Linear Probability, Logit, and Probit Models

Aldrich, John H. & Forrest D. Nelson

ISBN 10: 0803921330 / ISBN 13: 9780803921337

Editore: SAGE Publications, Newbury Park, 1984

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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. Codice articolo 056390

Dati bibliografici

Titolo: Linear Probability, Logit, and Probit Models

Casa editrice: SAGE Publications, Newbury Park

Data di pubblicazione: 1984

Legatura: Trade Paperback

Condizione: Very Good

Condizione sovraccoperta: wraps

Edizione: First

Descrizione articolo

Riassunto:

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.

L'autore:

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.