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.
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.
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.
Positive correlation: A positive correlation between two variables means both variables move in the same direction. An increase in one variable leads to an increase in the other variable and vice versa.
For example, spending more time on a treadmill burns more calories.
Negative correlation: A negative correlation between two variables means that the variables move in opposite directions. An increase in one variable leads to a decrease in the other variable and vice versa.
For example, increasing the speed of a vehicle decreases the time you take to reach your destination.
Weak/Zero correlation: No correlation exists when one variable does not affect the other.
For example, there is no correlation between the number of years of school a person has attended and the letters in his/her name.
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.
r = 1 means perfect positive correlation, where as one variable increases, the other also increases proportionally.
r = -1 means perfect negative correlation, where one variable increases as the other decreases in a completely inverse relationship.
r = 0 means no linear correlation, where there’s no predictable relationship between the changes of the two variables.
The value of the correlation coefficient indicates the strength of the relationship:
Closer to 1 or -1: Stronger relationship.
Closer to 0: Weaker relationship, near zero means no linear dependence.
Types of Correlation Coefficients
Pearson Correlation Coefficient: This is the most common method to measure linear correlation between two continuous variables, assuming they are normally distributed. It’s best for parametric data.
Spearman’s Rank Correlation Coefficient: Used when data is ordinal or when the relationship is not linear but monotonic, it’s a non-parametric alternative to Pearson’s correlation coefficient.
Kendall’s Tau: Another non-parametric measure for smaller sample sizes and ordinal data, similar to Spearman’s.
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.
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. |
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:
Agreement scales
Importance scales
Satisfaction scales
Numerical data like money, temperature, or age
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:
Pearson’s r correlation: For linear, quantitative relationships with no outliers.
Spearman’s rank correlation: For ranked (ordinal) variables.
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.
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:
Patient Outcomes and Treatment: Correlation analysis is used to study relationships between treatments and patient outcomes. For instance, a researcher may study the correlation between a patient’s blood pressure and a specific medication. This can help identify trends that may indicate whether a treatment works or if other factors need to be considered.
Customer Behavior and Sales Trends: Correlation can be used to understand customer behavior by linking factors like customer satisfaction and purchase frequency. Businesses may study the relationship between social media engagement and brand loyalty to predict future sales trends or design more targeted campaigns.
Stock Market Analysis: In finance, correlation is used to study the relationship between different financial assets. For example, investors may study the correlation between different stocks or between a stock and an economic indicator (e.g., interest rates). This helps in portfolio diversification by selecting negatively or weakly correlated assets, which can reduce overall risk.
User Engagement and Product Features: Tech companies can use correlation analysis to study the relationship between product features, such as user interface or software speed, and user engagement or satisfaction. This can then inform product development.
Employee Productivity and Work Environment: HR can use correlation to identify factors that impact employee performance. For example, they may study the relationship between flexible working hours and job satisfaction so the organization can design better working policies.
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:
Awareness of the behavior between two variables: A correlation helps to identify the absence or presence of a relationship between two variables. It tends to be more relevant to everyday life.
A good starting point for research: It proves to be a good starting point when a researcher starts investigating relationships for the first time.
Uses for further studies: Researchers can identify the direction and strength of the relationship between two variables and later narrow the findings down in later studies.
Simple metrics: Research findings are simple to classify. The findings can range from -1.00 to 1.00. There can be only three potential broad outcomes of the 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:
Correlation vs. Causation: Just because two variables are strongly correlated doesn’t mean one causes the other. There may be other factors affecting both.
Linear Relationships Only: Correlation only measures straight-line (linear) relationships, so it might miss more complex patterns between variables.
Sensitivity to Outliers: Outliers, or extreme data points, can distort the correlation, making the relationship look stronger or weaker than it really is.
Range Limitation: When data only covers a narrow range, the correlation might be misleading, either underestimating or exaggerating the true relationship.
Sample Size Impact: Small sample sizes can give unreliable results, while larger samples provide more stable and accurate correlations.
Potential for Misinterpretation: Correlation coefficients can be misunderstood without context, so it’s important to interpret them within the study's overall framework.
Learn how to set up and use this feature with our help file on correlation analysis.