IBM SPSS Statistics 27 Step by Step: A Simple Guide and Reference, seventeenth edition, takes a straightforward, step-by-step approach that makes SPSS software clear to beginners and experienced researchers alike. Extensive use of four-color screen shots, clear writing, and step-by-step boxes guide readers through the program. Output for each procedure is explained and illustrated, and every output term is defined. Exercises at the end of each chapter support students by providing additional opportunities to practice using SPSS.
This book covers the basics of statistical analysis and addresses more advanced topics such as multidimensional 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. The end sections include a description of data files used in exercises, an exhaustive glossary, suggestions for further reading, and a comprehensive index.
IBM SPSS Statistics 27 Step by Step is distributed in 85 countries, has been an academic best seller through most of the earlier editions, and has proved an invaluable aid to thousands of researchers and students.
New to this edition:
- Screenshots, explanations, and step-by-step boxes have been fully updated to reflect SPSS 27
- A new chapter on a priori power analysis helps researchers determine the sample size needed for their research before starting data collection.
Preface; 1. An Overview of IBM® SPSS® 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. A Priori Power Analysis: What Sample Size Do I Need? 11. Bivariate Correlation 12. The t Test Procedure 13. The One-Way ANOVA Procedure 14. General Linear Model: Two-Way ANOVA 15. General Linear Model: Three-Way ANOVA 16. Simple Linear Regression 17. Multiple Regression Analysis 18. Nonparametric Procedures 19. Reliability Analysis 20. Multidimensional Scaling 21. Factor Analysis 22. Cluster Analysis 23. Discriminant Analysis 24. General Linear Models: MANOVA and MANCOVA 25. G.L.M.: Repeated-Measures MANOVA 26. Logistic Regression 27. Hierarchical Log-Linear Models 28. Nonhierarchical Log-Linear Models 29. Residuals: Analyzing Left-Over Variance; Data Files Glossary References