Correlation Is Significant but Regression Is Not: Why Does This Happen and How Should You Interpret It?

When analysing research data, you may encounter a situation where two variables show a statistically significant relationship in Pearson Correlation, but the same variable becomes non-significant when included in a Multiple Regression model. This often raises an important question: are the two results contradictory, and which result should you rely on?

The answer is that this situation can occur normally and does not necessarily indicate an error in the analysis. Pearson Correlation and Multiple Regression answer different questions.

Correlation examines the direct relationship between two variables. Multiple Regression, on the other hand, examines the relationship between each Independent Variable and the Dependent Variable while considering the other Independent Variables in the model at the same time. As a result, a variable that is significantly related to Y when considered on its own may no longer show a significant relationship when other factors are taken into account.

What Does It Mean When Correlation Is Significant but Regression Is Not?

Suppose a study examines factors associated with Purchase Intention using Customer Experience, Service Quality and Brand Trust as Independent Variables.

Pearson Correlation might produce the following results:

Relationship	Pearson Correlation
Customer Experience and Purchase Intention	Significant
Service Quality and Purchase Intention	Significant
Brand Trust and Purchase Intention	Significant

Looking only at the Correlation results, all three Independent Variables appear to be significantly related to Purchase Intention. However, when all three variables are entered into a Multiple Regression model together, the results might look like this:

Independent Variable	Multiple Regression
Customer Experience	Not Significant
Service Quality	Significant
Brand Trust	Significant

This does not mean that Customer Experience has no relationship with Purchase Intention. Instead, it means that Customer Experience is related to Purchase Intention when the two variables are considered directly, but once Service Quality and Brand Trust are considered at the same time, Customer Experience may not provide enough additional information to show a statistically significant association within that Regression model.

This distinction is key to understanding why Correlation and Regression results can differ.

Why Can Correlation and Regression Produce Different Results?

The main reason is that Correlation looks at two variables at a time, while Multiple Regression can consider several variables simultaneously.

Suppose Customer Experience is related to Purchase Intention. At the same time, Customer Experience may also be related to Service Quality and Brand Trust. This means that part of the relationship between Customer Experience and Purchase Intention may overlap with what Service Quality or Brand Trust can also explain.

When Pearson Correlation examines only Customer Experience and Purchase Intention, this overlapping information remains part of the observed relationship. When all variables are entered into a Multiple Regression model, however, the model evaluates the relationship between Customer Experience and Purchase Intention while also taking the other Predictors into account.

The difference can be summarised simply:

Pearson Correlation	Multiple Regression
Examines two variables	Can examine several variables together
Looks at the direct relationship between X and Y	Examines X and Y while accounting for other variables in the model
Does not separate relationships shared with other Predictors	Helps distinguish the contribution of individual Predictors
A significant result does not guarantee significance in Regression	Results may change when other Predictors are included

Therefore, a change from Significant to Not Significant does not necessarily mean that the two analyses contradict each other. They are simply examining the relationship from different perspectives.

What Is Shared Variance?

Shared Variance may sound technical, but the basic idea is relatively straightforward.

Consider Service Quality and Customer Satisfaction. They represent different concepts, but they are often related in real situations because customers who experience better service may also report higher satisfaction.

If both Service Quality and Customer Satisfaction are related to Customer Loyalty, some of the information they provide about Customer Loyalty may overlap.

A Pearson Correlation between Service Quality and Customer Loyalty does not separate the part of that relationship that may also be shared with Customer Satisfaction. Multiple Regression considers the variables together and can therefore help show whether Service Quality is still associated with Customer Loyalty after Customer Satisfaction and other variables in the model have been taken into account.

This overlapping information is one reason why a variable can be Significant in Correlation but Not Significant in Regression.

Is Multicollinearity Involved?

It can be, but a non-significant Regression result should not automatically be attributed to Multicollinearity.

Multicollinearity occurs when Independent Variables in a Regression model are strongly related to one another, making it more difficult for the model to distinguish the contribution of each Predictor.

For example, suppose a study includes Service Quality, Service Experience and Service Satisfaction. If the questionnaire measures these concepts in very similar ways, the resulting variables may be strongly related.

When each variable is correlated separately with Customer Loyalty, all three might be Significant. However, when all three are entered into Multiple Regression simultaneously, one or more may become Not Significant because the Predictors contain a large amount of overlapping information.

However, Significant Correlation combined with Non-Significant Regression does not always indicate Multicollinearity. The data and Regression model should be examined before reaching that conclusion.

What Else Can Cause the Results to Differ?

In addition to Shared Variance and relationships among Independent Variables, several other factors may contribute to differences between Correlation and Regression results.

