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Regression Models for Categorical and Count Data

Regression Models for Categorical and Count Data

  • Peter Martin - University College London, UK, Lecturer in Applied Statistics in the Department of Applied Health Research at University College London.
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May 2022 | 272 pages | SAGE Publications Ltd

This text provides practical guidance on conducting regression analysis on categorical and count data. Step by step and supported by lots of helpful graphs, it covers both the theoretical underpinnings of these methods as well as their application, giving you the skills needed to apply them to your own research. It offers guidance on:

  • Using logistic regression models for binary, ordinal, and multinomial outcomes
  • Applying count regression, including Poisson, negative binomial, and zero-inflated models
  • Choosing the most appropriate model to use for your research
  • The general principles of good statistical modelling in practice.

Part of The SAGE Quantitative Research Kit, this book will give you the know-how and confidence needed to succeed on your quantitative research journey

Logistic regression
Ordinal logistic regression: the generalised ordered logit model
Multinomial logistic regression
Regression models for count data
The practice of modelling

An accessible but rigorous introduction to data analysis that makes good use of real-world examples. The focus in this book on categorical and count data makes it particularly appropriate for social scientists who are often aiming to understand the predictors of social phenomena that cannot be measured numerically.

Jane Elliott
Professor of Sociology at the University of Exeter

Anyone willing to learn about regression for the first time, as well as readers already familiar with the topic, can dive straight into this book and will be positively surprised by its clarity and accessibility. It covers everything one has to know when it comes to regression models for categorical and count data. [...] It has very apt examples and a clear style of writing. I think that the author has done a great job of keeping all the explanations as understandable as possible, making them accessible to anyone interested in the topic. All in all, this is an extremely good book and I highly recommend it if you want to learn more about regression.

Antonella Cirasola
Clinical Psychologist

This book succeeds in giving a great outline of a large number of different statistical modelling techniques, with a streamlined narrative that makes essential links between them. Instead of completely separate chapters, the book’s format builds upon the information of different sections to provide the reader with concise yet thorough knowledge of some of the most relevant techniques used in data-driven research. The simple and comprehensive way in which Peter guides us to interpret the variety of estimated coefficients of the different regression models explored is superb. Moreover, the last chapter provides essential directions on decision-making in the process of research when selecting and implementing some of the statistical modelling techniques covered in the book. This section is a call to us researchers to critically examined our research problems and make reasoned decisions about them instead of just following a statistical recipe.

Eliazar Luna
University College London

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