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

Format:

Book

Author/Creator:

Hagenaars, Jacques A. P.;Kuhnel, Steffen;Andress, Hans-Jurgen

Language:

English

Subjects (All):

Logistic regression analysis.

Edition:

1st ed.

Place of Publication:

Thousand Oaks, California: SAGE Publications, Incorporated 2025

Summary:

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.

Contents:

Cover

PRAISE FOR THIS BOOK

QUANTITATIVE APPLICATIONS IN THE SOCIAL SCIENCES

SERIES: QUANTITATIVE APPLICATIONS IN THE SOCIAL SCIENCES

Half title

Dedication

Title Page

Brief Contents

Detailed Contents

Series Editor Introduction

Preface and Acknowledgements

About the Authors

1 - Introduction

1.1 Purpose

1.2 Content

1.3 Causality

2 - Regression Models for a Dichotomous Dependent Variable

2.1 Introduction

Simulated Data Set University

2.2 Discrete Response Model — DRM

2.2.1 Logistic Regression, Response Profiles, Discrete (​DC​), and Instantaneous (​IC​) Change Measures

2.2.2 Logistic DRM as a Logit Model: Odds Ratios as Effect Measures

2.2.3 Probit Regression

2.2.4 Linear Probability Model – LPM

2.3 Latent Variable Model — LVM

2.3.1 Logistic Latent Variable Model

Underlying Standardized Effects and Explained Variance of ​​Y​​ *​​

2.3.2 Probit Latent Variable Model

2.3.3 Heteroscedastic Errors, Unequal Thresholds, and Biased Effects

2.4 Inserting Mavericks, “Orthogonal” Independent Variables, Into Equations Generated by AI.

Notes:

Part of the metadata in this record was created by AI, based on the text of the resource.

ISBN:

1-5443-6402-4

1-5443-6399-0

1-5443-6400-8

9781544364025