• 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 ...

Estimation Techniques: Ordinary Least Squares and Maximum Likelihood
Estimation techniques: Ordinary least squares and maximum likelihood
MartinElff
Introduction

A major task in regression analysis and in much of data analysis in the social sciences in general is the construction of a model that best represents (1) substantial assumptions and hypotheses a researcher may entertain and (2) auxiliary information or assumptions about the way the data under analysis are generated. To complete this task of model specification successfully, a researcher will need a fair knowledge of a variety of statistical models and their assumptions. Introducing these is one of the main purposes of this volume. In contrast to most other chapters, the present one presumes all questions with regard to model specification as already addressed and focuses on the ...

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