Inference of Correlations Among Testlet Effects: A Latent Variable Selection Method.

ABSTRACT: In psychological and educational measurement, a testlet-based test is a common and popular format, especially in some large-scale assessments. In modeling testlet effects, a standard bifactor model, as a common strategy, assumes different testlet effects and the main effect to be fully independently distributed. However, it is difficult to establish perfectly independent clusters as this assumption. To address this issue, correlations among testlets could be taken into account in fitting data. Moreover, one may desire to maintain a good practical interpretation of the sparse loading matrix. In this paper, we propose data-driven learning of significant correlations in the covariance matrix through a latent variable selection method. Under the proposed method, a regularization is performed o

SUBMITTER: Xu X 

PROVIDER: S-EPMC11670239 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature