Linear Probability, Logit, and Probit Models (Quantitative Applications in the Social Sciences)
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Linear Probability, Logit, and Probit Models (Quantitative Applications in the Social Sciences)
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.
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