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, the 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 screenshots, 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 materials and website data files, this is an essential resource for instructors and students needing a guide to use SPSS in their work, across the social sciences, behavioral sciences, education, and beyond.Dedications.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 Analyze.
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.M.: Repeated-Measures MANOVA.
Logistic Regression.
Hierarchical Log-Linear Models.
Nonhierarchical Log-Linear Models.
Residuals: Analyzing Left-Over Variance.
Meta-Analysis.
Data Files.