![moderator analysis in spss 25 moderator analysis in spss 25](https://centerstat.org/wp-content/uploads/2016/09/HSBoutput.jpg)
EMMEANS=TABLES(group) WITH(NEWAGE=45) COMPARE ADJ(SIDAK) EMMEANS=TABLES(group) WITH(NEWAGE=MEAN) COMPARE ADJ(SIDAK)
![moderator analysis in spss 25 moderator analysis in spss 25](https://i.stack.imgur.com/0V0GR.png)
UNIANOVA OverallPost BY group WITH NEWAGE Young people learned more in all three treatment groups.īut at an older age, say 50, the means of the purple and tan groups were not significantly different from the control group’s (blue), and the green (EIQ group) did worse! So it would tell us that at a young age of say 20, the three treatment groups (green, tan, and purple lines) all have means higher than the control (blue). But we can change the value of the covariate at which to compare the means using syntax.
![moderator analysis in spss 25 moderator analysis in spss 25](https://bandsfasr823.weebly.com/uploads/1/2/4/1/124123082/801633501.png)
If you use the menus in SPSS, you can only get those EMMeans at the Covariate’s mean, which in this example is about 25, where the vertical black line is. They do the same thing–calculate the mean of Y for each group, at a specific value of the covariate. SAS Proc GLM uses the LSMeans statement and SPSS GLM uses EMMeans. You can get p-values, adjusted for multiple comparisons, using either SAS or SPSS GLM. (Okay, not always easily done, but easily found in…)īut this doesn’t make very much sense when Age is really a moderator–a predictor we want to control for, and see how it affects the relationship between the independent (IV) and dependent variables (DV), but not really the IV we’re interested in.Ī better way to do it in this situation is to compare the means among groups at a low value of Age, say 20, and again at a high value of Age, say 50. One way to interpret this significant interaction is to compare the slopes of the four lines, which is easily done with any regression coefficient table. The effect of the training is depending on the trainee’s age. Another way to say that is there is a significant interaction between Age and Training Group. The continuous moderator is Age, and the outcome is OverallPost, which is the post-training test score to see how well they learned the material in each training program.Īs you can see, the effect of the training program is moderated by age. There are four groups, each of which received a different training. Here is an example of a scatterplot of just such a model: Scatterplot of Ancova There are other examples, but today I’m going to focus on an ANOVA model with a continuous covariate.Ī common model is one in which one predictor is categorical (we’ll use 4 categories) and the other is continuous.
![moderator analysis in spss 25 moderator analysis in spss 25](https://images-na.ssl-images-amazon.com/images/I/81GcrcpuY9L.jpg)
And we can come up with nice names for these models–a regression with dummy variables or an Analysis of Covariance.īut real understanding of the relationships among variables comes only when you dispense of the names and can focus on analyzing and interpreting the model using the kinds of variables you have. Second, regardless of which one you normally use, you’re going to occasionally have to use the other kind of predictor variables–categorical or continuous. They’re really the same model with different outfits on. Different scale here).įirst of all, the distinction between ANOVA and linear regression is arbitrary. (Okay, I was going to say tragedy, but let’s be real.