.
• The calculated correlation coefficient value can range from -1 to +1.
• Correlation research has many uses in psychology, for example, to obtain preliminary results that inform researchers whether variables should be explored using experimental research to establish causation relationships .

Flashcards in Correlational Studies 11

Non-experimental.

The strength of association.

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Frequently Asked Questions about Correlational Studies

What is a correlational study?

Correlational studies are a non-experimental research method used to understand the linear relationship/association between two variables determined by statistical analysis. 

What is the purpose of a correlational study?

The purpose of correlational research is to identify if there is a relationship between two variables and, if so, how strongly associated these variables are.

How do you write a hypothesis for a correlational study?

The hypothesis for correlational studies should highlight the variables being investigated, and the variables included should be operationalised. This means that the variables should be clearly defined and state how they will be measured in the study. (e.g., measuring anxiety using the Generalised Anxiety Disorder Scale).

How do you conduct a correlational study?

The steps that researchers take when conducting a correlational study are the following:

What is an example of a correlational study?

An example of a correlational study could be observing the number of ice creams sold on the hottest day in the supermarket.

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Correlational Study: Design, Methods and Examples

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Correlational research is a type of research design used to examine the relationship between two or more variables. In correlational research, researchers measure the extent to which two or more variables are related, without manipulating or controlling any of the variables.

Whether you are a beginner or an experienced researcher, chances are you’ve heard something about correlational research. It’s time that you learn more about this type of study more in-depth, since you will be using it a lot.

• What is correlation?
• When to use it?
• How is it different from experimental studies?
• What data collection method will work?

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What Is Correlational Research: Definition

A correlational research is a preliminary type of study used to explore the connection between two variables. In this type of research, you won’t interfere with the variables. Instead of manipulating or adjusting them, researchers focus more on observation.  Correlational study is a perfect option if you want to figure out if there is any link between variables. You will conduct it in 2 cases:

• When you want to test a theory about non-causal connection. For example, you may want to know whether drinking hot water boosts the immune system. In this case, you expect that vitamins, healthy lifestyle and regular exercise are those factors that have a real positive impact. However, this doesn’t mean that drinking hot water isn’t associated with the immune system. So measuring this relationship will be really useful.
• When you want to investigate a causal link. You want to study whether using aerosol products leads to ozone depletion. You don’t have enough expenses for conducting complex research. Besides, you can’t control how often people use aerosols. In this case, you will opt for a correlational study.

Correlational Study: Purpose

Correlational research is most useful for purposes of observation and prediction. Researcher's goal is to observe and measure variables to determine if any relationship exists. In case there is some association, researchers assess how strong it is. As an initial type of research, this method allows you to test and write the hypotheses. Correlational study doesn’t require much time and is rather cheap.

Correlational Research Design

Correlational research designs are often used in psychology, epidemiology , medicine and nursing. They show the strength of correlation that exists between the variables within a population. For this reason, these studies are also known as ecological studies.  Correlational research design methods are characterized by such traits:

• Non-experimental method. No manipulation or exposure to extra conditions takes place. Researchers only examine how variables act in their natural environment without any interference.
• Fluctuating patterns. Association is never the same and can change due to various factors.
• Quantitative research. These studies require quantitative research methods . Researchers mostly run a statistical analysis and work with numbers to get results.
• Association-oriented study. Correlational study is aimed at finding an association between 2 or more phenomena or events. This has nothing to do with causal relationships between dependent and independent variables .

Correlational Research Questions

Correlational research questions usually focus on how one variable related to another one. If there is some connection, you will observe how strong it is. Let’s look at several examples.

Correlational Research Types

Depending on the direction and strength of association, there are 3 types of correlational research:

• Positive correlation If one variable increases, the other one will grow accordingly. If there is any reduction, both variables will decrease.

• Negative correlation All changes happen in the reverse direction. If one variable increases, the other one should decrease and vice versa.

• Zero correlation No association between 2 factors or events can be found.

Correlational Research: Data Collection Methods

There are 3 main methods applied to collect data in correlational research:

• Surveys and polls
• Naturalistic observation
• Secondary or archival data.

It’s essential that you select the right study method. Otherwise, it won’t be possible to achieve accurate results and answer the research question correctly. Let’s have a closer look at each of these methods to make sure that you make the right choice.

