An Improved Parameter-Estimating Method in Bayesian Networks Applied for Cognitive Diagnosis Assessment.

Bayesian networks (BNs) can be employed to cognitive diagnostic assessment (CDA). Most of the existing researches on the BNs for CDA utilized the MCMC algorithm to estimate parameters of BNs. When EM algorithm and gradient descending (GD) learning method are adopted to estimate the parameters of BNs, some challenges may emerge in educational assessment due to the monotonic constraints (greater skill should lead to better item performance) cannot be satisfied in the above two methods.

Structural Parameter Standard Error Estimation Method in Diagnostic Classification Models: Estimation and Application.

The information matrix or its inverse variance-covariance matrix for the maximum likelihood estimates of model parameters in diagnostic classification models plays a key role in statistical inference. Although both the item and structural parameters should be contained in the calculation of the information matrix simultaneously, previous studies have mainly focused on performance of the item parameter standard error (), no study has investigated the structural parameter estimation methods systematically. In this study, we propose a class of structural parameter estimation methods based on the empirical cross-product matrix, the observed information matrix, and the sandwich-type covariance matrix.

The Impact of Item Calibration Error on Variable-Length Cognitive Diagnostic Computerized Adaptive Testing.

Calibration errors are inevitable and should not be ignored during the estimation of item parameters. Items with calibration error can affect the measurement results of tests. One of the purposes of the current study is to investigate the impacts of the calibration errors during the estimation of item parameters on the measurement accuracy, average test length, and test efficiency for variable-length cognitive diagnostic computerized adaptive testing.

Improved Wald Statistics for Item-Level Model Comparison in Diagnostic Classification Models.

Diagnostic classification models (DCMs) have been widely used in education, psychology, and many other disciplines. To select the most appropriate DCM for each item, the Wald test has been recommended. However, prior research has revealed that this test provides inflated Type I error rates.

A Comparison of Differential Item Functioning Detection Methods in Cognitive Diagnostic Models.

As a class of discrete latent variable models, cognitive diagnostic models have been widely researched in education, psychology, and many other disciplines. Detecting and eliminating differential item functioning (DIF) items from cognitive diagnostic tests is of great importance for test fairness and validity. A Monte Carlo study with varying manipulated factors was carried out to investigate the performance of the Mantel-Haenszel (MH), logistic regression (LR), and Wald tests based on item-wise information, cross-product information, observed information, and sandwich-type covariance matrices (denoted by , , , and , respectively) for DIF detection.

Applying the Statistic to Evaluate the Fit of Diagnostic Classification Models in the Presence of Attribute Hierarchies.

The performance of the limited-information statistic for diagnostic classification models (DCMs) is under-investigated in the current literature. Specifically, the investigations of for specific DCMs rather than general modeling frameworks are needed. This article aims to demonstrate the usefulness of in hierarchical diagnostic classification models (HDCMs).

Information matrix estimation procedures for cognitive diagnostic models.

Two new methods to estimate the asymptotic covariance matrix for marginal maximum likelihood estimation of cognitive diagnosis models (CDMs), the inverse of the observed information matrix and the sandwich-type estimator, are introduced. Unlike several previous covariance matrix estimators, the new methods take into account both the item and structural parameters. The relationships between the observed information matrix, the empirical cross-product information matrix, the sandwich-type covariance matrix and the two approaches proposed by de la Torre (2009, J.