What is Correlational Research: Definition, Types, and Examples

corelational research

Correlational research1 is a type of non-experimental research in which researchers measure two or more variables and assess the relationship or correlation between them without any manipulation. The variables in correlational research could be categorical (qualitative) or quantitative and their behavior is measured in their natural setting.

Correlations can be strong or weak and positive or negative. There could also be instances where there is no correlation between the variables. For example, changes in prices of consumer goods lead to a change in the demand for these products; so, the prices of consumer goods and the demand for them are correlated. Correlational research is useful in understanding economic behaviors and can be used to analyze the relationship between economic indicators (such as GDP and unemployment rates), examine the association between market variables (such as stock prices and interest rates), and study consumer behavior patterns, such as income and expenditure.

This article provides the correlational research definition, with a detailed description of the importance and purposes of correlational research to help you understand how and when the correlational research design can be used, explained with correlational research examples.

Table of Contents

What is Correlational Research?

Correlational research is a type of study design that analyzes the relationship between two or more variables. This type of research helps ascertain whether there is an association between the variables but doesn’t determine whether one causes the other. Correlational research studies can have three possible outcomes or relationships between the variables—positive, negative, or no correlation.2

Positive correlation: An increase (decrease) in one variable leads to an increase (decrease) in the second variable.
Negative correlation: An increase in one variable leads to a decrease in the other variable and vice versa.
No correlation: An increase or decrease in one variable does not change the other.

Researchers present results of correlational research using a numerical value called correlation coefficient, which measures the strength of the correlation. A correlation coefficient close to +1 indicates a very strong positive correlation, a coefficient close to −1 indicates a very strong negative correlation, and a coefficient of zero indicates no correlation.

When to Use Correlational Research?

Correlational research can be used in many fields, such as economics, psychology, and medicine to determine if two or more variables are related.

Researchers can choose to use correlational research in the following situations:[3]

To find only the association between variables irrespective of the causality of the relationship. That is, correlational research doesn’t ascertain whether a change in one variable causes a change in the other variable, but rather only helps understand if they’re related. For example, a company observes a decline in the sales of household appliances. Correlational research can help them identify the variables associated with the decline in sales, such as increasing prices, although it may not be the only variable contributing to the decline.
When researchers want to understand the effects of variables in a natural setting wherein the variables cannot be controlled. For example, visiting a hospital to ascertain the relationship between department or specialty type and wait time for patients.
When researchers think there could be a causal relationship between variables but it would be impossible, impractical, or unethical to manipulate the variables, such as when studying the effects of a traumatic event on individuals.
To generate hypotheses or predictions for further research.

How to Collect Data in Correlational Research?

In correlational research, since none of the variables are manipulated, how or where they are measured is not important. For example, participants could visit the researcher at a laboratory to complete tasks and the relationship between the variables could be assessed later, or the researcher could visit a shopping mall to ask people about their attitudes toward the environment and their shopping habits and then assess the relationship. Both these studies would be correlational because the variables aren’t manipulated.

There are mainly three types of data collection methods in correlational research—naturalistic observation, surveys, and archival research, as shown in the table below.[1], [2]

Parameter	Naturalistic observation	Surveys	Archival research
Definition	Involves observing and recording variables of interest in a natural setting without manipulation	Involves having a random sample of participants complete a survey, questionnaire, or test related to the research variables	Involves analyzing studies conducted long ago by other researchers, and reviewing historical records and case studies
Advantages	Well-suited for studies where researchers want to study the behavior of variables in their natural environment Provides more realistic results	Can collect large amounts of data in a short time Cost efficient and fast	Free to use and cost effective Provides large amount of data collected over a long period and can help study trends and relationships
Disadvantages	Researchers cannot control the study variables or explain the reason for participants’ behaviors Costly and time consuming Risk of researcher and participant bias	Results can be affected by poor survey questions and an unrepresentative sample	Data might be incomplete or unreliable Some topics may not be relevant in the current context No control over data collection methods
Example	Researchers visiting a pharmacy (natural setting) to observe how many people buy cold-related medicines on a winter day	A questionnaire for ascertaining if there is a relationship between education level and individual income	Using databases to study historical unemployment rates and crime statistics in a city over a certain period

How to Analyze Correlational Research?

