Introducing data analysis for social scientists

著者

書誌事項

Introducing data analysis for social scientists

David Rose and Oriel Sullivan

Open University Press, 1993

大学図書館所蔵 件 / 全8件

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注記

Includes bibliographical references (p. [199]-200) and index

内容説明・目次

内容説明

This textbook is designed for social science students taking their first course in quantitative data analysis. It requires no previous knowledge of statistics or computer use, nor any mathematics beyond an elementary level. It introduces students to the principles of analyzing data in simple stages, including an introduction to using computers and SPSS/PC+, the most widely used statistical package in the social sciences. The emphasis throughout is on an understanding of the underlying principles of data analysis, and on elucidating these with simple but realistic worked examples which stress the role of theory in social research and the logic of data analysis. The first four parts of the text give students a grasp on the logic and language of social research; preparation of data and basic ideas in coputing; descriptive data statistics for both single variables and bivariate analyses; and inferential statistics. The final part introduces some of the most multivariate techniques and discuesses the problems and potential of longitudinal studies. The text comes complete with exercises and examples from the British Class Survey, and a subset of that data on a free floppy disk. The work is designed to serve as a valuable beginner's guide to students in geography. political science, sociology, social policy, management, social psychology and related disciplines.

目次

  • Part 1 The logic and language of social research: introducing data analysis
  • the logic of data analysis. Part 2 From data collection to computer: preparing the data
  • getting to know the computer - DOS and SPSS/PC+. Part 3 Descriptive data analysis in social research: from computer to analysis - descriving single variables
  • univariate descriptive statistics using SPSS/PC+
  • bivariate analysis for categoric variables - measures of association
  • bivariate analysis for interval level variables - regression and correlation. Part 4 Inferential data analysis in social research: from sample to population - the idea of inferential statistics
  • tests of significance for categoric variables. Part 5 Introduction to multivariate analysis: general linear models - multivariate analysis
  • longitudinal data - their collection and analysis.

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