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Güler Yavuz Temel – Journal of Educational Measurement, 2024
The purpose of this study was to investigate multidimensional DIF with a simple and nonsimple structure in the context of multidimensional Graded Response Model (MGRM). This study examined and compared the performance of the IRT-LR and Wald test using MML-EM and MHRM estimation approaches with different test factors and test structures in…
Descriptors: Computation, Multidimensional Scaling, Item Response Theory, Models
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Cornelis Potgieter; Xin Qiao; Akihito Kamata; Yusuf Kara – Journal of Educational Measurement, 2024
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…
Descriptors: Oral Reading, Reading Fluency, Scores, Psychometrics
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Rutkowski, Leslie; Zhou, Yan – Journal of Educational Measurement, 2015
Given the importance of large-scale assessments to educational policy conversations, it is critical that subpopulation achievement is estimated reliably and with sufficient precision. Despite this importance, biased subpopulation estimates have been found to occur when variables in the conditioning model side of a latent regression model contain…
Descriptors: Error of Measurement, Error Correction, Regression (Statistics), Computation
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Jiang, Yanlin; von Davier, Alina A.; Chen, Haiwen – Journal of Educational Measurement, 2012
This article presents a method for evaluating equating results. Within the kernel equating framework, the percent relative error (PRE) for chained equipercentile equating was computed under the nonequivalent groups with anchor test (NEAT) design. The method was applied to two data sets to obtain the PRE, which can be used to measure equating…
Descriptors: Equated Scores, Evaluation, Error of Measurement, Computation
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Moses, Tim – Journal of Educational Measurement, 2012
The focus of this paper is assessing the impact of measurement errors on the prediction error of an observed-score regression. Measures are presented and described for decomposing the linear regression's prediction error variance into parts attributable to the true score variance and the error variances of the dependent variable and the predictor…
Descriptors: Error of Measurement, Prediction, Regression (Statistics), True Scores
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Jiao, Hong; Kamata, Akihito; Wang, Shudong; Jin, Ying – Journal of Educational Measurement, 2012
The applications of item response theory (IRT) models assume local item independence and that examinees are independent of each other. When a representative sample for psychometric analysis is selected using a cluster sampling method in a testlet-based assessment, both local item dependence and local person dependence are likely to be induced.…
Descriptors: Item Response Theory, Test Items, Markov Processes, Monte Carlo Methods
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Seo, Minhee; Roussos, Louis A. – Journal of Educational Measurement, 2010
DIMTEST is a widely used and studied method for testing the hypothesis of test unidimensionality as represented by local item independence. However, DIMTEST does not report the amount of multidimensionality that exists in data when rejecting its null. To provide more information regarding the degree to which data depart from unidimensionality, a…
Descriptors: Effect Size, Statistical Bias, Computation, Test Length
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Kim, Seonghoon; Feldt, Leonard S. – Journal of Educational Measurement, 2008
This article extends the Bonett (2003a) approach to testing the equality of alpha coefficients from two independent samples to the case of m [greater than or equal] 2 independent samples. The extended Fisher-Bonett test and its competitor, the Hakstian-Whalen (1976) test, are illustrated with numerical examples of both hypothesis testing and power…
Descriptors: Tests, Comparative Analysis, Hypothesis Testing, Error of Measurement
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Li, Yuan H.; Lissitz, Robert W. – Journal of Educational Measurement, 2004
The analytically derived asymptotic standard errors (SEs) of maximum likelihood (ML) item estimates can be approximated by a mathematical function without examinees' responses to test items, and the empirically determined SEs of marginal maximum likelihood estimation (MMLE)/Bayesian item estimates can be obtained when the same set of items is…
Descriptors: Test Items, Computation, Item Response Theory, Error of Measurement