After data collection, you can analyze the relationship between the variables using either correlation or regression analysis, or both. Scatter plots can be used to visualize the relationship.

Correlation Analysis

Correlation analysis[4] is a method to determine if a relationship exists between variables. This relationship can be depicted through a number called the correlation coefficient. The Pearson correlation method (Pearson’s coefficient = r) is commonly used to identify the number depicting the strength and linear correlation between two variables. This method uses a scatter plot and the direction of the line drawn in the graph depicts the correlation.

Regression Analysis

Regression analysis4 is used to estimate the relationship between a dependent variable and one or more independent variables. This method can be used to predict the amount of change in one variable that will be associated with a change in another variable. Linear regression is the most common type of regression. Regression analysis is helpful in understanding how different variables influence each other and what the outcomes are. When plotting your data on a graph, you get a regression line, which describes the relationship between the independent and dependent variables.

Understanding Correlation and Causation

Although both correlation and causation describe the relationships between variables, both have significant differences.[5] Correlation only identifies or determines that a relationship exists between variables. However, causation indicates that one event causes another. Causation occurs when one variable directly causes a change in another variable. This relationship is more difficult to prove and requires experimentation. Although correlation and causation can occur at the same time, correlation doesn’t imply causation because the relationship between variables could be due to either a third variable or a coincidence.

For example, there could be a correlation between the amount of exercise done by an individual and their reported level of happiness. Although it’s possible that an increase in exercise could cause an increase in the level of happiness, exercise cannot be confirmed as the sole cause because another unknown variable could be significantly influencing the happiness level.

Types of Correlational Research

There are three main types of correlation:6

Positive and negative correlation
Linear and non-linear correlation
Simple, multiple, and partial correlation

Correlation Type		Examples
Positive and negative
Positive	When two variables move in the same direction (when one increases, the other also increases)	Income vs expenditure, time spent on a treadmill vs calories burnt
Negative	When two variables move in opposite directions (when one increases, the other decreases)	Price vs demand, temperature vs sale of woolen garments
Linear and non-linear
Linear	When there is a constant change in one variable due to a change in another variable	Height vs weight, temperature vs sale of ice creams
Non-linear	When there is no constant change in one variable due to a change in another variable	Production of grains may or may not increase with increase in fertilizer use
Simple, multiple, and partial
Simple	Only two variables are assessed	Price vs demand, price vs income
Multiple	Three or more variables are assessed simultaneously	Wheat production vs rainfall and manure quality
Partial	Two variables are examined keeping the other variables constant	Production of wheat depends on various factors (rainfall, manure quality, sunlight, etc.) Studying wheat production vs rainfall, keeping other variables constant is a partial correlation

Characteristics of Correlational Research

Here are some of the key characteristics of correlational research.[4]

Non-experimental: There is no manipulation of variables. A predefined methodology is used to prove a hypothesis. Correlational research is the measurement of the natural relationship between two variables without interference from other variables.
Dynamic: The correlation between variables is not constant and is continually evolving. If two variables have a negative correlation at present, they may develop a positive correlation in the future.
Backward-looking: This type of research can look backwards at historical information to observe long-term trends and patterns. However, it cannot be used to make predictions.

Key Takeaways

Correlational research is a type of non-experimental research in which two or more variables are measured and the relationship between them is ascertained.
Correlational research can determine whether relationships exist between variables but cannot confirm causality, i.e., it doesn’t determine a cause-and effect relationship between variables.
Researchers cannot control or manipulate the variables in correlational research.
Correlational research can have three outputs—positive, negative, and no correlation.
Data can be collected through naturalistic observation, surveys, and archival research.

