Personally, I prefer to use these when testing interactions because the intepretation of coefficients can be slightly simpler. The results obtained should be identical whichever method you use. IV and moderator in the following worksheet. If you want to test simple slopes, you can use the following worksheet. Again, control variables should be centered or standardised before the analysis. However, note that simple slope tests are only useful for testing significance at specific values of the moderator. If you are using SPSS, this can be done by selecting “Covariance matrix” in the “Regression Coefficients” section of the “Statistics” dialog box.
Note that the variance of a coefficient is the covariance of that coefficient with itself – i. Also allows slopes to be plotted at values of the moderator chosen by the user. Also allows slopes to be plotted at specific values of the moderators chosen by the user. To plot simple quadratic effects, use Quadratic_regression. To plot quadratic effects moderated by one variable, use Quadratic_two-way_interactions. To plot quadratic effects moderated by two variables, use Quadratic_three-way_interactions.
There are a number of common problems encountered when trying to plot these effects. If the graph does not appear, it may be because it is off the scale. SPSS is prone to printing the covariances in a different order from the regression coefficients themselves, which can be confusing. Multiple regression: Testing and interpreting interactions. Moderation in management research: What, why, when and how. Journal of Business and Psychology, 29, 1-19.
This article includes information about most of the tests included on this page, as well as much more! Probing three-way interactions in moderated multiple regression: Development and application of a slope difference test. Journal of Applied Psychology, 91, 917-926. In statistics and regression analysis, moderation occurs when the relationship between two variables depends on a third variable. The third variable is referred to as the moderator variable or simply the moderator. Moderation analysis in the behavioral sciences involves the use of linear multiple regression analysis or causal modelling.
In this case, the role of x2 as a moderating variable is accomplished by evaluating b3, the parameter estimate for the interaction term. See linear regression for discussion of statistical evaluation of parameter estimates in regression analyses. However, the new interaction term will be correlated with the two main effects terms used to calculate it. This is the problem of multicollinearity in moderated regression. However, mean-centering is unnecessary in any regression analysis, as one uses a correlation matrix and the data are already centered after calculating correlations. Like simple main effect analysis in ANOVA, in post-hoc probing of interactions in regression, we are examining the simple slope of one independent variable at the specific values of the other independent variable.
Below is an example of probing two-way interactions. If both of the independent variables are categorical variables, we can analyze the results of the regression for one independent variable at a specific level of the other independent variable. 1 represents the difference in the dependent variable between males and females when life satisfaction is zero. However, a zero score on the Satisfaction With Life Scale is meaningless as the range of the score is from 7 to 35.