
Linear Probability, Logit, and Probit Models
SAGE Publications Inc (Publisher)
1st Edition
Published on 21. February 1985
Book
Paperback/Softback
96 pages
978-0-8039-2133-7 (ISBN)
Description
After showing why ordinary regression analysis is not appropriate in investigating dichotomous or otherwise "limited" dependent variables, this volume examines three techniques-linear probability, probit, and logit models-well-suited for such data. It reviews the linear probability model and discusses alternative specifications of nonlinear models.
More details
Series
Language
English
Place of publication
Thousand Oaks
United States
Target group
College/higher education
Dimensions
Height: 216 mm
Width: 140 mm
Weight
142 gr
ISBN-13
978-0-8039-2133-7 (9780803921337)
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Schweitzer Classification
Persons
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.
Expertise
* Prediction Markets
* Qualitative and Limited Dependent Variable
Expertise
* Prediction Markets
* Qualitative and Limited Dependent Variable
Content
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
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