This latest edition has been fully updated to accommodate the needs of users of SPSS Releases 17, 18 and 19 while still being applicable to users of SPSS Releases 15 and 16.

As with previous editions, Alan Bryman and Duncan Cramer continue to offer a comprehensive and user-friendly introduction to the widely used IBM SPSS Statistics. The simple, non-technical approach to quantitative data analysis enables the reader to quickly become familiar with SPSS and with the tests available to them.

No previous experience of statistics or computing is required as this book provides a step-by-step guide to statistical techniques, including:

  • Non-parametric tests
  • Correlation
  • Simple and multiple regression
  • Analysis of variance and covariance
  • Factor analysis.

This book comes equipped with a comprehensive range of exercises for further practice, and it covers key issues such as sampling, statistical inference, conceptualization and measurement and selection of appropriate tests. The authors have also included a helpful glossary of key terms.

The data sets used in Quantitative Data Analysis with IBM SPSS 17, 18 and 19 are available online at https://www.routledgetextbooks.com/textbooks/_author/bryman-9780415579193/; in addition, a set of multiple-choice questions and a chapter-by-chapter PowerPoint lecture course are available free of charge to lecturers who adopt the book.

chapter 1|17 pages

Data analysis and the research process

chapter 2|29 pages

Analysing data with computers

First steps with SPSS 17, 18 and 19

chapter 3|18 pages

Analysing data with computers

Further steps with SPSS 17, 18 and 19

chapter 4|18 pages

Concepts and their measurement

chapter 5|34 pages

Summarizing data

chapter 6|20 pages

Sampling and statistical significance

chapter 7|55 pages

Bivariate analysis

Exploring differences between scores on two variables

chapter 8|48 pages

Bivariate analysis

Exploring relationships between two variables

chapter 9|35 pages

Multivariate analysis

Exploring differences among three or more variables

chapter 10|38 pages

Multivariate analysis

Exploring relationships among three or more variables

chapter 11|18 pages

Aggregating variables

Exploratory factor analysis