I'll go for tertile groups: the youngest, intermediate and oldest 33.3% of the clients will make up my groups. Our simple slopes analysis starts with creating age groups. Altogether, these plots don't show clear violations of the regression assumptions. the residual scatterplot doesn't show any signs of heteroscedasticity or curvilinearity.This somewhat depends on its bin width and doesn't look too alarming the residual histogram doesn't look entirely normally distributed but -rather- bimodal.With regard to the residual plots (not shown here), note that We'll therefore examine the interaction in-depth by means of a simple slopes analysis. Regardless of statistical significance, I think the interaction may be ignored if its part correlation r < 0.10 or so but that's clearly not the case here. The training effect is almost large and the age and age by training interaction are almost medium. As effect size measures we could use the semipartial correlations (denoted as “Part”) where Now, for any effect to bear any importance, it must be statistically significant and have a reasonable effect size.Īt p = 0.000, all 3 effects are highly statistically significant. The negative B-coefficient for the interaction predictor indicates that the training effect becomes more negative -or less positive- with increasing ages. Training hours are positively related to muscle percentage: clients tend to gain 0.9 percentage points for each hour they work out per week. On average, clients lose 0.072 percentage points per year. SPSS Moderation Regression - Coefficients OutputĪge is negatively related to muscle percentage. REGRESSION /MISSING LISTWISE /STATISTICS COEFF OUTS R ANOVA ZPP /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT mperc /METHOD=ENTER cent_age cent_thours int_1 /SCATTERPLOT=(*ZRESID ,*ZPRED) /RESIDUALS HISTOGRAM(ZRESID). *Regression with mean centered predictors and interaction predictor. Our moderation regression is not different from any other multiple linear regression analysis: we navigate toĬlicking Paste results in the following syntax.
Multiple regression spss how to#
We did the mean centering with a simple tool which is downloadable from SPSS Mean Centering and Interaction Tool.Īlternatively, mean centering manually is not too hard either and covered in How to Mean Center Predictors in SPSS? SPSS Moderation Regression - Dialogs These 3 predictors are all present in muscle-percent-males-interaction.sav, part of which is shown below. SPSS Moderation Regression - Example Data finally, we enter both mean centered predictors and the interaction predictor into a regression analysis.we then multiply the centered predictors into an interaction predictor variable.if both predictors are quantitative, we usually mean center them first.So how to test for such a moderation effect? Well, we usually do so in 3 steps: In multiple regression analysis, this is known as a moderation interaction effect. The effect of training on muscularity declines with age. Our doctor suspects that clients who train more are also more muscled. He also asks them how many hours per week they typically spend on training. Simple Slopes Analysis II - CoefficientsĪ sports doctor routinely measures the muscle percentages of his clients.SPSS Moderation Regression - Coefficients Output.SPSS Moderation Regression - Example Data.SPSS Moderation Regression Tutorial By Ruben Geert van den Berg under Regression
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