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Correlation analysis

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What is correlation analysis?

Correlation analysis is a statistical method used in research to measure the strength of the linear relationship between two variables and compute their association. Simply put, correlation analysis calculates the change in one variable due to the change in the other.

When it comes to market research, researchers use correlation analysis to analyze quantitative data collected through research methods like surveys and live polls. They try to identify the relationship, patterns, significant connections, and trends between two variables or datasets.

What is Correlation Analysis?

Correlation analysis is a tool researchers use to identify how two things might be connected and how strong that connection is. It helps them determine whether and how much one thing changes with the other.

A high correlation points to a strong relationship between the two variables, while a low correlation means that the variables are weakly related.

There is a positive correlation between two variables when an increase in one leads to an increase in the other. On the other hand, a negative correlation means that when one variable increases, the other decreases, and vice versa.



Example of correlation analysis

Correlation between two variables can be either a positive correlation, a negative correlation, or no correlation. Let's look at examples of each of these three types.

For example, spending more time on a treadmill burns more calories.

For example, increasing the speed of a vehicle decreases the time you take to reach your destination.

For example, there is no correlation between the number of years of school a person has attended and the letters in his/her name.

Correlation analysis

The Correlation Coefficient

One of the statistical analysis concepts most closely related to this is the correlation coefficient.

The correlation coefficient is the unit of measurement used to calculate the strength of the linear relationship between the variables in a correlation analysis. It’s easy to identify since it’s represented by the letter r. It is usually a value without units and between 1 and -1.

The value of the correlation coefficient indicates the strength of the relationship:

Types of Correlation Coefficients

The correlation coefficient summarizes the relationship, but remember that correlation does not imply causation—meaning that even if two variables are highly correlated, it doesn’t mean one causes the other to change.

If you want a deeper understanding of how to calculate and interpret the Pearson correlation coefficient, we recommend consulting our detailed guide: Pearson Correlation Coefficient.

The Difference Between Correlation and Regression Analysis

When analyzing data, it’s important to understand how variables connect to each other. Correlation and regression are two main methods for exploring these connections. Both can help you study relationships, but they serve different goals.

Correlation shows if two variables are connected, while regression takes it further by using one variable to predict another. Comparing these methods helps us decide when to use each one and how they add value to data analysis.

Comparison Correlation Analysis Regression Analysis
Definition Determines co-relationship or association or absence of a relationship of two or more variables Predicts the value of the dependent variable based on the known value of the independent variable, assuming an average mathematical relationship between two or more variables
Use Case To represent a linear relationship between two variables To fit a best line and estimate one variable on the basis of another variable
Indicates The extent to which two variables move together strength-wise Regression indicates the impact of a unit change in the known variable (x) on the estimated variable (y)
Objective To find a numerical value expressing the relationship between variables -1, 0, +1 To estimate the values of a random variable on the basis of the values of a fixed variable.


How to Measure Correlation

To correlate, start by collecting data through an online survey. This means creating, coding, and deploying the survey. Then, the responses will be analyzed to determine the strength and type of relationships between the variables.

Correlation is super useful in all types of surveys, customer satisfaction, employee feedback, customer experience (CX) programs, and market research. These surveys have multiple questions that are perfect for correlation analysis and can reveal some great insights.

Here are the steps to follow when correlating using an online survey:

Step 1: Design the Survey

First, design the survey carefully and make sure it has questions that will generate data that can be correlated. Plan by choosing metrics that are either numerical or ordinal, such as:

Once the survey is designed, it needs to be coded and tested thoroughly to make sure it’s working properly. This step is critical because errors like mislabeled scales or incorrect data validation can ruin the correlation analysis.

Once the survey is fully tested and validated, it can be deployed to the target audience for data collection.

Step 2: Analyze the Correlation Between Two Variables

Once you have the responses from the survey, it’s time to run the correlation. This means looking at the relationship between two variables to see if there are patterns or connections. There are two main methods:

Both methods will give you insights into how the variables are connected and guide data-driven decisions in marketing, product development, and customer experience.

QuestionPro makes the entire process of correlation analysis easier by providing advanced survey tools and analytics features. With its user-friendly interface, you can design surveys with the right scales and numerical inputs and get clean and structured data for analysis.

Also, QuestionPro’s built-in analytics will automatically calculate the correlation coefficients and give you real-time insights into the relationships between variables.

Uses of correlation analysis

Correlation analysis is used for practical cases. Here, the researcher can’t manipulate individual variables. It is useful when experimentation is impractical, unethical, or impossible. Here are some examples where correlation analysis is applied:

Advantages of correlation analysis

In statistics, correlation refers to the fact that there is a link between various events. One of the tools to infer whether such a link exists is correlation analysis. Practical simplicity is undoubtedly one of its main advantages.

To perform reliable correlation analysis, it is essential to make in-depth observations of two variables, which gives us an advantage in obtaining results. Some of the most notorious benefits of correlation analysis are:

Considerations and Limitations of Correlation Analysis

Correlation analysis helps us understand how variables relate, but it has some important limitations to keep in mind for accurate interpretation. Here are the key points to consider:

How to use correlation analysis in your surveys?

Learn how to set up and use this feature with our help file on correlation analysis.

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