- Summary
- Contents
- Subject index
‘The editors of the new SAGE Handbook of Regression Analysis and Causal Inference have assembled a wide-ranging, high-quality, and timely collection of articles on topics of central importance to quantitative social research, many written by leaders in the field. Everyone engaged in statistical analysis of social-science data will find something of interest in this book.’
- John Fox, Professor, Department of Sociology, McMaster University
‘The authors do a great job in explaining the various statistical methods in a clear and simple way - focussing on fundamental understanding, interpretation of results, and practical application - yet being precise in their exposition.’
- Ben Jann, Executive Director, Institute of Sociology, University of Bern
‘Best and Wolf have put together a powerful collection, especially valuable in its separate discussions of uses for ...
Chapter 11: Regression with Complex Samples
Regression with Complex Samples
Introduction
A Short History of Regression Analysis and Inference for Complex Sample Survey Data
The science of survey sampling, survey data collection methodology and the analysis of survey data is less than a century old. The basic theory for ‘design-based’ inference for descriptive population parameters such as means, proportions and totals was laid down in a landmark paper by Jerzy Neyman (1934). Following the publication of Neyman's paper, there was a major proliferation of new work on sample design, estimation of population parameters and variance estimation techniques required to develop confidence intervals for sample-based inference, or what in more recent times has been labeled design-based inference (Deming, 1950; Hansen et al., 1953; Sukatme, 1954; ...
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