Del 194 - Quantitative Applications in the Social Sciences

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

Häftad, Engelska, 2024

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

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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.

Produktinformation

Utgivningsdatum2024-06-11
Mått139 x 215 x 14 mm
Vikt250 g
FormatHäftad
SpråkEngelska
SerieQuantitative Applications in the Social Sciences
Antal sidor208
Upplaga1
FörlagSAGE Publications
ISBN9781544364018

Tillhör följande kategorier

Chapter 1. IntroductionPurpiseContentCausalityChapter 2. Regression Models for A Dichotomous Dependent VariableIntroductionDiscrete Response Model — DRMLatent Variable Model — LVMInserting Mavericks, “Orthogonal” Independent Variables, Into EquationsChapter 3. Interpreting And Comparing Effects Within One EquationComparing Effects Within a Single LVM EquationComparing Effects Within a Single DRM EquationCausal Interpretations in LVM and DRM Logistic RegressionChapter 4. Comparing Subgroups Or Time Points: Investigating Interaction EffectsInteraction Effects in LVMInteraction Effects in DRMInteraction and Causal AnalysisChapter 5. Causal Modeling: Estimating Total, Direct, Indirect And Spurious Effects; Using Effect Coefficients From Different (Nested) EquationsIntroductionLVMDRMCasual ModelingChapter 6. Concluding Remarks; Extensions, Effect Measures And EvaluationPolytomous Dependent VariableHow to Measure Effects in Logistic RegressionConcluding Remarks

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.