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Sainan Xu; Jing Lu; Jiwei Zhang; Chun Wang; Gongjun Xu – Grantee Submission, 2024
With the growing attention on large-scale educational testing and assessment, the ability to process substantial volumes of response data becomes crucial. Current estimation methods within item response theory (IRT), despite their high precision, often pose considerable computational burdens with large-scale data, leading to reduced computational…
Descriptors: Educational Assessment, Bayesian Statistics, Statistical Inference, Item Response Theory
Nord, C.; Hicks, L.; Hoover, K.; Jones, M.; Lin, A.; Lyons, M.; Perkins, R.; Roey, S.; Rust, K.; Sickles, D. – National Center for Education Statistics, 2011
This user's guide documents the procedures used to collect, process, and summarize data from the 2009 High School Transcript Study (HSTS 2009). Chapters detail the sampling of schools and graduates (chapters 2 and 3), data collection procedures (chapter 4), data processing procedures (chapter 5), and weighting procedures (chapter 6). Chapter 7…
Descriptors: High School Graduates, Academic Records, National Competency Tests, Questionnaires

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