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Yuting Han; Zhehan Jiang; Lingling Xu; Fen Cai – AERA Online Paper Repository, 2024
To address the computational constraints of parameter estimation in the polytomous Cognitive Diagnosis Model (pCDM) in large-scale high data volume situations, this study proposes two two-stage polytomous attribute estimation methods: P_max and P_linear. The effects of the two-stage methods were studied via a Monte Carlo simulation study, and the…
Descriptors: Medical Education, Licensing Examinations (Professions), Measurement Techniques, Statistical Data
McNeish, Daniel; Harring, Jeffrey R. – Educational and Psychological Measurement, 2017
To date, small sample problems with latent growth models (LGMs) have not received the amount of attention in the literature as related mixed-effect models (MEMs). Although many models can be interchangeably framed as a LGM or a MEM, LGMs uniquely provide criteria to assess global data-model fit. However, previous studies have demonstrated poor…
Descriptors: Growth Models, Goodness of Fit, Error Correction, Sampling
Stander, Julian; Dalla Valle, Luciana – Journal of Statistics Education, 2017
We discuss the learning goals, content, and delivery of a University of Plymouth intensive module delivered over four weeks entitled MATH1608PP Understanding Big Data from Social Networks, aimed at introducing students to a broad range of techniques used in modern Data Science. This module made use of R, accessed through RStudio, and some popular…
Descriptors: Foreign Countries, College Students, College Mathematics, Statistics
Gill, Brian; Shoji, Megan; Coen, Thomas; Place, Kate – Regional Educational Laboratory Mid-Atlantic, 2016
School districts and states across the Regional Educational Laboratory Mid-Atlantic Region and the country as a whole have been modifying their teacher evaluation systems to identify more effective and less effective teachers and provide better feedback to improve instructional practice. The new systems typically include components related to…
Descriptors: Predictive Validity, Test Bias, Test Content, School Districts