After more then 50 years of probabilistic choice modeling in economics, marketing, political science, psychology, and related disciplines, theoretical and computational advances give scholars access to a sophisticated array of modeling and inference resources. We review some important, but perhaps often overlooked, properties of major classes of probabilistic choice models. For within-respondent applications, we discuss which models require repeated choices by an individual to be independent and response probabilities to be stationary. We show how some model classes, but not others, are invariant over variable preferences, variable utilities, or variable choice probabilities. These models, but not others, accommodate pooling of responses or averaging of choice proportions within participant when underlying parameters vary across observations. These, but not others, permit pooling/averaging across respondents in the presence of individual differences. We also review the role of independence and stationarity in statistical inference, including for probabilistic choice models that, themselves, do not require those properties. Copyright © 2017 John Wiley & Sons, Ltd.Additional Supporting Information may be found online in the supporting information tab for this article.key words probabilistic choice models; iid assumptions; modeling heterogeneity; statistical testingWhen selecting a probabilistic choice model to use in their work, scholars may want to consider which model class makes what assumptions and which model class warrants or precludes what types of data manipulations. In this conceptual and theoretical synthesis, we summarize, for several prominent probabilistic choice models, whether or not they assume mutually independent (in the sense of probability theory) responses and/or stationary response probabilities. 1 We also unpack whether various models are invariant under varying preferences, varying utility functions, or varying response probabilities, either because of individual differences or because of within-subject heterogeneity in behavior. We discuss what it means to average or pool binary choice data across respondents and/or across time points. To that end, we spell out, for some prominent statistical tests of probabilistic choice models, whether they, in turn, make additional independence and/or stationarity assumptions even when the models themselves do not require such properties to hold. We concentrate on those models, statistical tests, and data-generating processes whose basic observational unit-one observation-is one person's selection of one choice alternative among two, at one moment in time. Knowing a model's interconnected assumptions helps the applied scholar better understand their role in empirical evaluations. In particular, when a model performs or replicates poorly, knowing all assumptions is crucial for revising the theory in question.
Motivation and backgroundConsider the following two questions posed in an experiment. We use these questions for various illustrations thr...