Quantitative data analysis with IBM SPSS 17, 18 and 19 : a guide for social scientists

著者

書誌事項

Quantitative data analysis with IBM SPSS 17, 18 and 19 : a guide for social scientists

Alan Bryman and Duncan Cramer

Routledge, 2011

  • : hbk
  • : pbk

タイトル別名

Quantitative data analysis with IBM SPSS 17, 18 & 19 : a guide for social scientists

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注記

Includes bibliographical references (p. 365-368) and index

内容説明・目次

内容説明

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 http://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.

目次

Preface. 1. Data Analysis and the Research Process. 2. Analyzing Data with Computers: First Steps with SPSS 19, 18 and 17. 3. Analyzing Data with Computers: Further Steps with SPSS 19, 18 and 17. 4. Concepts and Their Measurement. 5. Summarizing Data. 6. Sampling and Statistical Significance. 7. Bivariate Analysis: Exploring Differences between Scores on Two Variables. 8. Bivariate Analysis: Exploring Relationships. 9. Multivariate Analysis: Exploring Differences among Three or More Variables. 10. Multivariate Analysis: Exploring Relationships among Three or More Variables. 11. Aggregating Variables: Exploratory Factor Analysis. Answers to Exercises. Glossary. Bibliography. Index.

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