Interpreting And Comparing Effects In Logistic Probit And Logit Regression
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Interpreting and Comparing Effects in Logistic, Probit, and Logit Regression
Author: Jacques A. P. Hagenaars
language: en
Publisher: SAGE Publications
Release Date: 2024-01-16
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.
Interpreting and Comparing Effects in Logistic, Probit and Logit Regression
Author: Jacques A P Hagenaars
language: en
Publisher: Sage Publications, Incorporated
Release Date: 2024-03-05
Interpreting Effects in Logistic Regression and Logit Models shows how to compare coefficient estimates from regression models for categorical dependent variables in three typical research situations: (i) within one model, (ii) between identical models estimated in different subgroups, and (iii) between nested models. Additionally, this volume presents a practical, unified treatment of comparison problems and considers the advantages and disadvantages of each approach and when to use them.
Linear Probability, Logit, and Probit Models
After showing why ordinary regression analysis is not appropriate for investigating dichotomous or otherwise 'limited' dependent variables, this volume examines three techniques which are well suited for such data. It reviews the linear probability model and discusses alternative specifications of non-linear models.