Full Description
IBM SPSS Statistics 25 Step by Stepfifteenth 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. Exercises at the end of each chapter support students by providing additional opportunities to practice using SPSS.This book covers both the basics of descriptive statistical analysis using SPSS through to more advanced topics such as multiple regression, multidimensional scaling and MANOVA, including instructions for Windows and Mac. This makes it ideal for both undergraduate statistics courses and for postgraduates looking to further develop their statistics and SPSS knowledge.New to this edition:Updated throughout to SPSS 25Updated / restructured material on: Chart Builder; Univariate ANOVA; moderation on two- and three-way ANOVA; and Factor Analytic Techniques (formerly Factor Analysis structure) New material on computing z and T scores, and on computing z scores within descriptive statistics Clearer in-chapter links between the type of data and type of research question that the procedure can answer Updated / additional datasets, exercises, and expanded Companion Website material, including Powerpoint slides for instructors
Contents
Preface 1. An Overview of IBM SPSS Statistics2a. 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 Statistics8. Crosstabulation and 2 Analyses 9. The Means Procedure 10. Bivariate Correlation11. The t Test Procedure 12. The One-Way ANOVA Procedure 13. General Linear Models: Two-Way ANOVA 14. General Linear Models: 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 VarianceData Files Glossary References Credits Index
