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

dc.contributor.authorMa, Wenchao
dc.contributor.authorJiang, Zhehan
dc.contributor.otherUniversity of Alabama Tuscaloosa
dc.contributor.otherPeking University
dc.date.accessioned2023-09-28T19:34:00Z
dc.date.available2023-09-28T19:34:00Z
dc.date.issued2021
dc.description.abstractDespite 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.en_US
dc.format.mediumelectronic
dc.format.mimetypeapplication/pdf
dc.identifier.citationMa, 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
dc.identifier.doi10.1177/0146621620977681
dc.identifier.orcidhttps://orcid.org/0000-0002-6763-0707
dc.identifier.orcidhttps://orcid.org/0000-0002-1376-9439
dc.identifier.urihttps://ir.ua.edu/handle/123456789/11440
dc.languageEnglish
dc.language.isoen_US
dc.publisherSage
dc.subjectcognitive diagnosis
dc.subjectdiagnostic classification
dc.subjectEM algorithm
dc.subjectBayes modal
dc.subjectmonotonic constraints
dc.subjectG-DINA
dc.subjectGENERALIZED DINA MODEL
dc.subjectHIDDEN MARKOV MODEL
dc.subjectCLASSIFICATION MODELS
dc.subjectEFFECT SIZE
dc.subjectSTATISTICS
dc.subjectORDER
dc.subjectFIT
dc.subjectSocial Sciences, Mathematical Methods
dc.subjectPsychology, Mathematical
dc.titleEstimating Cognitive Diagnosis Models in Small Samples: Bayes Modal Estimation and Monotonic Constraintsen_US
dc.typeArticle
dc.typetext
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