IBM SPSS Statistics 26 Step by Step (16^th Ed.) A Simple Guide and Reference

This book covers the basics of statistical analysis and addresses more advanced topics such as multi-dimensional scaling, factor analysis, discriminant analysis, measures of internal consistency, MANOVA (between- and within-subjects), cluster analysis, Log-linear models, logistic regression and a chapter describing residuals. Back matter includes a description of data files used in exercises, an exhaustive glossary, suggestions for further reading and a comprehensive index.

IMB SPSS Statistics 26 Step by Step is distributed in 85 countries, has been an academic best seller through most of the earlier editions, and has proved invaluable aid to thousands of researchers and students.

• Screenshots, explanations, and step-by-step boxes have been fully updated to reflect SPSS 26
• How to handle missing data has been revised and expanded and now includes a detailed explanation of how to create regression equations to replace missing data
• More explicit coverage of how to report APA style statistics; this primarily shows up in the Output sections of Chapters 6 through 16, though changes have been made throughout the text.

Preface

1. An Overview of IBM® SPSS® Statistics

2A. IBM SPSS Statistics Processes for PC

2B. IBM SPSS Statistics Processes for Mac

3. Creating and Editing a Data File

4. Managing Data

5. Graphs and Charts: Creating and Editing

6. Frequencies

7. Descriptive Statistics

8. Crosstabulation and χ2 Analyses

9. The Means Procedure

10. Bivariate Correlation

11. The t Test Procedure

12. The One-Way ANOVA Procedure

13. General Linear Model: Two-Way ANOVA

14. General Linear Model: Three-Way ANOVA

15. Simple Linear Regression

16. Multiple Regression Analysis

17. Nonparametric Procedures

18. Reliability Analysis

19. Multidimensional Scaling

20. Factor Analysis

21. Cluster Analysis

22. Discriminant Analysis

23. General Linear Models: MANOVA and MANCOVA

24. G.L.M.: Repeated-Measures MANOVA

25. Logistic Regression

26. Hierarchical Log-Linear Models

27. Nonhierarchical Log-Linear Models

28. Residuals: Analyzing Left-Over Variance

Darren George is a Professor of Psychology at Burman University whose research focuses on intimate relationships and optimal performance. He teaches classes in research methodology, statistics, personality/social psychology, and sport and performance psychology.

Paul Mallery is a Professor of Psychology at La Sierra University whose research focuses on the intersection of religion and prejudice. He teaches classes is research methodology, statistics, social psychology, and political psychology.