In a 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
Correlations can be classified as follows:
| Type of Correlation | Definition | Example |
|---|---|---|
| Positive Correlation | Variables change in the same direction. | If fat intake increases, weight also increases. |
| Negative Correlation | Variables change in opposite directions. | The more warm water you drink, the more body fat might decrease. |
| Zero Correlation | No relationship exists between variables. | The amount of water you drink is not related to height increase. |
When to Use Correlational Research
Correlational research is most useful when experimental studies are impractical, unethical, or too costly. It is used to identify the association between two or more variables.
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.
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.
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How to Conduct Correlational Research
To ensure that our results are valid and reliable, we go through several structural steps for conducting correlational research. Unlike experimental research, here you do not need manipulative variables, but you observe them in their natural context to find out their relationship.
Step 1: Define the Research Problem
You can select the issues according to the requirements 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 can you make?
Example:
To find out whether daily screen time for students affects sleep quality.
Step 2: Select the Sample
Choose the participants to represent the targeted population. The sample size should be large enough to give us statistical accuracy, and for this purpose, random sampling is recommended to reduce bias.
- For small pilot studies, around 30 participants would be enough.
- For larger studies, hundreds of participants are required.
Example:
To understand the link between education level and income, you can randomly sample households around your neighbourhood.
Step 3: Choose Data Collection Method
There are various types of data collection methods used in correlational research. The most common methods used for data collection are as follows:
Surveys
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.
- Pros: Cost-effective, fast, standardized.
- Cons: Risk of bias, dishonest responses.
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.
- Pros: Authentic, real-world data.
- Cons: Time-consuming, little control over variables.
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 changes affect, and the 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.
- Pros: Inexpensive, quick, and large datasets available.
- Cons: Data may be incomplete or collected for different purposes.
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 genders.
Step 4: Analyze the Data
After the collection of data following statistical methods are applied:
- Correlation analysis (e.g., Pearson’s r) measures the direction of the relationship and its strength.
- Regression analysis is used to see and predict how one variable affects another and change with each other.
- Scatterplots to visually represent relationships.
Example:
To see if higher study hours are related to better exam performers, you calculate a Pearson correlation.
Step 5: Interpret Results Carefully
It is important to note that correlation does not imply causation. A significant correlation does not prove that one variable causes the other; it only shows that they move together.
Example:
If you find a relation between productivity and consumption of coffee, but the reality is that the strong relation is not just because of coffee consumption, it is actually caused by sleeping habits.
Frequently Asked Questions
Correlational research examines relationships between two or more variables without manipulation.
No, Causation is related to experimental evidence, whereas Correlation shows association.
The example can be studying a relationship between smoking and health outcomes.
