Abstract
In Configural Frequency Analysis (CFA), model-data discrepancies are interpreted with reference to CFA base models. Thus far, CFA base models are defined as probability models that differ in the constraints they place on variable relations. In this article, it is proposed extending the scope of CFA base models. Specifically, it is proposed that the specification of base models is conducted with reference to data generation processes (DGPs). These processes result in uni- or multivariate distributions that reflect variable relations, probability distributions, or processes that result in change in series of observations. The new, DGP-based definition of CFA base models retains the concept of unique interpretability of CFA results, but it opens the doors to many more forms of base models than considered before. Four classes of base models are defined. Data examples illustrate various CFA base models.