This new edition of one of the most widely read textbooks in its field introduces the reader to data analysis with the most powerful and versatile statistical package on the market: IBM SPSS Statistics 22. This practical and informal book combines simplicity and clarity of presentation with a comprehensive treatment of the use of IBM SPSS Statistics 22 for the description, exploration and confirmation of data. As in earlier editions, coverage has been extended to address the issues raised by readers since the previous edition. Each statistical technique is presented in a realistic research context and is fully illustrated with annotated screen shots of SPSS dialog boxes and output. A fully updated version of this tried and tested textbook, IBM SPSS Statistics 22 Made Simple will: Get you started with SPSS. Show you how to describe and explore a data set with the help of SPSS's extensive graphics and data-handling menus. Help you choose the most appropriate statistical techniques. Warn you of pitfalls arising from the misuse of statistics. Show you how to report the results of a statistical analysis. Show you how to use syntax to implement some useful procedures and operations.The book's accompanying website contains data sets for the chapters of the book, as well as a large body of exercises (with data sets), and notes on statistical terms. Instructor resources include a PowerPoint lecture course and Multiple-Choice Question tests, which are also available free of charge to lecturers adopting the book and their students.

1. Introduction. 2. Getting Started with SPSS Statistics 22. 3. Editing Data Sets. 4. Describing and Exploring Your Data. 5. More On Graphs and Charts. 6. Comparing Averages: Two-sample and One-sample Tests. 7. The One-way ANOVA. 8. Between Subjects Factorial Experiments. 9. Within Subjects Experiments. 10. Mixed Factorial Experiments. 11. Measuring Statistical Association. 12. Regression. 13. The Analysis of Covariance (ANCOVA). 14. Analyses of Multiway Frequency Tables. 15. Predicting Category Membership: Logistic Regression. 16. The Search for Latent Variables: Factor Analysis.