Estimating Cognitive Diagnosis Models in Small Samples: Bayes Modal Estimation and Monotonic Constraints

Loading...
Thumbnail Image
Date
2021
Journal Title
Journal ISSN
Volume Title
Publisher
Sage
Abstract

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) estimation and monotonic constraints to stabilize item parameter estimation and facilitate person classification in small samples based on the generalized deterministic input noisy "and" gate (G-DINA) model. Both simulation study and real data analysis were used to assess the utility of the BM estimation and monotonic constraints. Results showed that in small samples, (a) the G-DINA model with BM estimation is more likely to converge successfully, (b) when prior distributions are specified reasonably, and monotonicity is not violated, the BM estimation with monotonicity tends to produce more stable item parameter estimates and more accurate person classification, and (c) the G-DINA model using the BM estimation with monotonicity is less likely to overfit the data and shows higher predictive power.

Description
Keywords
cognitive diagnosis, diagnostic classification, EM algorithm, Bayes modal, monotonic constraints, G-DINA, GENERALIZED DINA MODEL, HIDDEN MARKOV MODEL, CLASSIFICATION MODELS, EFFECT SIZE, STATISTICS, ORDER, FIT, Social Sciences, Mathematical Methods, Psychology, Mathematical
Citation
Ma, W., & Jiang, Z. (2020). Estimating Cognitive Diagnosis Models in Small Samples: Bayes Modal Estimation and Monotonic Constraints. In Applied Psychological Measurement (Vol. 45, Issue 2, pp. 95–111). SAGE Publications. https://doi.org/10.1177/0146621620977681