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Correlational research is a type of research design commonly used in the social and behavioral sciences. It measures the relationship between two or more variables.
Researchers using correlational research design typically look at associations or correlations in data without establishing that one event causes another. To statistically analyze correlational data, researchers must control variables that may affect the relationships found in the data.
Let’s take a closer look at the correlational method.
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What Is Correlational Research?
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How Is Correlational Research Conducted?
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Examples Of Correlational Research
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What To Watch Out For In Correlational Research Design
What Is Correlational Research?
Different research techniques have different uses. Here are some features of correlational study:
- Correlational research is often used in observational studies. This means researchers gather information about an event and attempt to correlate it with other variables (also called independent or dependent variables) that they cannot control.
- Researchers use the correlational method because, unlike experimental design, correlational research doesn’t control for individual differences and other factors.
- This also means the results of the correlational method may not be as reliable as those of experimental studies. Analyzing the data can be challenging. Researchers must use statistical tests to determine whether observed relationships are statistically significant.
- Correlational research doesn’t always provide evidence that one factor causes another. They’re correlational, not causal. It can, however, provide information about relationships between variables.
There are specific situations where a correlational study can be a useful tool. Now that we know what is correlational research, let’s look at how it’s done.
How Is Correlational Research Conducted?
In correlational research, the most important part of the design process is to identify the variables. Here’s how:
- Researchers get ready for data collection. They might create a nomogram where they can plot all the variables. A nomogram is a grid with rows and columns. The rows represent variables, while the columns represent observations.
- Researchers collect their data. Once collected, they use a second nomogram to help them place their observations. This is called plotting in the correlational method.
- The data is sorted and researchers look for patterns. Then they enter the data into the nomogram according to those patterns.
- Researchers choose additional variables that will help them identify the relationship between the dependent and independent variables.
- Researchers can collect data from different sources to compare their findings.
Such considerations must be incorporated in all types of correlational research design.
Examples Of Correlational Research
Scientists might want to see if people working in the public sector are less likely to take their car for repairs than those who work in the private sector. If they identify this variable, they’ll need to use an appropriate nomogram to determine which variables represent it. They‘d first classify variables into two categories: public employees and private employees. Next, they’d plot data on an appropriate nomogram that shows how many observations each category represents. That’s one of the examples of correlational research.
What To Watch Out For In Correlational Research Design
Here are some further considerations for effective correlational research design:
- Proper sampling is essential to the validity of any study. It’s important that each observation represent the entire sample. Researchers can use random sampling or stratified sampling in the correlational method.
- Random sampling involves choosing subjects at random. If there are eight cases in an experiment and 12 people to choose from, for example, a coin flip can decide which two people each observation will be based on. This helps ensure that each observation is represented equally in a correlational research.
- Stratified sampling allows researchers to choose subjects based on specific characteristics, such as gender or race in correlational research. This helps ensure that each case represents the population.
- It’s also important that the sample represents the population. Race, gender, age, social class and other factors all affect results. Researchers can correct for these biases by using appropriate sampling techniques.
- In all types of correlational research design, the sample should be large enough that there are no extreme outliers or isolated points in the data.
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