Correlation analysis in market research is a statistical method that identifies the strength of a relationship between two or more variables. In a nutshell, the process reveals patterns within a dataset’s many variables.
Using one of the several formulas, the end result will be a numerical output between -1 and +1.
Let’s say you are interested in the relationship between two variables, Variable A and Variable B.
Results close to +1 indicate a positive correlation, meaning as Variable A increases, Variable B also increases.
In contrast, outputs closer to -1 are a sign of a negative correlation. These results mean that as Variable A increases, Variable B decreases.
A value near 0 in a correlation analysis indicates a less meaningful relationship between Variable A and Variable B.
While you are technically testing two variables at a time, you can look at as many variables as you would like in a grid output with the same variables listed as both the columns and rows.
In the example output below, you can look at the corresponding box to see the relationship between two variables.
Notice how the values for correlation analysis of the same variable are equal to 1. This makes sense, as the values of the same variable would increase and decrease completely in sync.
How to Measure Correlation in Market Research
The first step in running a correlation analysis in market research is designing the survey. You will need to plan ahead with questions in mind for the analysis.
This includes anything that yields data which is both numerical and ordinal. Think metrics such as:
- Agreement scales
- Importance scales
- Satisfaction scales
Once the survey is finalized, you will need to program and test it to ensure the questions are functioning correctly. Mislabeled scales or improper data validation in the programming will taint the data used for correlation analysis.
The next step will be to administer the fieldwork of the survey.
Clean data for the analysis after the target number of responses is reached. This protects the integrity of the data for the analysis, as well.
You can then run a correlation analysis using one of several methods.
The two most common ways to run correlation analysis are the Pearson r correlation and the Spearman rank correlation.
Most data analysis software features a tool to run a correlation analysis after you enter the inputs automatically. The analysis can also be calculated by hand for variables, but you will save time and avoid human error by using a program.
Lastly, review the results to see how different variables are connected.
Benefits of Finding Correlation Analysis in Market Research
There are several reasons to consider running a correlation analysis in your next market research study.
For one, planning a correlation analysis motivates market researchers to ask better questions in the survey.
Knowing many variables will be examined during the analysis, researchers will spend more time thinking through all the most important and relevant data that should be collected.
Once you have the data, the correlation analysis helps you identify which variables have the strongest relationships. Unforeseen negative or positive correlations may help businesses make better-informed decisions.
Even though correlation analysis results are not a great predictor themselves, they can still inform future qualitative or quantitative research.
For instance, you may discover a significant pattern between variables that inspires additional research.
Correlation analysis also nicely leads to regression analysis. By comparison, regression analysis tells you what Variable A might look like based on a particular value of Variable B.
In other words, correlation tells you there is a relationship, but regression shows you what that relationship looks like.
When to Use Correlation Analysis in Market Research
Correlation analysis is useful for all kinds of data sets, but there are common uses within market research.
These surveys typically include many questions that make ideal variables in a correlation analysis.
As previously mentioned, there are a few different approaches to correlation analysis, including the Pearson and Spearman formulas.
- For the Pearson correlation, variables should each have a normal distribution and a presumed linear relationship.
- When using the Spearman correlation, the variables must contain ordinal data in which their position is important.
Only use correlation analysis if you understand and can explain to a client that correlation is not causation.
It is tempting to jump to the conclusion that two variables have a direct result on each other, but this analysis is meant for identifying connections, not predicting them.
That said, when there is an interest in discovering relationships between two or more variables, correlation analysis is an excellent fit in a market research project.
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As a Research Analyst, Tim is involved in every stage of a market research project for our clients. He first developed an interest in market research while studying at Binghamton University based on its marriage of business, statistics, and psychology.
Learn more about Tim, here.