Interpreting and Comparing Effects in Logistic, Probit, and Logit Regression: (Quantitative Applications in the Social Sciences)

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.

Table of Contents:
Chapter 1. Introduction Purpise Content Causality Chapter 2. Regression Models for A Dichotomous Dependent Variable Introduction Discrete Response Model — DRM Latent Variable Model — LVM Inserting Mavericks, “Orthogonal” Independent Variables, Into Equations Chapter 3. Interpreting And Comparing Effects Within One Equation Comparing Effects Within a Single LVM Equation Comparing Effects Within a Single DRM Equation Causal Interpretations in LVM and DRM Logistic Regression Chapter 4. Comparing Subgroups Or Time Points: Investigating Interaction Effects Interaction Effects in LVM Interaction Effects in DRM Interaction and Causal Analysis Chapter 5. Causal Modeling: Estimating Total, Direct, Indirect And Spurious Effects; Using Effect Coefficients From Different (Nested) Equations Introduction LVM DRM Casual Modeling Chapter 6. Concluding Remarks; Extensions, Effect Measures And Evaluation Polytomous Dependent Variable How to Measure Effects in Logistic Regression Concluding Remarks

Review :
This book has very clear, pristine explanations of topics such as how DRMs work, great numerical methods for maximizing and specifying, and helpful explanatory tests and interpretative effects, all written at an intermediate level. The discussion of various ways of interpreting coefficients in each of the models is the most useful part of the text. While many other texts touch on the difficulties of interpreting coefficients and perhaps offer an approach or two, the authors of this volume thoroughly review multiple approaches common and unique to each of the models. This book has a well-organized structure and includes coverage of useful information and skills in the logistic regression. Scholars can apply these models to their own research projects.