How to do a Moderation analysis in statistics?
A moderation analysis typically consists of the following steps. Compute the interaction term XZ=X*Z. Fit a multiple regression model with X, Z, and XZ as predictors. Test whether the regression coefficient for XZ is significant or not. Interpret the moderation effect. Display the moderation effect graphically.
How to test for a moderation interaction in multiple regression?
In multiple regression analysis, this is known as a moderation interaction effect. The figure below illustrates it. So how to test for such a moderation effect? Well, we usually do so in 3 steps:
How to check normality assumption in moderator analysis?
In our enhanced moderator analysis guide, we: (a) show you how to check this assumption using the Shapiro-Wilk test for normality using SPSS Statistics; (b) explain how to interpret the result; and (c) provide possible options if your data fails to meet this assumption. You can check assumptions #3, #4, #5, #6, #7 and #8 using SPSS Statistics.
How to do a moderation regression in SPSS?
Our moderation regression is not different from any other multiple linear regression analysis: we navigate to A nalyze R egression L inear and fill out the dialogs as shown below. Clicking P aste results in the following syntax.
How to do a moderator analysis with a dichotomous moderator variable?
If it is, gender (i.e., the dichotomous moderator variable) moderates the relationship between the years of education and salary. This “quick start” guide shows you how to carry out a moderator analysis with a dichotomous moderator variable using SPSS Statistics, as well as interpret and report the results from this test.
How to determine if a moderating effect exists?
We use the standard method of determining whether a moderating effect exists, which entails the addition of an (linear) interaction term in a multiple regression model. For this reason, you might often hear this type of analysis being referred to as a moderated multiple regression or as its abbreviation, MMR (e.g., Aguinis, 2004).