Likelihood-Based Estimation of Model-Derived Oral Reading Fluency

IF 1.4 4区 心理学 Q3 PSYCHOLOGY, APPLIED Journal of Educational Measurement Pub Date : 2024-06-22 DOI:10.1111/jedm.12404
Cornelis Potgieter, Xin Qiao, Akihito Kamata, Yusuf Kara
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Abstract

As 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.

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基于似然法估计模型得出的口语阅读流利度
作为开发改进型口语阅读流利度(ORF)评估系统工作的一部分,Kara 等人通过完全贝叶斯方法,根据口语阅读流利度数据的准确性和速度的潜在变量心理测量模型估算了 ORF 分数。本研究进一步研究了基于似然估计法的 ORF 分数模型,包括最大似然估计法(MLE)、最大后验法(MAP)和预期后验法(EAP)及其标准误差。利用真实的 ORF 评估数据集演示了所提出的估计方法。此外,还通过模拟研究评估了模型衍生 ORF 分数的估计值及其标准误差。在真实数据分析和模拟研究中,将完全贝叶斯方法作为比较对象。结果表明,这三种基于似然法的模型衍生 ORF 分数及其标准误差估计方法的性能令人满意。
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来源期刊
CiteScore
2.30
自引率
7.70%
发文量
46
期刊介绍: The Journal of Educational Measurement (JEM) publishes original measurement research, provides reviews of measurement publications, and reports on innovative measurement applications. The topics addressed will interest those concerned with the practice of measurement in field settings, as well as be of interest to measurement theorists. In addition to presenting new contributions to measurement theory and practice, JEM also serves as a vehicle for improving educational measurement applications in a variety of settings.
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