Bayesian modeling of item heterogeneity in dichotomous recognition memory data and prospects for computerized adaptive testing.

ABSTRACT: Most current models of recognition memory fail to separately model item and person heterogeneity which makes it difficult to assess ability at the latent construct level and prevents the administration of adaptive tests. Here we propose to employ a General Condorcet Model for Recognition (GCMR) in order to estimate ability, response bias and item difficulty in dichotomous recognition memory tasks. Using a Bayesian modeling framework and MCMC inference, we perform 3 separate validation studies comparing GCMR to the Rasch model from IRT and the 2-High-Threshold (2HT) recognition model. First, two simulations demonstrate that recovery of GCMR ability estimates with varying sparsity and test difficulty is more robust and that estimates improve from the two other models under common test scenar

SUBMITTER: Gusten J 

PROVIDER: S-EPMC8786965 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature