• 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 ...

Non-Linear and Non-Additive Effects in Linear Regression
Non-linear and non-additive effects in linear regression
HenningLohmann
Introduction

This chapter discusses the approaches to modeling non-additive and non-linear relationships within the framework of multiple regression. When using multiple regression in its simplest form we have to assume that relationships are linear and additive (see Chapter 5 in this volume). Linearity means that the strength of a relationship between a variable X and a variable Y does not differ across the range of the variable X. However, many research questions in the social sciences address non-linear relationships. We speak of additivity if the relationship between a variable X1 and a variable Y is not dependent on the value of a variable X2. Yet often we observe interactions between two variables: the ...

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