Surveys in Correlational Study

Survey is an easy way to collect data about a population in a correlational study. Depending on the nature of the question, you can choose different survey variations. Questionnaires, polls and interviews are the three most popular formats used in a survey research study. To conduct an effective study, you should first identify the population and choose whether you want to run a survey online, via email or in person.

Naturalistic Observation: Correlational Research

Naturalistic observation is another data collection approach in correlational research methodology. This method allows us to observe behavioral patterns in a natural setting. Scientists often document, describe or categorize data to get a clear picture about a group of people. During naturalistic observations, you may work with both qualitative and quantitative research information. Nevertheless, to measure the strength of association, you should analyze numeric data. Members of a population shouldn’t know that they are being studied. Thus, you should blend in a target group as naturally as possible. Otherwise, participants may behave in a different way which may cause a statistical error. 

Correlational Study: Archival Data

Sometimes, you may access ready-made data that suits your study. Archival data is a quick correlational research method that allows to obtain necessary details from the similar studies that have already been conducted. You won’t deal with data collection techniques , since most of numbers will be served on a silver platter. All you will be left to do is analyze them and draw a conclusion. Unfortunately, not all records are accurate, so you should rely only on credible sources.

Pros and Cons of Correlational Research

Choosing what study to run can be difficult. But in this article, we are going to take an in-depth look at advantages and disadvantages of correlational research. This should help you decide whether this type of study is the best fit for you. Without any ado, let’s dive deep right in.

Advantages of Correlational Research

Obviously, one of the many advantages of correlational research is that it can be conducted when an experiment can’t be the case. Sometimes, it may be unethical to run an experimental study or you may have limited resources. This is exactly when ecological study can come in handy.  This type of study also has several benefits that have an irreplaceable value:

• Works well as a preliminary study
• Allows examining complex connection between multiple variables
• Helps you study natural behavior
• Can be generalized to other settings.

If you decide to run an archival study or conduct a survey, you will be able to save much time and expenses.

Disadvantages of Correlational Research

There are several limitations of correlational research you should keep in mind while deciding on the main methodology. Here are the advantages one should consider:

• No causal relationships can be identified
• No chance to manipulate extraneous variables
• Biased results caused by unnatural behavior
• Naturalistic studies require quite a lot of time.

As you can see, these types of studies aren’t end-all, be-all. They may indicate a direction for further research. Still, correlational studies don’t show a cause-and-effect relationship which is probably the biggest disadvantage. 

Difference Between Correlational and Experimental Research

Now that you’ve come this far, let’s discuss correlational vs experimental research design . Both studies involve quantitative data. But the main difference lies in the aim of research. Correlational studies are used to identify an association which is measured with a coefficient, while an experiment is aimed at determining a causal relationship.  Due to a different purpose, the studies also have different approaches to control over variables. In the first case, scientists can’t control or otherwise manipulate the variables in question. Meanwhile, experiments allow you to control variables without limit. There is a  causation vs correlation  blog on our website. Find out their differences as it will be useful for your research.

Example of Correlational Research

Above, we have offered several correlational research examples. Let’s have a closer look at how things work using a more detailed example.

Example You want to determine if there is any connection between the time employees work in one company and their performance. An experiment will be rather time-consuming. For this reason, you can offer a questionnaire to collect data and assess an association. After running a survey, you will be able to confirm or disprove your hypothesis.

Correlational Study: Final Thoughts

That’s pretty much everything you should know about correlational study. The key takeaway is that this type of study is used to measure the connection between 2 or more variables. It’s a good choice if you have no chance to run an experiment. However, in this case you won’t be able to control for extraneous variables . So you should consider your options carefully before conducting your own research. 

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Frequently Asked Questions About Correlational Study

1. what is a correlation.

Correlation is a connection that shows to which extent two or more variables are associated. It doesn’t show a causal link and only helps to identify a direction (positive, negative or zero) or the strength of association.

2. How many variables are in a correlation?

There can be many different variables in a correlation which makes this type of study very useful for exploring complex relationships. However, most scientists use this research to measure the association between only 2 variables.

3. What is a correlation coefficient?

Correlation coefficient (ρ) is a statistical measure that indicates the extent to which two variables are related. Association can be strong, moderate or weak. There are different types of p coefficients: positive, negative and zero.

4. What is a correlational study?

Correlational study is a type of statistical research that involves examining two variables in order to determine association between them. It’s a non-experimental type of study, meaning that researchers can’t change independent variables or control extraneous variables.

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Correlational Research

Rajiv S. Jhangiani; I-Chant A. Chiang; Carrie Cuttler; and Dana C. Leighton

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.

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

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Chapter 3. Psychological Science

3.2 Psychologists Use Descriptive, Correlational, and Experimental Research Designs to Understand Behaviour

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 behaviour 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 3.2, 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.

Descriptive Research: Assessing the Current State of Affairs

Descriptive research is designed to create a snapshot of the current thoughts, feelings, or behaviour of individuals. This section reviews three types of descriptive research : case studies , surveys , and naturalistic observation (Figure 3.4).

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 behaviour . 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).

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 are questions 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 of 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 behaviours 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 10 doctors prefer Tymenocin” or “The median income in the city of Hamilton is $46,712.” Yet other times (particularly in discussions of social behaviour), 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 3.3.

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 3.5 where most of the scores are located near the centre 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 .

A distribution can be described in terms of its central tendency — that is, the point in the distribution around which the data are centred — and its dispersion, or spread . The arithmetic average, or arithmetic mean , symbolized by the letter M , 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 3.5 the mean height of the students is 67.12 inches (170.5 cm). 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 3.6), 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 3.6 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).

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 3.6 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 , as seen in Figure 3.7.

Or they may be more spread out away from it, as seen in Figure 3.8.

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 3.5 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 behaviour. Case studies provide detailed information about a single person or a small group of people, surveys capture the thoughts or reported behaviours of a large population of people, and naturalistic observation objectively records the behaviour 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 behaviours 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 as shown in Figure 3.9, where the curved arrow represents the expected correlation between these two variables.

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 3.10 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 3.10 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 3.10 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 3.10 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 .

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 3.11 shows a multiple regression analysis in which three predictor variables (Salary, job satisfaction, and years employed) are used to predict a single outcome (job performance). 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.

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 behaviour will cause increased aggressive play in children. He has collected, from a sample of Grade 4 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 behaviour. Although the researcher is tempted to assume that viewing violent television causes aggressive play, there are other possibilities. One alternative 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 (Figure 3.13):

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 (Figure 3.14).

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 like to watch violent television and who also behave aggressively in comparison to children whose parents use less harsh discipline (Figure 3.15)

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 behaviour 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 behaviour 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 Behaviour

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 3.16):

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 behaviour. 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 behaviour) was the level and duration of noise delivered to the opponent. The design of the experiment is shown in Figure 3.17

Two advantages of the experimental research design are (a) the assurance that the independent variable (also known as the experimental manipulation ) occurs prior to the measured dependent variable, and (b) 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 behaviour, 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 behaviours 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?

Image Attributions

Figure 3.4: “ Reading newspaper ” by Alaskan Dude (http://commons.wikimedia.org/wiki/File:Reading_newspaper.jpg) is licensed under CC BY 2.0

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. (1909/1964). 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.

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

Long Descriptions

Figure 3.6 long description: There are 25 families. 24 families have an income between $44,000 and $111,000 and one family has an income of $3,800,000. The mean income is $223,960 while the median income is $73,000. [Return to Figure 3.6]

Figure 3.10 long description: Types of scatter plots.

• Positive linear, r=positive .82. The plots on the graph form a rough line that runs from lower left to upper right.
• Negative linear, r=negative .70. The plots on the graph form a rough line that runs from upper left to lower right.
• Independent, r=0.00. The plots on the graph are spread out around the centre.
• Curvilinear, r=0.00. The plots of the graph form a rough line that goes up and then down like a hill.
• Curvilinear, r=0.00. The plots on the graph for a rough line that goes down and then up like a ditch.

[Return to Figure 3.10]

Introduction to Psychology - 1st Canadian Edition Copyright © 2014 by Jennifer Walinga and Charles Stangor is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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How you ask matters: The wording of your research question affects your analysis

Writing a research question for your study can be a daunting task. The appropriate statistical language must be used for each question in order to determine the type of hypothesis test you can run. Therefore, how you ask your research question is the foundation for the rest of your analysis! The first step of the process is to determine what the goal of your research will be. Are you attempting to look at differences in means, or are you just looking to see if two variables are related to one another? Once you have determined what your ultimate research goal is, you can move on to writing the research questions in statistical language — AKA lining up the research question/hypothesis with the type of statistical test that you want to run.

