The authors of this article developed new approaches to present analytic results from mixed-effects binary logit models in longitudinal data analysis. We first described basic specifications of mixed-effects logit models, the derivation of the fixed and the random effects, and nonlinear predictions of the response probability and the corresponding standard errors. Particular attention was paid ...

Categorical outcome (or discrete outcome or qualitative response) regression models are models for a discrete dependent variable recording in which of two or more categories an outcome of interest lies. For binary data (two categories) probit and logit models or semiparametric methods are used. For multinomial data (more than two categories) that are unordered, common models are multinomial and...

The spatial model has been in use in political science for close to 30 years, and in that period it has achieved a place of prime importance as our paradigm of the process of candidatechoice used by voters. For much of this time political scientists have estimated models of candidatechoice using binary logit or probit, even in cases where there were more than two choices facing voters. Recently...

longitudinal  study  plays  an  important  role  in  the  epidemiological,  clinical,  and  social  science studies. in these kinds of studies, every individual is observed frequently during a period of time. the statistical analysis of longitudinal presents special opportunities  and challenges. the repeated outcomes for one individual tend to be correlated among themselves also one of the pro...

Current opinion regarding the selection of link function in binary response models is that the probit and logit links give essentially similar results. This seems to be true for univariate binary response models; however, for multivariate binary response models such advice is misleading. We address a gap in the literature by empirically examining the relationship between link function selection...

We show that the distributions of random coefficients in various discrete choice models are nonparametrically identified. Our identification results apply to static discrete choice models including binary logit, multinomial logit, nested logit, and probit models as well as to dynamic programming discrete choice models. In these models the only key condition we need to verify for identification ...