Interpreting and Comparing Effects in Logistic, Probit, and Logit Regression
eBook - ePub

Interpreting and Comparing Effects in Logistic, Probit, and Logit Regression

  1. English
  2. ePUB (mobile friendly)
  3. Available on iOS & Android
eBook - ePub

Interpreting and Comparing Effects in Logistic, Probit, and Logit Regression

About this book

Log-linear, logit and logistic regression models are the most common ways of analyzing data when (at least) the dependent variable is categorical. This volume shows how to compare coefficient estimates from regression models for categorical dependent variables in three typical research situations: (i) within one equation, (ii) between identical equations estimated in different subgroups, and (iii) between nested equations. Each of these three kinds of comparisons brings along its own particular form of comparison problems. Further, in all three areas, the precise nature of comparison problems in logistic regression depends on how the logistic regression model is looked at and how the effects of the independent variables are computed. This volume presents a practical, unified treatment of these problems, and considers the advantages and disadvantages of each approach, and when to use them, so that applied researchers can make the best choice related to their research problem. The techniques are illustrated with data from simulation experiments and from publicly available surveys. The datasets, along with Stata syntax, are available on a companion website.

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Yes, you can access Interpreting and Comparing Effects in Logistic, Probit, and Logit Regression by Jacques A. P. Hagenaars,Steffen Kühnel,Hans-Jürgen Andress in PDF and/or ePUB format, as well as other popular books in Social Sciences & Social Science Research & Methodology. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Praise for this Book
  3. Quantitative Applications in the Social Sciences
  4. Series: Quantitative Applications in the Social Sciences
  5. Half Title
  6. Acknowledgements
  7. Publisher Note
  8. Title Page
  9. Copyright Page
  10. Brief Contents
  11. Detailed Contents
  12. Series Editor Introduction
  13. Preface and Acknowledgements
  14. About the Authors
  15. 1 Introduction
  16. 2 Regression Models for a Dichotomous Dependent Variable
  17. 3 Interpreting and Comparing Effects Within One Equation
  18. 4 Comparing Subgroups or Time Points: Investigating Interaction Effects
  19. 5 Causal Modeling: Estimating Total, Direct, Indirect and Spurious Effects; Using Effect Coefficients From Different (Nested) Equations
  20. 6 Concluding Remarks; Extensions, Effect Measures, and Evaluation
  21. References
  22. Index