Linear Probability, Logit and Probit Models

Aldrich, John H.; Nelson, Forrest D.

ISBN 10: 0803921330 / ISBN 13: 9780803921337

Published by Sage, Thousand Oaks, California, U.S.A., 1984

About this Item

Like new. 95 pages. Size: 5 1/2" x 8 1/2".

Seller Inventory # S246-055894

Bibliographic Details

• Title: Linear Probability, Logit and Probit Models
• Publisher: Sage, Thousand Oaks, California, U.S.A.
• Publication Date: 1984
• Language: English
• ISBN 10: 0803921330
• ISBN 13: 9780803921337
• Binding: Paperback
• Condition: Very Good

About this title

Synopsis

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.

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