Paul Mallery is currently a professor at La Sierra University where he teaches social psychology and experimental methodology (including the application of SPSS). He received his Ph.D. in Social Psychology from UCLA (1994) with emphases in statistics and political psychology. Paul formerly worked as a computer specialist, both programming and teaching computer usage.

2. IBM SPSS Statistics Processes for PC and Mac: Mouse and keyboard processing, frequently-used dialog boxes, editing output, printing results, the Options Option

3. Creating and Editing a Data File

4. Managing Data: Listing cases, replacing missing values, computing new variables, recoding variables, exploring data, selecting cases, sorting cases, merging files

5. GRAPHS: Creating anad editing graphs and charts

IBM SPSS STATISTICS BASE MODULE

6. FREQUENCIES: Frequencies, bar charts, histograms, percentiles

7. DESCRIPTIVE Statistics: Measures of central tendency, variability, deviation from normality, size, and stability

8. CROSSTABULATION and Chi-Square Analyses

9. The MEANS Procedure

10. Bivariate CORRELATION: Bivariate correlations, partial correlations, and the correlation matrix

11. The T TEST Procedure: Independent-samples, paired-samples, and one-sample tests

12. The One-Way ANOVA Procedure: One-Way Analysis of Variance

13. General Linear Models: Two-Way Analysis of Variance

14. General Linear Models: Three-Way Analysis of Variance and the influence of covariates

15. Simple Linear REGRESSION

16. MULTIPLE REGRESSION ANALYSIS

17. NONPARAMETRIC Procedures

18. RELIABILITY ANALYSIS: Coefficient alpha and split-half reliability

19. MULTIDIMENSIONAL SCALING

20. FACTOR ANALYSIS

21. CLUSTER ANALYSIS
22. DISCRIMINANT ANALYSIS

IBM SPSS REGRESSION AND ADVANCED STATISTICS MODULES

23. General Linear Models: MANOVA and MANCOVA Multivariate Analysis of Variance and Covariance

24. General Linear Models: Repeated-Measures MANOVA: Multivariate Analysis of Variance with repeated measures and within-subjects factors

25. LOGISTIC REGRESSION

26. Hierarchical LOGLINEAR MODELS

27. General LOGLINEAR MODELS

28. RESIDUALS: Analyzing left-over variance

Data Files

Glossary

References

Index

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