Hierarchical regression comes down to comparing different regression models. Each model adds 1(+) predictors to the previous model, resulting in a “hierarchy” of models.
This tutorial quickly walks you through this analysis in SPSS.
Quick guide on creating dummy variables in SPSS for categorical predictors in regression.
Intermediate tutorial with practice data, examples and a handy tool.
How to run and interpret dummy variable regression in SPSS?
These 3 examples walk you through everything you need to know.
For mean centering predictors in SPSS, just add their means to the data. Then simply subtract them from the original variables. With examples & practice data.
Categorical variables can't readily be used as predictors in regression analysis; they must be split up into dichotomous variables known as “dummy variables”.
This tutorial offers a super easy tool for creating these.
"I'd like to mean center a lot of variables in order to compute interaction terms for a regression analysis. Is there an easy way to do this for many variables simultaneously?"
This simple SPSS tool creates one, many or all scatterplots among a set of variables.
Optionally, it adds (non)linear fit lines and regression tables as well.
Before running SPSS stepwise regression, first just get a grip on your data. This tutorial walks you through the essential data checks.
SPSS stepwise regression example. Easy-to-follow explanation of what and why with downloadable data file and annotated output.
SPSS stepwise regression analysis in normal language. With illustrations, downloadable practice data and syntax.
Multiple regression is a statistical technique that aims to predict a variable of interest from several other variables. This tutorial explains multiple regression in normal language with many illustrations and examples.
This tutorial walks you through several simple options for creating scatterplots with regression lines.
We'll also cover linear and nonlinear regression lines for all cases or subgroups of cases.