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Linear Probability, Logit, and Probit Models
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Linear Probability, Logit, and Probit Models

Volume: 45
November 1984 | 96 pages | SAGE Publications, Inc
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Ordinary regression analysis is not appropriate for investigating dichotomous or otherwise `limited' dependent variables, but this volume examines three techniques -- linear probability, probit, and logit models -- which are well-suited for such data. It reviews the linear probability model and discusses alternative specifications of non-linear models. Using detailed examples, Aldrich and Nelson point out the differences among linear, logit, and probit models, and explain the assumptions associated with each.
 
The Linear Probability Model
 
Specification of Nonlinear Probability Models
 
Estimation of Probit and Logit Models for Dichotomous Dependent Variables
 
Minimum Chi-Square Estimation and Polytomous Models Summary and Extensions

John H. Aldrich

John H. Aldrich is Pfizer-Pratt University Professor of Political Science at Duke University. He is author of Why Parties: A Second Look (2011), coeditor of Positive Changes in Political Science (2007), and author of Why Parties (1995) and Before the Convention (1980). He is a past president of both the Southern Political Science Association and the Midwest Political Science Association and is serving as president of the American Political Science Association. In 2001 he was elected a fellow in the American Academy of Arts and Sciences. More About Author

Forrest D. Nelson

Expertise * Prediction Markets * Qualitative and Limited Dependent Variable More About Author

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ISBN: 9780803921337
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