There are three types of statistical research questions, each with its own associated analysis. These three types are descriptive, comparative, and relational. This blog will discuss all three and the appropriate statistical analysis for each.

First, there are descriptive research questions. These types of questions seek to simply describe a situation, and do not include any hypothesis testing. For these questions, means and standard deviations or frequencies and percentages would be calculated, depending on the level of measurement of the variables.

For example, imagine that you are a researcher interested in the leadership style of ice cream shop employers and the job satisfaction of their employees. Descriptive research questions for this topic could be:

• What is the leadership style of ice cream shop employers? • What is the job satisfaction of ice cream shop employees?

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Next is a comparative research question. These types of questions are used to compare variables or groups to assess differences between them. By using the word difference in your research question, you can create a comparative research question. Comparative analyses that seek to assess differences include the t-test and the analysis of variance (ANOVA) family of tests. A question of this type could be:

• Is there a significant difference in the job satisfaction of ice cream shop employees who have employers with different leadership styles?

Lastly, there are relational research questions. These types of questions seek to assess the relationship between two or more variables or groups. This type of question could be phrased in two different ways:

• Does the leadership style of ice cream shop employers predict job satisfaction of ice cream shop employees? • Is there a correlation between leadership style of ice cream shop employers and job satisfaction of ice cream shop employees?

Notice that here, there are two different relational questions. One uses the word predict while the other uses the word correlation. There are several “buzzwords” in quantitative research that indicate very specific analyses, including predict, correlation, difference, relationship, positive, negative, and more. The use of the word predict will always indicate the use of a regression analysis. However, the use of the word correlate indicates the use of a simple correlation analysis (Note that this is different from describing your research design as correlational—which simply implies that you are assessing a relationship).

Alternatively, you can use the word relationship instead of correlate or predict. By using the word relationship instead, you have created a relational research question that is more general than using the words predict or correlate, leaving you with more options when it comes down to cementing your data analysis.

Finally, it is important to note the use of the terms positive and negative within a research question. These terms are known as augmenting words, or words that can be added to your research question if you want to be specific with the hypothesized direction of the relationship between variables. These should only be used when there is significant evidence in the literature to support your research question and associated hypotheses. This is because using these terms limits your ability to reject your null hypothesis . For example, if you asked:

• Is there a significant positive correlation between age and the job satisfaction of ice cream shop employees? Your null hypothesis would be: • There is no significant positive correlation between age and the job satisfaction of ice cream shop employees.

This would mean that if you ran your results and found that there is a significant negative correlation between the two variables, you found a significant result, but you still could not reject your null hypothesis. This is because you predicted a significant positive correlation but did not find a positive relationship. Therefore, words such as positive or negative should only be used when you want to make a specific directional hypothesis and there is substantial evidence in the literature to guide your hypothesis.

Overall, how you word your research question affects the inferences and conclusions you can form from the data—for example, if you only ask a descriptive question, you cannot assess if there are any differences between groups. Alternatively, if you ask a comparative question, you would not be able to determine if there are predictive relationships between your variables. Therefore, your entire dissertation should be carefully planned and orchestrated, even down to the specific wording of your research questions!

Top 150+ Correlational Research Topics For Students [2024]

Correlational research looks at how two or more things relate without saying one causes the other. It tries to find patterns and connections between different things to see how changes in one might be connected to changes in another.

In education, correlational studies are super important because they help us understand how different factors affect how well students learn. Whether looking at teaching methods or considering students’ backgrounds, correlational research helps teachers determine how to help students do better in school.

Our blog is here to give students interesting correlational research topics. We want to make it easy for students to find ideas and get excited about doing research. 

We aim to get you thinking and curious about how things are connected so you can learn more about them.

What is Correlation? An Introduction

Table of Contents

Correlation is defined as how two variables change simultaneously. It helps us comprehend their relationship. 

When two variables are correlated, changes in one tend to be associated with changes in the other, but it doesn’t necessarily mean that one causes the other. 

Correlation can be positive, meaning both variables move in the same direction, or negative, where they move in opposite directions. 

