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Quantitative Data Analysis for Social Scientists 2nd Revised edition [Pehme köide]

  • Formaat: Paperback / softback, 312 pages, kõrgus x laius: 234x156 mm, appendix, bibliography, index
  • Ilmumisaeg: 04-Aug-1994
  • Kirjastus: Routledge
  • ISBN-10: 0415113075
  • ISBN-13: 9780415113076
Quantitative Data Analysis for Social Scientists 2nd Revised edition
  • Formaat: Paperback / softback, 312 pages, kõrgus x laius: 234x156 mm, appendix, bibliography, index
  • Ilmumisaeg: 04-Aug-1994
  • Kirjastus: Routledge
  • ISBN-10: 0415113075
  • ISBN-13: 9780415113076
Most introductions to the techniques of statistical analysis concentrate on the often complex statistical formulae involved. Many students find these formulae extremely daunting, yet in practice computers are increasingly used to perform the same calculations in seconds.
Quantitative Data Analysis for Social Scientists is designed as a non-technical guide, ignoring the traditional formulaic methods and introducing students to the most widely used computer package for analysing quantitative data. This is the Statistical Package for the Social Sciences (SPSS), whose most recently released versions (for both mainframe computers and IBM-compatible personal computers) are here employed. The authors have assumed no previous familiarity with either statistics or computing, and take the reader step-by-step through each of the techniques for which SPSS can be used.
Each technique is illustrated by sets of data through which the reader can work, and tested again at the end of each chapter. Answers to the exercises are provided at the end of the book.
Designed specifically for social scientists, the book will be essential reading for psychology, sociology, social policy and history students following courses in statistics, data analysis or research methods.
Preface
1. Data analysis and the research process
2. Analysing data with
computers: first steps with SPSS and SPSS/PC+
3. Analysing data with
computers: further steps with SPSS and SPSS/PC+
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 of more variables
11.
Aggregating variables: exploratory factor analysis Appendix Answers to
exercises Bibliography Index