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Interpreting Probability Models
Logit, Probit, and Other Generalized Linear Models
Volume:
101
August 1994 | 96 pages | SAGE Publications, Inc
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.
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
