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



May 2024 | 208 pages | SAGE Publications, Inc
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 at: https://study.sagepub.com/researchmethods/qass/hagenaars-interpreting-effects.
 
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

 

Supplements

Student Study Site
Datasets and Stata scripts are available on this accompanying student website so that readers can replicate the examples used in the book.

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.

Kara Sutton
Southern Methodist University

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.

Jingshun Zhang
Florida Gulf Coast University