Developed by Naomi Schalken and Rens van de Schoot
This tutorial provides the reader with a basic introduction to the software package SPSS. The reader will be guided through the investigation of basic data relations using correlations and through the process of conducting a multiple regression analysis in SPSS.
Throughout this tutorial we will use a dataset from Van de Schoot, van der Velden, Boom & Brugman (2010). Using multiple regression, we will predict adolescents’ socially desirable answering patterns (sd) from overt (overt) and covert (covert) antisocial behaviour. For more information on the sample, instruments, methodology and research context we refer the interested reader to the paper (see references). Here we will focus on data-analysis only. All the solutions, final data sets and syntax files can be found in the subfolder tilted ‘solutions’.
Exercise 1 - Multiple regression in SPSS
In this exercise you will run a regression model with sd as outcome variable and overt and covert as predictors. You can find the data in popular_regr_1.xlsx, which you can directly read into SPSS. Note: later on in the exercise you will be asked to compare the results from Exercise 1 with results obtained in R/lavaan. Make sure to save or write down the results you found in this exercise.
Exercise 1a. Obtain correlations between the variables of interest by running the following syntax
File -> New -> Syntax:
/VARIABLES=sd covert overt
Simply copy-paste these four lines into the new syntax file, select all text
Ctrl+A and run the commands
Question: What do the significance, signs and magnitudes of the correlations tell you about the relationships between variables?
Exercise 1b. Run the multiple regression model described above by running the following syntax:
/DESCRIPTIVES MEAN STDDEV CORR SIG N
/STATISTICS COEFF OUTS R ANOVA
/METHOD=ENTER covert overt
/SCATTERPLOT=(sd , covert) (sd , overt).
Question: What do you conclude from the regression coefficients? Include the significance and relevance (R2) of effects in your answer.