No abstract

No abstract

Latent class models have been developed for assessment of hierarchic relations in scaling and behavioral analysis. This article investigated the use of three model selection information criteria—Akaike AIC, Schwarz SIC, and Bozdogan CAIC—for non-nested models. In general, SIC and CAIC were superior to AIC for relatively simple models, whereas AIC was superior for more complex models, although accuracy was often quite low for such models. In addition, some effects were detected for error rates in the models.

Descriptions are presented of two related probabilistic models that can be used for making classification decisions with respect to mastery of specific concepts or skills. Included are the development of procedures for: (a) assessing the adequacy of “fit” provided by the models; (b) identifying optimal decision rules for mastery classification; and (c) identifying minimally sufficient numbers of items necessary to obtain acceptable levels of misclassification.

No abstract