How to work with a moderating variable in the regression test with SPSS?

By Riya Jain & Priya Chetty on March 6, 2020

In studies that aim to determine the relationship between two variables, the regression equation is typically applied. However, sometimes the strength or the direction of this relationship could be controlled by other variables. These influencing or control variables are said to be moderating variables and the effect of these interactions is represented as an interaction effect.


In a causal relationship between salary and health-related expenses, age has an interaction effect. Young people spend comparatively less on their health as compared to older people. Age is thus the moderating variable in the determination of the amount of money spent on health from their salary.

Presence of a moderating variable in SPSS
Presence of a moderating variable in SPSS

Process of examining the moderating effect presence in regression

As the moderating variable is considered an independent variable, thus multiple regression analysis is performed for studying the impact of moderating variable and its interaction effect. The following steps are followed to assess the presence of moderating variables in a linkage between variables.

Step 1: Import the data into SPSS

Step 2: Compute the standardized value of independent variables by clicking on Analyze > Descriptive Statistics > Descriptive as shown below.

Computing the standardised value of independent variables in SPSS
Figure 2: Computing the standardised value of independent variables in SPSS

Select all the independent variables including the moderation variable for computation of standardized values. Click on ‘Save standardized values as variables’ and then select ‘ok’.

Computation of standardized values  in SPSS
Figure 3: Computation of standardized values in SPSS

Computed standardized values will appear in the datasheet of SPSS as shown below.

Standardized values
Figure 4: Standardized values

Step 3: Determine the interaction effect by clicking on Transform > Compute variables

Transformation of data
Figure 5: Transformation of data

The new variable needs to be created to store the value of the interaction effect. Name the variable by entering its name in ‘Target Variable’.

Naming the variable
Figure 6: Naming the variable

Multiply the standardized value of the moderator with the standardized value of the independent variable individually and select click on ‘OK’.

Interaction effect computation
Figure 7: Interaction effect computation

A new variable will be created in the SPSS Datasheet consisting of the interaction effect as shown below.

Result of interaction effect computation
Figure 8: Result of interaction effect computation

Repeat step 3 until the interaction effect for each independent variable is computed i.e. in case of three independent variables i.e. A, B, and C and one moderator i.e. M. Step 3 will be repeated thrice i.e. for computing ZA*ZM, ZB*ZM, and ZC*ZM.

Use 634CAE327AA5A to save 2000 on standard order of 3001 - 5000 words and get testable hypothesis recommendations for your methodology.
Order now

Step 4: Regress the dependent variable on the independent variable, moderator, and interaction effect in two blocks wherein 1 block consist of the only independent variable and the 2nd block includes the interaction effect along with the independent variable and moderator.

Block 1 specification
Figure 9: Block 1 specification
Block 2 specification
Figure 10: Block 2 specification

Does age work as a moderating variable factor between salary and health expenses?

In order to validate whether age act as an interacting variable in linking the salary and health expenses, a regression analysis is necessary. Below stated hypothesis is tested to verify this linkage:

H0: There is no moderating effect of age on the linkage between salary and health expenses.

HA: There is a moderating effect of age on the linkage between salary and health expenses.

Results are shown in the below table.

Health ExpenseCoefficientT-statisticp-valueR2Adjusted R2F Ratio
Model without moderation effect
Constant .422 1.314 .195 .731 .719 63.756
Salary .361 2.190 .034      
Age .540 2.965 .005      
Moderation Effect
Constant .558 1.143 .259 .731 .714 41.772
Salary .370 2.201 .033      
Age .502 2.401 .020      
Interaction -.033 -.374 .710      

Table 1: Regression Results

The above table shows that with the inclusion of the interaction effect of age in the relationship between salary and health expenses, the value of R2 is the same i.e. 0.731 but the Adjusted R2 has decreased i.e. from 0.719 to 0.714. The adjusted R2 value shows that about 71.4% of the variation in health expenses is represented by salary wherein age work as a moderator. Furthermore, though still more accurate prediction about health expenses could be done (F-ratio > 1) with the inclusion of the interaction effect, this accuracy has decreased i.e. F-ratio decreased from 63.756 to 41.772. Finally, Model 2 has the p-value of 0.710 for the interaction effect which shows that the null hypothesis of having no moderating effect of age on the linkage between salary and health expenses is not rejected because the value is greater than the significance level of the study i.e. 0.05. However, the p-value of age shown in model 1 is less than the significance level of the study i.e. 0.005 < 0.05. Thus, age works as an independent variable in influencing the health expenses of a person instead of a moderator.


I am a management graduate with specialisation in Marketing and Finance. I have over 12 years' experience in research and analysis. This includes fundamental and applied research in the domains of management and social sciences. I am well versed with academic research principles. Over the years i have developed a mastery in different types of data analysis on different applications like SPSS, Amos, and NVIVO. My expertise lies in inferring the findings and creating actionable strategies based on them. 

Over the past decade I have also built a profile as a researcher on Project Guru's Knowledge Tank division. I have penned over 200 articles that have earned me 400+ citations so far. My Google Scholar profile can be accessed here

I now consult university faculty through Faculty Development Programs (FDPs) on the latest developments in the field of research. I also guide individual researchers on how they can commercialise their inventions or research findings. Other developments im actively involved in at Project Guru include strengthening the "Publish" division as a bridge between industry and academia by bringing together experienced research persons, learners, and practitioners to collaboratively work on a common goal. 


I am a master's in Economics from Amity University. Having a keen interest in Econometrics and data analysis, I was a part of the Innovation Project of Daulat Ram College, Delhi University. My core expertise and interest are in environment-related issues. Apart from academics, I love music and exploring new places.


1 thought on “How to work with a moderating variable in the regression test with SPSS?”