Bergtold, Jason S.Spanos, ArisOnukwugha, Eberechukwu2022-07-142022-07-142010-01-01https://hdl.handle.net/2097/42353The 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.Elsevier user license: Articles published under an Elsevier user license are protected by copyright. Users may access, download, copy, translate, text and data mine (but may not redistribute, display or adapt) the articles for non-commercial purposes provided that users abide by the terms of the license.https://www.elsevier.com/about/policies/open-access-licenses/elsevier-user-licenseBernoulli Regression ModelGeneralized Linear ModelsLatent Variable ModelsLogistic RegressionModel SpecificationProbabilistic Reduction ApproachText