Description

Book Synopsis

This book covers the main models within the GLM (i.e., logistic, Poisson, negative binomial, ordinal, and multinomial). For each model, estimations, interpretations, model fit, diagnostics, and how to convey results graphically are provided. There is a focus on graphic displays of results as these are a core strength of using R for statistical analysis. Many in the social sciences are transitioning away from using Stata, SPSS and SAS, to using R, and this book uses statistical models which are relevant to the social sciences. Social Science Applications of Regression for Categorical Outcomes Using R will be useful for graduate students in the social sciences who are looking to expand their statistical knowledge, and for Quantitative social scientists due to it's ability to act as a practitioners guide.

Key Features:

  • Applied- in the sense that we will provide code that others can easily adapt

  • Flexible- R is basically just a fancy

    Table of Contents

    1. Introduction 2. Introduction to R Studio and Packages 3. Overview of OLS Regression and Introduction to the General Linear Model 4. Describing Categorical Variables and Some Useful Tests of Association 5. Regression for Binary Outcomes 6. Regression for Binary Outcomes – Moderation and Squared Terms 7. Regression for Ordinal Outcomes 8. Regression for Nominal Outcomes 9. Regression for Count Outcomes 10. Additional Outcome Types 11. Special Topics: Comparing Between Models and Missing Data

Applications of Regression for Categorical

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£139.50

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RRP £155.00 – you save £15.50 (10%)

Order before 4pm today for delivery by Sat 13 Dec 2025.

A Hardback by David Melamed, Long Doan

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    View other formats and editions of Applications of Regression for Categorical by David Melamed

    Publisher: Taylor & Francis Ltd
    Publication Date: 7/26/2023 12:00:00 AM
    ISBN13: 9780367894634, 978-0367894634
    ISBN10: 0367894637
    Also in:
    Mathematics Probability and statistics Psychological methodology Psychological theory

    Description

    Book Synopsis

    This book covers the main models within the GLM (i.e., logistic, Poisson, negative binomial, ordinal, and multinomial). For each model, estimations, interpretations, model fit, diagnostics, and how to convey results graphically are provided. There is a focus on graphic displays of results as these are a core strength of using R for statistical analysis. Many in the social sciences are transitioning away from using Stata, SPSS and SAS, to using R, and this book uses statistical models which are relevant to the social sciences. Social Science Applications of Regression for Categorical Outcomes Using R will be useful for graduate students in the social sciences who are looking to expand their statistical knowledge, and for Quantitative social scientists due to it's ability to act as a practitioners guide.

    Key Features:

    • Applied- in the sense that we will provide code that others can easily adapt

    • Flexible- R is basically just a fancy

      Table of Contents

      1. Introduction 2. Introduction to R Studio and Packages 3. Overview of OLS Regression and Introduction to the General Linear Model 4. Describing Categorical Variables and Some Useful Tests of Association 5. Regression for Binary Outcomes 6. Regression for Binary Outcomes – Moderation and Squared Terms 7. Regression for Ordinal Outcomes 8. Regression for Nominal Outcomes 9. Regression for Count Outcomes 10. Additional Outcome Types 11. Special Topics: Comparing Between Models and Missing Data

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