Bergtold, Jason S.Spanos, ArisOnukwugha, Eberechukwu2024-02-202024-02-2020101755-5345https://hdl.handle.net/10919/118069The latent variable and generalized linear modelling approaches do not provide a systematic approach for modelling discrete choice observational data. Another alternative, the probabilistic reduction (PR) approach, provides a systematic way to specify such models that can yield reliable statistical and substantive inferences. The purpose of this paper is to re-examine the underlying probabilistic foundations of conditional statistical models with binary dependent variables using the PR approach. This leads to the development of the Bernoulli Regression Model, a family of statistical models, which includes the binary logistic regression model. The paper provides an explicit presentation of probabilistic model assumptions, guidance on model specification and estimation, and empirical application.Pages 1-2828 page(s)application/pdfenCreative Commons Attribution-NonCommercial 2.0 UKBernoulli Regression ModelGeneralized Linear ModelsLatent Variable ModelsLogistic RegressionModel SpecificationProbabilistic Reduction ApproachArticle - Refereedhttps://doi.org/10.1016/S1755-5345(13)70033-232