Likelihood‐Based Estimation of Model‐Derived Oral Reading Fluency

Abstract

AbstractAs part of the effort to develop an improved oral reading fluency (ORF) assessment system, Kara et al. estimated the ORF scores based on a latent variable psychometric model of accuracy and speed for ORF data via a fully Bayesian approach. This study further investigates likelihood‐based estimators for the model‐derived ORF scores, including maximum likelihood estimator (MLE), maximum a posteriori (MAP), and expected a posteriori (EAP), as well as their standard errors. The proposed estimators were demonstrated with a real ORF assessment dataset. Also, the estimation of model‐derived ORF scores and their standard errors by the proposed estimators were evaluated through a simulation study. The fully Bayesian approach was included as a comparison in the real data analysis and the simulation study. Results demonstrated that the three likelihood‐based approaches for the model‐derived ORF scores and their standard error estimation performed satisfactorily.