Understanding correlation is crucial in various fields like science, economics, and social sciences, as it allows us to identify patterns, make predictions, and better comprehend the complexities of the world around us.

Also Read: “ Top 151+ Quantitative Research Topics for ABM Students “.

Benefits of Correlational Research Topics For Students

Correlational research topics offer numerous benefits for students, allowing them to explore relationships between variables and understand the complexity of real-world phenomena. Here are several benefits of correlational research topics for students:

Enhances critical thinking skills

Engaging in correlational research encourages students to analyze data, draw conclusions, and evaluate the relationships between variables, fostering critical thinking abilities.

Provides real-world application

Correlational research topics often relate to everyday phenomena, allowing students to apply theoretical concepts to practical situations promoting a deeper understanding of the subject matter.

Fosters research skills

Conducting correlational studies equips students with valuable research skills, including data collection, analysis, and interpretation, essential for academic and professional success.

Stimulates curiosity and creativity

Exploring correlational research topics ignites curiosity and creativity, inspiring students to explore new ideas, generate hypotheses, and develop innovative solutions to complex problems.

Prepares for future academic pursuits

Engaging in correlational research prepares students for future academic endeavors by honing their research abilities and preparing them for more advanced research projects at higher levels of education.

List of Interesting Correlational Research Topics For Students

Here’s a list of interesting correlational research topics for students across various disciplines:

• The correlation between teacher enthusiasm and student engagement.
• The relationship between parental involvement and student academic performance.
• Correlating study habits with GPA in high school students.
• The impact of class size on student achievement.
• Relationship between technology use and learning outcomes.
• Correlation between sleep quality and academic success in college students.
• The correlation between extracurricular activity and academic achievement.
• Correlation between self-esteem and academic achievement.
• The influence of school climate on student behavior and achievement.
• Relationship between student-teacher rapport and academic success.

Health and Wellness

• Correlation between exercise frequency and mental health.
• Relationship between diet and stress levels in college students.
• The impact of social support on overall health.
• Correlating screen time with sleep quality in adolescents.
• The relationship between mindfulness practices and emotional well-being.
• Correlation between access to green spaces and physical activity levels.
• The influence of peer pressure on health-related behaviors.
• Relationship between music preference and stress reduction.
• The correlation between pet ownership and mental health.
• The relationship between outdoor recreation and overall wellness.

Social Sciences

• Correlation between socioeconomic status and academic achievement.
• The link between social media usage and self-esteem.
• The impact of family structure on social behavior.
• Correlation between political ideology and charitable giving.
• Relationship between cultural background and communication styles.
• The influence of peer group on academic motivation.
• Correlation between media consumption and attitudes towards diversity.
• Relationship between personality traits and career success.
• The impact of community involvement on civic engagement.
• Correlation between volunteering and life satisfaction.

Technology and Society

• The relationship between smartphone use and attention span.
• Correlation between video game usage and problem-solving skills.
• The influence of social media on interpersonal relationships.
• Relationship between Internet usage and academic performance.
• Correlation between online shopping habits and financial literacy.
• The impact of digital literacy on job opportunities.
• Relationship between virtual reality exposure and empathy levels.
• Correlation between social networking and political engagement.
• The relationship between technology use and environmental awareness.
• Correlation between online activism and real-world action.

Economics and Finance

• The relationship between household income and savings behavior.
• Correlation between education level and earning potential.
• The impact of inflation on consumer spending habits.
• Relationship between stock market performance and consumer confidence.
• Correlation between financial literacy and debt management.
• The influence of advertising on consumer purchasing decisions.
• Relationship between economic growth and unemployment rates.
• Correlation between housing prices and neighborhood demographics.
• The relationship between government spending and economic growth.
• Correlation between education funding and student outcomes.

Environmental Studies

• The relationship between air pollution and respiratory health.
• Correlation between waste management practices and environmental sustainability.
• The impact of deforestation on biodiversity.
• Relationship between climate change awareness and pro-environmental behaviors.
• Correlation between water quality and public health.
• The influence of renewable energy adoption on greenhouse gas emissions.
• Relationship between urbanization and wildlife habitat loss.
• Correlation between environmental regulations and industry practices.
• The relationship between sustainable agriculture and food security.
• Correlation between green infrastructure and urban heat island effect.
• The link between childhood trauma and adult mental health.
• Correlation between personality type and career choice.
• The effects of early attachment types on romantic relationships.
• Relationship between parental discipline strategies and child behavior.
• Correlation between introversion/extroversion and social networking.
• The effect of peer pressure on risk-taking behavior.
• The link between body image and social media use.
• Correlation between anxiety levels and academic performance.
• The relationship between self-esteem and relationship satisfaction.
• Correlation between happiness levels and gratitude practices.

