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He, Yinhong – Journal of Educational Measurement, 2023
Back random responding (BRR) behavior is one of the commonly observed careless response behaviors. Accurately detecting BRR behavior can improve test validities. Yu and Cheng (2019) showed that the change point analysis (CPA) procedure based on weighted residual (CPA-WR) performed well in detecting BRR. Compared with the CPA procedure, the…
Descriptors: Test Validity, Item Response Theory, Measurement, Monte Carlo Methods
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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
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Elizabeth Talbott; Andres De Los Reyes; Junhui Yang; Mo Wang – Society for Research on Educational Effectiveness, 2024
Youth in the U.S. face a mental health crisis--one that has steadily worsened over the past 10 years (Centers for Disease Control, 2023). Youth with disabilities may experience increased mental health risk compared to their peers without disabilities. Recent national surveys and systematic reviews reveal that mental health risk may be elevated for…
Descriptors: Youth, Youth Problems, Mental Health, Crisis Intervention
Garrett, Phyllis – ProQuest LLC, 2009
The use of polytomous items in assessments has increased over the years, and as a result, the validity of these assessments has been a concern. Differential item functioning (DIF) and missing data are two factors that may adversely affect assessment validity. Both factors have been studied separately, but DIF and missing data are likely to occur…
Descriptors: Sample Size, Monte Carlo Methods, Test Validity, Effect Size
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Yoo, Jin Eun – Educational and Psychological Measurement, 2009
This Monte Carlo study investigates the beneficiary effect of including auxiliary variables during estimation of confirmatory factor analysis models with multiple imputation. Specifically, it examines the influence of sample size, missing rates, missingness mechanism combinations, missingness types (linear or convex), and the absence or presence…
Descriptors: Monte Carlo Methods, Research Methodology, Test Validity, Factor Analysis