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Correlational Research – Steps & Examples

Published by 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

Type of correlation Definition Example
Simple correlation A simple correlation aims at studying the relationship between only two variables. Correlation between height and weight.
Partial correlation In partial correlation, you consider multiple variables but focus on the relationship between them and assume other variables as constant. Correlation between investment and profit when the influence of production cost and advertisement cost remains constant.
Multiple correlations Multiple correlations aim at studying the association between three or more variables. Capital, production, Cost, Advertisement cost, and profit.

Based on the direction of change of variables

Type of correlation Definition Example
Positive correlation The two variables change in a similar direction. If fat increases, the weight also increases.
Negative correlation The two variables change in the opposite direction. Drinking warm water decreases body fat.
Zero correlation The two variables are not interrelated. There is no relationship between drinking water and increasing height.


When to Use Correlation Design?

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

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.

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.

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.

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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. 

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.

Pros Cons
  • It’s cost-effective.
  • Easy to conduct.
  • You get quick responses.
  • Responses may not be reliable or dishonest.
  • Some questions may not be easier to analyse

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.

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.

Pros Cons

  • It can be conducted in a natural environment.
  • The observation is natural without any manipulation.
  • It provides better qualitative data.
  • A researcher cannot control the variables.
  • Lack of rigidity and standardisation.

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.

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.

Pros Cons
  • The researcher has control over variables.
  • Easy to establish the relationship between  cause and effect.
  • Inexpensive and convenient.
  • Easy to replicate.
  • The artificial environment may impact the behaviour of the participants.
  • Inaccurate results
  • The short duration of the lab experiment may not be enough to get the desired results.
Pros Cons
  • Cost-effective
  • Suitable for trend analysis and identification.
  • An ample amount of existing data is available.
  • You need to manipulate data to make it relevant.
  • Information may be incomplete or inaccurate.

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.

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

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.

About Carmen Troy

Avatar for Carmen TroyTroy has been the leading content creator for ResearchProspect since 2017. He loves to write about the different types of data collection and data analysis methods used in research.