Correlation analysis is a tremendous tool to use in understanding how one variable affects another. It can also be used to find out how much they affect each other.
By providing a distinct perspective on which factors impact your business the most, you can feel more confident in the actions you take after the report.
Correlations can be misunderstood or misused, which is why it’s important to have experience or be using expert insights in order to get things correct.
In this blog post, our online survey agency provides more insight into correlation analysis including its definition, how to measure and interpret correlation, examples, and more.
What is Correlation Analysis? Our Definition
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.
It's all about identifying relationships between variables–specifically in research.
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.
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 columns and rows.
The Drive Research team explores more into the definition of correlation analysis in the video below.
How to Measure Correlation
You must first conduct an online survey to analyze the correlation between two variables. The process includes writing, programming, and fielding a survey. The results are later used to determine strength scores.
You are likely to find a useful application for them in customer satisfaction surveys , employee surveys , customer experience (CX) programs, or market surveys .
These surveys typically include many questions that make ideal variables in a correlation analysis.
Below is the process our online survey agency follows to measure correlation.
Step 1. Write the survey
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 that is both numerical and ordinal.
Think of metrics such as:
Step 2. Program + field the survey
Once the survey is finalized, you will need to program and test it to ensure the questions are functioning correctly.
This is important because mislabeled scales or improper data validation in the programming will taint the data used for correlation analysis.
Use our online survey testing checklist for what to look for because launching the questionnaire into fieldwork.
Once everything checks out, it's time to administer the fieldwork of the survey.
Step 3. Analyze the correlation between 2 variables
Next, clean the survey data after the target number of responses is reached. This protects the integrity of the data for analysis.
The two most common ways to run a correlation include:
Though, most data analysis software features a tool to run a correlation analysis after you enter the inputs automatically.
For instance, you can run the analysis through some sort of spreadsheet software, like Microsoft Excel.
Here is a great video that walks through the process of using Excel to calculate a correlation coefficient.
If you're not comfortable conducting a survey and using Excel, contact our market research company. Our team can commission a full correlation analysis study on behalf of your organization.
The Coefficients To Use
Just like we showed in the measuring section during the analyzing phase, there are two main coefficients to use: the Pearson Coefficient and the Spearman’s Coefficient.
The Pearson Correlation Coefficient (r) is used to measure the strength and direction of the linear relationship between two continuous variables.
It assumes that both variables are normally distributed, have a linear relationship, and are measured on an interval or ratio scale.
The coefficient ranges from -1 to +1, where +1 indicates a perfect positive linear relationship, -1 indicates a perfect negative linear relationship, and 0 indicates no linear relationship.
This coefficient is useful for understanding how changes in one variable are associated with changes in another.
Spearman's Rank Correlation Coefficient (ρ or rs) measures the strength and direction of the monotonic relationship between two ranked variables.
It assumes that the data can be ranked and that the relationship between the variables is monotonic, meaning it consistently increases or decreases but is not necessarily linear.
The coefficient ranges from -1 to +1, with +1 indicating a perfect positive monotonic relationship, -1 indicating a perfect negative monotonic relationship, and 0 indicating no monotonic relationship.
This method is useful when the data do not meet the assumptions of Pearson correlation, particularly for ordinal data or non-linear relationships.
How to Interpret Correlation Analysis
Correlation coefficients range from 0 to 1, where the higher the coefficient means the stronger correlation.
When the value is greater than 0.7, there is considered to be a strong correlation between the two variables.
All correlation strength scores and classifications are outlined below.
C orrelation Analysis Example
Employee surveys are a great example of how correlation analysis is used.
For instance, most full-service employee survey companies utilize correlation analysis to determine which independent variables (such as salary or benefits) impact a dependent variable (such as employee satisfaction).
Let's look at an example.
A common employee net promoter score (eNPS) question to measure correlation is "How likely an employee is to recommend working at a company to a friend or family member on a 1 to 10 scale where 1 reflects “not at all likely” and 10 reflects “very likely."
The final eNPS serves as the dependent variable.
Then, a follow-up question should be included that asks employees to rate how satisfied they are with different organizational factors.
For example, asking employees to rate their level of satisfaction on a 1 to 5 scale where 1 is “not at all satisfied” and 5 is “very satisfied” with factors such as salary, benefits, training, and diversity and inclusion.
The final scores serve as independent variables.
Correlation analysis will tell you which independent variables most positively correlate with eNPS.
For instance, if salary and benefits had a correlation coefficient of 0.6, it would have a “strong” correlation with eNPS.
Benefits of Finding Correlation Analysis in Market Research
There are several reasons to consider running a correlation analysis in your next market research study.
Get more from your data
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.
Make more informed decisions
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.
A great counterpart to regression analysis
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.
Drawbacks to Measuring Correlation
Correlation analysis is useful to understand how variables interact with one another.
That said, pitfalls exist and have to be looked out for if you choose to run the survey in-house. Drawbacks to measuring correlation include:
Coincidences within the results
One of those biggest pitfalls is you may get a result that shows a strong correlation, either negative or positive.
Take customer satisfaction, for example. Through the analysis, you may find customer experience is highly graded and correlates strongly with overall satisfaction.
To say that one is directly causing the other could be faulty and should be carefully considered.
There are many factors at play that could just be a coincidence when reviewing correlation analysis statistics , and it's essential to make conclusions within the scope of reason.
Correlation is not causation
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.
Correlation analysis in research allows for a deeper look into variables within a business or industry.
To assure 100% confidence, we recommend partnering with a market research company like Drive Research.
Our team of experts has years of experience using correlation analysis to analyze feedback from our client's employees, customers, and other audiences.
Want to include correlation analysis in your next research study? Contact Drive Research for a quote.
As a Research Manager, 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.