Factor	Possible Effect
Relationships among Independent Variables	The information provided by individual Predictors may overlap
Multicollinearity	Makes it more difficult to distinguish the contribution of individual Predictors
Sample Size	An inadequate sample may make individual effects more difficult to detect
Outliers	Extreme observations can influence both Correlation and Regression results
Variables included in the model	The result for one Predictor can change when other variables are added or removed
Research Model	The way variables and relationships are defined affects how the results should be interpreted

For this reason, when Correlation is Significant but Regression is Not Significant, researchers should not look only at the P-Value. The overall model, relationships among variables and characteristics of the data should also be considered.

Should You Remove a Variable If It Is Not Significant in Regression?

Not necessarily. An Independent Variable should not be removed from a Regression model simply because its P-Value is greater than 0.05.

If the variable was included based on the Research Framework, Theory, Previous Research or a predefined Hypothesis, removing it only to produce a model containing Significant Predictors may make the analysis inconsistent with the original research design.

On the other hand, if several variables appear to measure very similar concepts or there is evidence of Multicollinearity, the structure of the variables and model may need further examination.

The decision to retain or remove a variable should therefore be supported by both the Research Design and the Statistical Analysis rather than by P-Value alone.

If Regression Is Not Significant, Does That Mean the Hypothesis Is Not Supported?

It depends on how the Hypothesis was formulated and which Statistical Test was intended to test it.

For example, suppose the Hypothesis states that Customer Experience is related to Purchase Intention and Pearson Correlation was selected in advance as the appropriate test. In that case, the Correlation result is the result relevant to that Hypothesis.

However, if the Hypothesis asks whether Customer Experience is associated with Purchase Intention after considering other factors simultaneously, and Multiple Regression was selected to test that relationship, the Regression result is more directly aligned with the Hypothesis.

Researchers should therefore avoid choosing between Correlation and Regression after seeing the results simply because one is Significant and the other is Not Significant. The Statistical Analysis should be aligned with the Research Objective and Hypothesis from the beginning.

How Should You Explain the Results in a Research Paper?

When Correlation is Significant but Regression is Not Significant, the results of each analysis should be reported according to what that method actually tests rather than presented as contradictory findings.

For example, if Customer Experience is significantly correlated with Purchase Intention but becomes non-significant in Multiple Regression, the interpretation might explain that Customer Experience showed a statistically significant relationship with Purchase Intention when the two variables were considered directly. However, when Customer Experience was analysed together with other Independent Variables in the Multiple Regression model, its association with Purchase Intention was no longer statistically significant.

The discussion can then consider whether relationships among the Independent Variables or overlapping information among the Predictors may help explain the difference.

What should be avoided is a statement such as Correlation shows a relationship but Regression shows no relationship.

Regression does not necessarily show that the two variables have no relationship at all. Instead, it evaluates the Predictor within a model that also contains other variables.

Which Should You Trust: Correlation or Regression?

Correlation and Regression should not be treated as competing analyses where one result is more trustworthy than the other. They answer different Research Questions.

If the Research Question asks...	More Relevant Method
Are X and Y related?	Pearson Correlation
Is the relationship between X and Y positive or negative?	Pearson Correlation
How is X associated with Y when other Predictors are considered?	Multiple Regression
Which Predictors remain associated with Y when considered together?	Multiple Regression
Can a model be used to explain or predict Y?	Regression

If you are still unsure about the fundamental difference between the two methods, see Pearson Correlation vs Linear Regression: What Is the Difference and Which Should You Use?

For a more detailed explanation of each method, see What Is Pearson Correlation? and What Is Regression Analysis?

How Can Planning the Analysis in Advance Help?

It is not always possible—or desirable—to prevent Correlation and Regression from producing different results, because those differences may reflect genuine patterns in the data. What researchers can do is make the Research Design and Analysis Plan clear from the beginning.

The study should define what each Research Objective is intended to examine, which variables are Independent and Dependent Variables, which Statistical Test will be used for each Hypothesis, and whether the Constructs in the Research Framework are sufficiently distinct from one another.

For researchers who are still planning their study, our questionnaire design and review service can help align the research objectives, variables, hypotheses and analysis plan before data collection begins.

For those who have already analysed their data and found that Correlation is Significant while Regression is Not Significant, Data Investigator's SPSS statistical data analysis service includes data checking, Correlation and Regression analysis, model assessment and interpretation in accordance with the Research Objectives and Hypotheses.

Conclusion: Why Can Correlation Be Significant While Regression Is Not?

A Significant Correlation combined with a Non-Significant Regression result can occur normally and does not necessarily mean that the analysis is incorrect or that the results contradict each other.

Pearson Correlation examines the direct relationship between two variables, whereas Multiple Regression evaluates the relationship between each Predictor and the Dependent Variable while accounting for other Predictors included in the model.

As a result, a variable that is significantly related to Y when considered on its own may not show a sufficiently distinct relationship when other variables are considered simultaneously. Shared Variance, Multicollinearity, Sample Size, Outliers and the structure of the Regression model can all contribute to this outcome.

The key is not to decide whether Correlation or Regression should be believed. Instead, researchers should understand what question each analysis is answering and interpret the findings according to the Research Objective, Hypothesis and model being tested.

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