Correlational Research | Guide, Design & Examples
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.
|Both variables change in the same direction
|As height increases, weight also increases
|The variables change in opposite directions
|As coffee consumption increases, tiredness decreases
|There is no relationship between the variables
|Coffee consumption is not correlated with height
Correlational vs experimental 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.
|Used to test strength of association between variables
|Used to test cause-and-effect relationships between variables
|Variables are only observed with no manipulation or intervention by researchers
|An independent variable is manipulated and a dependent variable is observed
|Limited control is used, so other variables may play a role in the relationship
|Extraneous variables are controlled so that they can’t impact your variables of interest
|High external validity: you can confidently generalise your conclusions to other populations or settings
|High internal validity: you can confidently draw conclusions about causation
When to use correlational research
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.
How to collect correlational data
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.
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 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.
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.
How to analyse correlational data
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.
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.
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.
Correlation and causation
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.
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.
Frequently asked questions about correlational research
- What is the definition of a correlation?
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.
- What is the definition of correlational research?
- What’s the difference between correlational and experimental research?
- 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.
- How many variables are in a correlation?
A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.
- What is the definition of a correlation coefficient?
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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