Criminal Justice

• The association between childhood trauma and adult mental health.
• Correlation between access to education and recidivism rates.
• The impact of community policing on crime prevention.
• Relationship between substance abuse and criminal behavior.
• Correlation between gun control laws and violent crime rates.
• The influence of media portrayal on perceptions of crime.
• Relationship between juvenile delinquency and family dynamics.
• Correlation between sentencing disparities and race.
• The relationship between policing tactics and public trust.
• Correlation between restorative justice programs and rehabilitation rates.

Business and Management

• The relationship between employee satisfaction and productivity.
• Correlation between leadership style and team performance.
• The impact of workplace diversity on organizational success.
• The link between staff training programs and work happiness.
• Correlation between customer satisfaction and repeat business.
• The impact of company culture on employee turnover.
• Relationship between ethical business practices and consumer trust.
• Correlation between innovation and market competitiveness.
• The relationship between employee engagement and company profitability.
• Correlation between marketing strategies and brand loyalty.

Media and Communication

• The link between media consumption and political polarization.
• Correlation between advertising exposure and consumer behavior.
• The influence of media depiction on body image.
• Relationship between news consumption and knowledge of current events.
• Correlation between social media usage and interpersonal communication skills.
• The influence of celebrity endorsements on brand perception.
• Relationship between media violence exposure and aggression levels.
• Correlation between news bias and public opinion.
• The link between media literacy and critical thinking abilities.
• Correlation between reality television consumption and social attitudes.

Culture and Society

• The relationship between cultural diversity and creativity.
• Correlation between cultural heritage preservation and community identity.
• The impact of globalization on cultural values.
• Relationship between language diversity and social cohesion.
• Correlation between cultural norms and attitudes towards gender roles.
• Communication styles are influenced by cultural background.
• Relationship between cultural assimilation and mental health.
• Correlation between cultural festivals and community bonding.
• The relationship between cultural stereotypes and prejudice.
• Correlation between cultural adaptation and immigrant integration.

Sports and Recreation

• The relationship between sports participation and academic achievement.
• Correlation between exercise frequency and stress reduction.
• The impact of sports team success on school spirit.
• Relationship between youth sports involvement and leadership skills.
• Correlation between sports fandom and social connections.
• The influence of sports participation on self-esteem.
• Relationship between sportsmanship and moral development.
• Correlation between coaching style and athlete motivation.
• The relationship between sports injuries and long-term health outcomes.
• Correlation between sports specialization and athletic performance.

Science and Technology

• The relationship between science education and technological innovation.
• Correlation between technology use and environmental impact.
• The impact of science literacy on public policy attitudes.
• Relationship between STEM education and career opportunities.
• Correlation between scientific research funding and breakthrough discoveries.
• The influence of technology on scientific research methodologies.
• Relationship between science communication and public understanding.
• Correlation between technological advancements and quality of life.
• The relationship between science engagement and environmental conservation efforts.
• Correlation between technology adoption and societal changes.

Language and Linguistics

• The relationship between bilingualism and cognitive development.
• Correlation between language proficiency and academic success.
• The impact of language diversity on social integration.
• Relationship between language acquisition and brain development.
• Correlation between language use and cultural preservation.
• The influence of language barriers on access to healthcare.
• Relationship between language learning strategies and proficiency levels.
• Correlation between language policies and educational outcomes.
• The relationship between language evolution and societal change.
• Correlation between language dialects and regional identities.

Travel and Tourism

• The relationship between travel experiences and cultural awareness.
• Correlation between tourism development and economic growth.
• The impact of travel restrictions on tourism industries.
• Relationship between destination marketing and tourist arrivals.
• Correlation between travel preferences and personality traits.
• The influence of travel experiences on personal growth.
• Relationship between travel safety perceptions and tourist behavior.
• Correlation between travel motivations and destination choices.
• The relationship between travel blogging and destination popularity.
• Correlation between travel trends and environmental sustainability.
• The relationship between public transportation accessibility and urban development .

