Peter Martin

Regression Models for Categorical and Count Data (eBook, ePUB)

• Format: ePub

Produktbeschreibung

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

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Produktdetails

• Verlag: SAGE Publications
• Seitenzahl: 272
• Erscheinungstermin: 26. März 2022
• Englisch
• ISBN-13: 9781529762686
• Artikelnr.: 62234897

Autorenporträt

Dr Peter Martin is Lecturer in Applied Statistics at University College London. He has taught statistics to students of sociology, psychology, epidemiology, and other disciplines since 2003. One of the joys of being a statistician is that it opens doors to research collaborations with many people in diverse fields. Dr Martin has been involved in investigations in life course research, survey methodology, and the analysis of racism. In recent years his research has focused on health inequalities, psychotherapy, and the evaluation of healthcare services. He has a particular interest in topics around mental health.

Inhaltsangabe

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

Rezensionen

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 20220510