George D., Mallery P. IBM SPSS Statistics 29 Step by Step: A Simple Guide and Reference

Routledge, 2024. — 441 p.IBM SPSS Statistics 29 Step by Step: A Simple Guide and Reference, eighteenth 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. New to this edition is a new chapter on meta-analysis that describes new SPSS procedures for analyzing effect sizes across studies, and the content has been thoroughly updated in line with the latest version of the SPSS software, SPSS 29. The end sections include a description of data files used in exercises, an exhaustive glossary, suggestions for further reading, and a comprehensive index. Accompanied by updated online instructor’s materials and website data files, this is an essential resource for instructors and students needing a guide to using SPSS in their work, across the social sciences, behavioural sciences, education, and beyond.Dedications
Preface
An Overview of IBM SPSS Statistics
IBM SPSS Statistics Processes for PC
IBM SPSS Statistics Processes for Mac
Creating and Editing a Data File
Managing Data
Graphs and Charts: Creating and Editing
Frequencies
Descriptive Statistics
Crosstabulation and χ2 Analyses
The Means Procedure
A Priori Power Analysis: What Sample Size Do I Need?
Bivariate Correlation
The t Test Procedure
The One-Way ANOVA Procedure
General Linear Model: Two-Way ANOVA
General Linear Model: Three-Way ANOVA
Simple Linear Regression
Multiple Regression Analysis
Nonparametric Procedures
Reliability Analysis
Multidimensional Scaling
Factor Analysis
Cluster Analysis
Discriminant Analysis
General Linear Models: MANOVA and MANCOVA
G.L.М.: Repeated-Measures MANOVA
Logistic Regression
Hierarchical Log-Linear Models
Nonhierarchical Log-Linear Models
Residuals: Analyzing Left-Over Variance
Meta-Analysis
Data Files
Glossary
References
Credits
Index