Considerations for Fitting Dynamic Bayesian Networks With Latent Variables: A Monte Carlo Study.

ABSTRACT: Dynamic Bayesian networks (DBNs; Reye, 2004) are a promising tool for modeling student proficiency under rich measurement scenarios (Reichenberg, 2018). These scenarios often present assessment conditions far more complex than what is seen with more traditional assessments and require assessment arguments and psychometric models capable of integrating those complexities. Unfortunately, DBNs remain understudied and their psychometric properties relatively unknown. The current work aimed at exploring the properties of DBNs under a variety of realistic psychometric conditions. A Monte Carlo simulation study was conducted in order to evaluate parameter recovery for DBNs using maximum likelihood estimation. Manipulated factors included sample size, measurement quality, test length, the number o

SUBMITTER: Reichenberg RE 

PROVIDER: S-EPMC8908410 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature