Interpreting Probability Models: Logit, Probit and Other Generalized Linear Models (Quantitative Applications in the Social Sciences No. 101)
By: Tim F. Liao (author)Paperback
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What is the probability that something will occur, and how is that probability altered by a change in an independent variable? To answer these questions, Tim Futing Liao introduces a systematic way of interpreting commonly used probability models. Since much of what social scientists study is measured in noncontinuous ways and, therefore, cannot be analyzed using a classical regression model, it becomes necessary to model the likelihood that an event will occur. This book explores these models first by reviewing each probability model and then by presenting a systematic way for interpreting the results from each.
Tim Liao is Professor of Sociology & Statistics. His research interests include historical/comparative sociology, demography, and methodology. He is a former Deputy Editor of The Sociological Quarterly, (1992-2000) and the current Editor of Sage's Quantitative Applications in the Social Sciences series. He served on the council of the ASA Methodology Section (1998-2001) and on the council of the North America Chinese Sociological Association (2000-2002). He has been on the editorial board of Sociological Methods & Research since 1994 and on the editorial board of Sociological Methodology since 2003. He is the Chair-Elect of the Methodology Section of the American Sociology Association from August 2007 to August 2009.
Introduction Generalized Linear Models and the Interpretation of Parameters Binary Logit and Probit Models Sequential Logit and Probit Models Ordinal Logit and Probit Models Multinomial Logit Models Conditional Logit Models Poisson Regression Models Conclusion
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