West (1991), Multiple Regression: Testing * This SPSS program is based upon Appendix C: SAS Program for Test of Critical Next, here is the program for computing the covariate values that correspond to the limits of the regions of significance. * The next set of commands runs regressions and requests descriptive statistics for each group separately. * The commands below perform a test of the HOS assumption. * The following data step reads in the raw data from the example.ĪDD VALUE LABELS college 1 'Engineering' 2 'Business'. Here is SPSS command syntax that replicates the example from Aiken and West (1991, pp. The regression results and the output from DESCRIPTIVES provide the raw data for the final program, which identifies regions of significance. The following commands (1) read in the raw data (2) perform a test of the HOS assumption using SPSS UNIANOVA and (3) perform separate SPSS REGRESSION and DESCRIPTIVES runs for each of the two groups. The two groups compared in this example are engineering and business students. The example involves a hypothetical data set in which the starting salaries of college graduates are modeled as a function of the type of degree earned and the overall GPA. To help SPSS users understand how the program works, we have included the data and SPSS commands that are necessary to replicate the regression results from Aiken and West (1991).
While this technical note is intended to be used by researchers who are unfamiliar with the Aiken and West (1991) text, many users will find that they need to consult that text for a fuller understanding of what is involved. The SAS version of the file appeared in Aiken and West's (1991) monograph on testing and interpreting interactions in multiple regression. The sample SPSS command syntax file provided here is a translation of a SAS command file written by Jenn-Yun Tein of Arizona State University. This technical note will provide a sample SPSS command syntax file, which researchers may use to apply a modification of the Johnson-Neyman technique due to Potthoff (1964). Johnson and Neyman (1936) developed a technique for determining regions of significance when there is a significant group-by-covariate interaction. The values sxsqr1 and sxsqr2 should be the sums of squared deviations of the X or predictor variable around its within-group means, not the predicted sums of squares from the within-groups regressions of Y on X. IMPORTANT NOTE: Please note that the formulas given in Aiken & West for the modified Johnson-Neyman technique contain an error.