I was able to make the GLM results match the multiple regression, but I don’t understand why. This discrepancy only occurs when the interaction term is included in the models otherwise, the output of the two procedures matches. The p-values for the categorical IV and the interaction term are the same across models. The SPSS GLM and multiple regression procedures give different p-values for the continuous IV. I have a continuous DV, categorical IV, continuous IV, and I would like to include the interaction of IVs. See these for more info:ĭummy Coding in SPSS GLM–More on Fixed Factors, Covariates, and Reference Groups, Part 1 Dummy Coding in SPSS GLM–More on Fixed Factors, Covariates, and Reference Groups, Part 2 If you have them backwards, everything will look different.
#SPSS MODELER 18 IMPUTATION CODE#
When you dummy code your variables yourself in Regression, you’re matching GLM’s default coding.You have to ask for them, and in GLM they’re called “Parameter Estimates” in the Options button. GLM doesn’t give you the regression coefficients by default. Both procedures will give you a table of F statistics and can give a table of regression coefficients along with p-values, but they are labeled differently, look different, and don’t all appear by default.Make sure you’re not trying to compare p-values from regression coefficients in one to the p-values from the F table in the other. Edited to add:Ī number of commenters below are wondering why the results aren’t matching between SPSS’s GLM and Linear Regression. So my approach is to generally use GLM for my regression analysis, then rerun the model in regression if I see a reason to be concerned about multicollinearity. And the stepwise procedures are only useful with truly exploratory analyses, and even then you need to be able to test the models on another data set.
Remember, you can’t use standardized coefficients on dummy variables anyway (well, SPSS will let you, but they don’t mean anything). Of these three options, only the third is really useful when you are testing specific hypotheses that contain interactions and categorical predictors. These are really an advantage when your model is exploratory in nature and contains only continuous variables. It will do multicollinearity diagnostics. It will do model selection procedures, such as stepwise regression and hierarchical model building, that allows you to enter variables in blocks.ģ. It automatically gives standardized regression coefficients.Ģ. Regression has these options that GLM doesn’t:ġ. Once again, this can become very tedious, especially if those interactions contain dummy variables. In Regression, you have to create each interaction as a separate variable. But if you have several, and many of them are multi-category, this is a big advantage, both as a time saver, and for getting an overall p-value for the variable as a whole.Ģ. If you have only one or two binary categorical variables, this isn’t a huge advantage.
It will dummy code categorical variables for you. GLM has these options that Regression doesn’t:ġ. How do you decide when to use GLM and when to use Regression? It is what I usually use.īut in SPSS there are options available in the GLM and Regression procedures that aren’t available in the other. Regression models are just a subset of the General Linear Model, so you can use GLM procedures to run regressions.