Analyzing Tabular Data

First published in 1993, Analyzing Tabular Data is an accessible text introducing a powerful range of analytical methods. This book is for those who have little prior knowledge of quantitative analysis or statistics, but who have a practical need to extract the most from their data.

Autorentext

Nigel Gilbert

Klappentext

First published in 1993, Analyzing Tabular Data is an accessible text introducing a powerful range of analytical methods. Empirical social research almost invariably requires the presentation and analysis of tables, and this book is for those who have little prior knowledge of quantitative analysis or statistics, but who have a practical need to extract the most from their data. The book begins with an introduction to the process of data analysis and the basic structure of cross-tabulations. At the core of the methods described in the text is the loglinear model. This and the logistic model, are explained and their application to causal modelling, to event history analysis, and to social mobility research are described in detail. Each chapter concludes with sample programs to show how analysis on typical datasets can be carried out using either the popular computer packages, SPSS, or the statistical programme, GLIM. The book is packed with examples which apply the methods to social science research.

Sociologists, geographers, psychologists, economists, market researchers and those involved in survey research in the fields of planning, evaluation and policy will find the book to be a clear and thorough exposition of methods for the analysis of tabular data.

Zusammenfassung
First published in 1993, Analyzing Tabular Data is an accessible text introducing a powerful range of analytical methods. This book is for those who have little prior knowledge of quantitative analysis or statistics, but who have a practical need to extract the most from their data.

Inhalt

Preface 1. Real and imaginary worlds 2. Classification and measurement 3. Cross-tabulations 4. Association and interaction 5. Loglinear analysis 6. Choosing and fitting models 7. Modelling mobility and change 8. High-dimension tables 9. Effects and odd ratios 10. Logistic regression 11. Causal analysis 12. Models with ordinal variables 13. Event history models Reference Index