Article

Generic Identifiability of the DINA Model and Blessing of Latent Dependence

2022-04-26

Abstract excerpt

<p>Cognitive Diagnostic Models are a powerful family of fine-grained discrete latent variable models in psychometrics. Within this family, the DINA model is a fundamental and parsimonious one that has received significant attention. Similar to other complex latent variable models, identifiability is an important issue for CDMs, including the DINA model. Gu and Xu (Psychometrika, 84(2):468-483, 2019) established th...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: da50f0e2-10eb-517a-8426-1914d983a06d
DOI: 10.31234/osf.io/qfkxb

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Generic Identifiability of the DINA Model and Blessing of Latent DependenceDOI 10.31234/osf.io/qfkxbRead

A novel generalized model framework of diagnostic classification models for multiple-choice itemsSemantic proximity Read

A Sequential General Nonparametric Classification Method for Polytomous ResponsesSemantic proximity Read

A comparison of the predictive performance of continuous and class-based latent trait modelsSemantic proximity Read

Generalized Bayesian Method for Diagnostic Classification ModelsSemantic proximity Read

Every Trait Counts: Marginal Maximum Likelihood Estimation for Novel Multidimensional Count Data Item Response Models with Rotation or l1-Regularization for Simple StructureSemantic proximity Read

A reusable Bayesian IRT workflow for instrument validation: A tutorial with a small-sample case study in teacher belief measurementSemantic proximity Read

Understanding Ability and Reliability Differences Measured with Count Items: The Distributional Regression Test Model and the Count Latent Regression ModelSemantic proximity Read

Enhancing Neural Cognitive Diagnosis: Q-Matrix Recovery, Task Distraction Sensitivity, and Dynamic Attention Modeling from Synthetic DataSemantic proximity Read

Driving Factors of Individual Differences in Broad Retrieval Ability: Gr Is More Than the Sum of Its PartsSemantic proximity Read