Linear Probability Logit & Probit Models

1. Auflage 1984
ISBN: 978-0-8039-2133-7
Verlag: Sage Publications

Buch, Englisch, Band 45, 96 Seiten, Format (B × H): 153 mm x 203 mm, Gewicht: 118 g

Reihe: Quantitative Applications in the Social Sciences

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.

Autoren/Hrsg.

Aldrich, John

Nelson, Forrest D

Fachgebiete

• Sozialwissenschaften Soziologie | Soziale Arbeit Soziologie Allgemein Demographie, Demoskopie

Inhaltsverzeichnis

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

Über Autor(innen)

Aldrich, John
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.

Nelson, Forrest D
Expertise

* Prediction Markets
* Qualitative and Limited Dependent Variable