Sage University Paper in the series: Quanitative Applications in the Social Sciences. Series editors: John L. Sullivan, University of Minnesota and Richard G. Niemi, University of Rochester.

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T�tulo: Linear Probability, Logit, and Probit Models
Editor: SAGE Publications, Newbury Park
Fecha de publicaci�n: 1984
Idioma: Ingl�s
ISBN 10: 0803921330
ISBN 13: 9780803921337
Encuadernaci�n: Trade Paperback
Edici�n: First
Estado: Very Good
Estado de la sobrecubierta: wraps

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Sinopsis

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.

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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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