Frequently Asked Questions

Q1. What is the purpose of correlational research?

A1. There are two main purposes of correlational research.7 The first is to determine the degree to which a relationship exists between two or more variables without manipulating any variables. The second purpose is to develop prediction models to be able to predict the future value of a variable from the current value of one or more other variables.

Q2. What are the advantages and limitations of correlational research?

A2. Here are a few advantages and disadvantages of correlational research.4

Advantages of Correlational Research	Disadvantages of Correlational Research
The relationship between variables is observed in their natural setting and neither variable is manipulated. There is no need to set up a controlled environment.	Correlational research is limited in scope because it provides only the statistical relationship between two variables but not the reason for the relationship.
In marketing, correlational research can help identify a potential target market or advertising strategy.	It doesn’t show the cause and effect so another research method should be used to determine the causal relationship.
Correlational research is more economical because it takes less time and capital to conduct than experimental research.	It cannot be a reliable source for future predictions because correlational research depends on the past to determine relationships.
It can be used to identify the link between two variables when conducting exploratory study is inappropriate or unethical.	Correlational research yields limited amount of data.

Q3. What is the difference between correlational and experimental research?

A3. Experimental research is a scientific research method in which researchers can manipulate one or more independent variables and analyze the effect on the dependent variable. This differs from correlational research in which researchers cannot control the variables. Correlational and experimental research differ in several ways, as shown in the table below.4

Characteristic	Correlational Research	Experimental Research
Methodology	Researchers study the variables to identify a pattern that links them naturally. There is no interaction between the researcher and variables and no catalysts are introduced	Researchers introduce a catalyst to analyze its effect on the variables, thus manipulating the variables
Observation	The researcher passively observes and measures the relationship between variables	The researcher introduces a change in the behavior of the variables and observes the results
Causality	Identifies associations between two variables but doesn’t determine cause and effect	The introduction of a catalyst changes the variables, establishing a cause and effect or causal relationship
Number of variables	Only two	Unlimited

To identify whether a study design is correlational or experimental, the best option would be to look at the methodology and see if there is any manipulation of variables.

Conclusion

To summarize, correlational research should be used by researchers only to determine if a relationship exists between two variables and not to ascertain causation. Several methods of data collection and analysis can be used in correlational research. We hope this article has provided in-depth information about the purpose, uses, and types of correlational research to help you accomplish your research objectives.

References

Correlational research. Research methods in psychology. 2016. University of Minnesota library. Accessed October 14, 2024. https://open.lib.umn.edu/psychologyresearchmethods/chapter/7-2-correlational-research/
Cherry, K. Correlation studies in psychology research. Verywell Mind website. Updated May 4, 2023. Accessed October 15, 2024. https://www.verywellmind.com/correlational-research-2795774
Price PC, Jhangiani RS, Chiang i-CA, et al. Research methods in psychology. 3rd ed. 2017. Accessed October 16, 2024. https://opentext.wsu.edu/carriecuttler/chapter/correlational-research/#:~:text=Another%20reason%20that%20researchers%20would,impossible%2C%20impractical%2C%20or%20unethical.
How to use correlational research to spot patterns and trends. Market Research Solutions. Accessed October 16, 2024. https://www.surveymonkey.com/market-research/resources/correlational-research/
Correlations vs causation: What’s the difference? Coursera. Updated November 29, 2023. Accessed October 17, 2024. https://www.coursera.org/articles/correlation-vs-causation
Correlation: Meaning, significance, types, and degree of correlation. Geeks for geeks website. Updated May 31, 2024. Accessed October 18, 2024. https://www.geeksforgeeks.org/correlation-meaning-significance-types-and-degree-of-correlation/#what-is-correlation
Correlational research designs. Troy University—Montgomery online library. Accessed October 18, 2024. https://spectrum.troy.edu/renckly/week5.htm

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