These topics offer students various possibilities for conducting correlational research across various domains, allowing them to explore meaningful relationships between different variables and contribute to existing knowledge.

Tips for Conducting Correlational Research

Conducting correlational research requires careful planning, attention to detail, and adherence to established research methodologies . Here are some tips to help students conduct correlational research effectively:

1. Clearly define variables

Identify the variables you want to study and ensure they are measurable and relevant to your research question.

2. Choose appropriate measures

Select reliable and valid measures for each variable to capture the data accurately.

3. Collect sufficient data

Ensure your sample size is large enough to detect meaningful correlations and consider diverse populations if applicable.

4. Use appropriate statistical analysis

Employ statistical techniques like the Pearson correlation coefficient to analyze the relationship between variables.

5. Consider potential confounding variables

Be aware of other factors that may influence the correlation and control for them if possible.

6. Interpret results cautiously

Remember that correlation does not imply causation; consider alternative explanations for observed relationships.

7. Communicate findings effectively

Present your results clearly and accurately, including any limitations or caveats in your interpretations.

Correlational research topics offer invaluable insights into the intricate relationships between variables across diverse fields. 

Researchers can uncover patterns, make predictions, and deepen our understanding of complex phenomena by exploring correlations. While correlational studies do not establish causation, they provide a foundational framework for further investigation and practical applications.

Through meticulous analysis and interpretation, correlational research contributes to advancements in education, health, social sciences, and beyond. 

As we continue to explore the interconnectedness of variables, correlational research remains a powerful tool for unraveling the mysteries of the world around us and driving progress in various fields.

What is the difference between correlational research and experimental research?

Correlational research examines the relationship between variables without manipulating them, while experimental research involves manipulating variables to determine cause-and-effect relationships. Experimental research allows for stronger causal inferences compared to correlational research.

What are some strengths and weaknesses of correlational research? 

Strengths include being relatively inexpensive and efficient and avoiding manipulation, which might be unethical. Weaknesses include not establishing causality and being susceptible to confounding variables.

Can correlational research establish causation between variables?

No, correlational research cannot establish causation between variables. While it can identify relationships and associations, it does not manipulate variables to determine cause-and-effect, making it unable to establish causal relationships definitively.

What are some common pitfalls to avoid when conducting correlational research?

Common pitfalls in correlational research include mistaking correlation for causation, failing to control for confounding variables, relying on small or biased samples, and neglecting to consider the directionality or third-variable explanations for observed correlations.

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Geospatial and temporal patterns of natural and man-made (technological) disasters (1900–2024): insights from different socio-economic and demographic perspectives.

1. Introduction

Literature review geospatial and temporal patterns of disasters, 2.1. hypothetical framework, 2.2. data collection and preparation, identification and sourcing of key socio-economic indicators, 2.3. analyses.

• Analysis of the geographical distribution of disasters by continents for the period from 1900 to 2024. This included calculating the total number and percentage of different types of disasters on each continent. The geographical distribution was analyzed to identify regions with the highest frequency of disasters and to determine the specific characteristics of disasters in those regions, including their type, frequency, severity, and the resulting impacts on local communities and infrastructure.
• Detailed analysis of the frequency of disasters by countries, including the identification of countries most affected by different types of disasters. The analysis included quantifying the number of events by country and assessing their impact on human and economic resources.
• Analysis of temporal trends in the frequency of natural and technological disasters in 10- and 5-year intervals. This analysis enabled the identification of changes in the frequency and types of disasters over time, as well as the identification of periods with the highest disaster frequency.

3.1. Geographical Distribution of Natural and Man-Made (Technological) Disasters

3.1.1. in-depth analysis of disaster distribution by continent with comprehensive supporting data, 3.1.2. in-depth analysis of disaster distribution by country with comprehensive supporting data, 3.2. temporal distribution of natural and man-made (technological) disasters, 3.2.1. yearly and monthly trends in occurrences of natural and man-made disasters, 3.2.2. yearly and monthly trends in consequences of natural and man-made disasters, 4. the impact of socio-economic indicators on the distribution and consequences of disasters, 5. discussion, 6. recommendations, 7. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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