Missing data are hard to avoid, or even inevitable, in longitudinal learning diagnosis and
other longitudinal studies. Sample attrition is one of the most common missing patterns in …

Despite the increasing popularity, cognitive diagnosis models have been criticized for
limited utility for small samples. In this study, the authors proposed to use Bayes modal (BM) …

Respondents' problem-solving behaviors comprise behaviors that represent complicated
cognitive processes that are frequently systematically tied to one another. Biometric data …

Timely diagnostic feedback is helpful for students and teachers, enabling them to adjust their
learning and teaching plans according to a current diagnosis. Motivated by the practical …

Limited-information fit measures appear to be promising in assessing the goodness-of-fit of
dichotomous response cognitive diagnosis models (CDMs), but their performance has not …

This article proposes a class of cognitive diagnosis models (CDMs) for polytomously scored
items with different link functions. Many existing polytomous CDMs can be considered as …

The purpose of this study is to develop and evaluate two diagnostic classification models
(DCMs) for scoring ordinal item data. We first applied the proposed models to an operational …

In cognitive diagnosis models, the condensation rule describes the logical relationship
between the required attributes and the item response, reflecting an explicit assumption …

The expectation-maximization (EM) algorithm is a commonly used technique for the
parameter estimation of the diagnostic classification models (DCMs) with a prespecified Q …

Accurate estimation of flood frequency is an important task for water resources management.
This starts with appropriate selection of probability distribution to flood samples (annual …