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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
Liu, Yang; Wang, Xiaojing – Journal of Educational and Behavioral Statistics, 2020
Parametric methods, such as autoregressive models or latent growth modeling, are usually inflexible to model the dependence and nonlinear effects among the changes of latent traits whenever the time gap is irregular and the recorded time points are individually varying. Often in practice, the growth trend of latent traits is subject to certain…
Descriptors: Bayesian Statistics, Nonparametric Statistics, Regression (Statistics), Item Response Theory
van Rijn, Peter W.; Rijmen, Frank – ETS Research Report Series, 2012
Hooker and colleagues addressed a paradoxical situation that can arise in the application of multidimensional item response theory (MIRT) models to educational test data. We demonstrate that this MIRT paradox is an instance of the explaining-away phenomenon in Bayesian networks, and we attempt to enhance the understanding of MIRT models by placing…
Descriptors: Item Response Theory, Educational Testing, Bayesian Statistics, Statistical Analysis
Zwick, Rebecca – ETS Research Report Series, 2012
Differential item functioning (DIF) analysis is a key component in the evaluation of the fairness and validity of educational tests. The goal of this project was to review the status of ETS DIF analysis procedures, focusing on three aspects: (a) the nature and stringency of the statistical rules used to flag items, (b) the minimum sample size…
Descriptors: Test Bias, Sample Size, Bayesian Statistics, Evaluation Methods
Boyd, Donald; Lankford, Hamilton; Loeb, Susanna; Wyckoff, James – Journal of Educational and Behavioral Statistics, 2013
Test-based accountability as well as value-added asessments and much experimental and quasi-experimental research in education rely on achievement tests to measure student skills and knowledge. Yet, we know little regarding fundamental properties of these tests, an important example being the extent of measurement error and its implications for…
Descriptors: Accountability, Educational Research, Educational Testing, Error of Measurement
Rock, Donald A. – ETS Research Report Series, 2012
This paper provides a history of ETS's role in developing assessment instruments and psychometric procedures for measuring change in large-scale national assessments funded by the Longitudinal Studies branch of the National Center for Education Statistics. It documents the innovations developed during more than 30 years of working with…
Descriptors: Models, Educational Change, Longitudinal Studies, Educational Development
Steinheiser, Frederick H., Jr. – 1976
A computer simulation of Bayes' Theorem was conducted in order to determine the probability that an examinee was a master conditional upon his test score. The inputs were: number of mastery states assumed, test length, prior expectation of masters in the examinee population, and conditional probability of a master getting a randomly selected test…
Descriptors: Bayesian Statistics, Classification, Computer Programs, Criterion Referenced Tests
Peer reviewedBraun, Henry I.; And Others – Psychometrika, 1983
Empirical Bayes methods are shown to provide a practical alternative to standard least squares methods in fitting high dimensional models to sparse data. An example concerning prediction bias in educational testing is presented as an illustration. (Author)
Descriptors: Bayesian Statistics, Educational Testing, Goodness of Fit, Mathematical Models
Novick, Melvin R. – 1973
This project is concerned with the development and implementation of some new statistical techniques that will facilitate a continuing input of information about the student to the instructional manager so that individualization of instruction can be managed effectively. The source of this informational input is typically a short…
Descriptors: Bayesian Statistics, Computer Oriented Programs, Computer Programs, Criterion Referenced Tests

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