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Linear Probability, Logit, and Probit Models (Quantitative Applications in the Social Sciences) - Softcover
Book 35 of 194: Quantitative Applications in the Social Sciences
Synopsis
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.
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About the Authors
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
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Linear Probability Logit & Probit Models
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Linear Probability, Logit, and Probit Models
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Linear Probability, Logit and Probit Models
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Linear Probability, Logit, and Probit Models Sage University Paper, 45
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Linear Probability, Logit, and Probit Models
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Linear Probability, Logit, and Probit Models (Quantitative Applications in the Social Sciences)
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Linear Probability, Logit, and Probit Models (Quantitative Applications in the Social Sciences, No. 45) (Volume 45)
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