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

Volume: 45
February 1985 | 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

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