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Joo, Seang-Hwane; Lee, Philseok – Journal of Educational Measurement, 2022
Abstract This study proposes a new Bayesian differential item functioning (DIF) detection method using posterior predictive model checking (PPMC). Item fit measures including infit, outfit, observed score distribution (OSD), and Q1 were considered as discrepancy statistics for the PPMC DIF methods. The performance of the PPMC DIF method was…
Descriptors: Test Items, Bayesian Statistics, Monte Carlo Methods, Prediction
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Lozano, José H.; Revuelta, Javier – Educational and Psychological Measurement, 2023
The present paper introduces a general multidimensional model to measure individual differences in learning within a single administration of a test. Learning is assumed to result from practicing the operations involved in solving the items. The model accounts for the possibility that the ability to learn may manifest differently for correct and…
Descriptors: Bayesian Statistics, Learning Processes, Test Items, Item Analysis
Jing Lu; Chun Wang; Jiwei Zhang; Xue Wang – Grantee Submission, 2023
Changepoints are abrupt variations in a sequence of data in statistical inference. In educational and psychological assessments, it is pivotal to properly differentiate examinees' aberrant behaviors from solution behavior to ensure test reliability and validity. In this paper, we propose a sequential Bayesian changepoint detection algorithm to…
Descriptors: Bayesian Statistics, Behavior Patterns, Computer Assisted Testing, Accuracy
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Wyse, Adam E. – Educational Measurement: Issues and Practice, 2017
This article illustrates five different methods for estimating Angoff cut scores using item response theory (IRT) models. These include maximum likelihood (ML), expected a priori (EAP), modal a priori (MAP), and weighted maximum likelihood (WML) estimators, as well as the most commonly used approach based on translating ratings through the test…
Descriptors: Cutting Scores, Item Response Theory, Bayesian Statistics, Maximum Likelihood Statistics
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Finch, Holmes; Edwards, Julianne M. – Educational and Psychological Measurement, 2016
Standard approaches for estimating item response theory (IRT) model parameters generally work under the assumption that the latent trait being measured by a set of items follows the normal distribution. Estimation of IRT parameters in the presence of nonnormal latent traits has been shown to generate biased person and item parameter estimates. A…
Descriptors: Item Response Theory, Computation, Nonparametric Statistics, Bayesian Statistics
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Mahmud, Jumailiyah; Sutikno, Muzayanah; Naga, Dali S. – Educational Research and Reviews, 2016
The aim of this study is to determine variance difference between maximum likelihood and expected A posteriori estimation methods viewed from number of test items of aptitude test. The variance presents an accuracy generated by both maximum likelihood and Bayes estimation methods. The test consists of three subtests, each with 40 multiple-choice…
Descriptors: Maximum Likelihood Statistics, Computation, Item Response Theory, Test Items
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Kim, Do-Hong; Lambert, Richard G.; Durham, Sean; Burts, Diane C. – Early Education and Development, 2018
Research Findings: This study builds on prior work related to the assessment of young dual language learners (DLLs). The purposes of the study were to (a) determine whether latent subgroups of preschool DLLs would replicate those found previously and (b) examine the validity of GOLD® by Teaching Strategies with empirically derived subgroups.…
Descriptors: Preschool Education, Teaching Methods, Bilingualism, Bilingual Education
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Sheng, Yanyan; Wikle, Christopher K. – Educational and Psychological Measurement, 2008
As item response models gain increased popularity in large-scale educational and measurement testing situations, many studies have been conducted on the development and applications of unidimensional and multidimensional models. Recently, attention has been paid to IRT-based models with an overall ability dimension underlying several ability…
Descriptors: Test Items, Individual Testing, Item Response Theory, Evaluation Methods
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Penfield, Randall D. – Applied Measurement in Education, 2006
This study applied the maximum expected information (MEI) and the maximum posterior-weighted information (MPI) approaches of computer adaptive testing item selection to the case of a test using polytomous items following the partial credit model. The MEI and MPI approaches are described. A simulation study compared the efficiency of ability…
Descriptors: Bayesian Statistics, Adaptive Testing, Computer Assisted Testing, Test Items
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May, Henry – Journal of Educational and Behavioral Statistics, 2006
In this article, a new method is presented and implemented for deriving a scale of socioeconomic status (SES) from international survey data using a multilevel Bayesian item response theory (IRT) model. The proposed model incorporates both international anchor items and nation-specific items and is able to (a) produce student family SES scores…
Descriptors: Item Response Theory, Bayesian Statistics, Socioeconomic Status, Scaling
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Bejar, Isaac I. – Journal of Educational Measurement, 1980
Two procedures are presented for detecting violations of the unidimensionality assumption made by latent trait models without requiring factor analysis of inter-item correlation matrices. Both procedures require that departures from unidimensionality be hypothesized beforehand. This is usually possible in achievement tests where several content…
Descriptors: Achievement Tests, Bayesian Statistics, Cluster Grouping, Content Analysis
Perkins, Kyle – 1987
In this paper four classes of procedures for measuring the instructional sensitivity of reading comprehension test items are reviewed. True experimental designs are not recommended because some of the most important reading comprehension variables do not lend themselves to experimental manipulation. "Ex post facto" factorial designs are…
Descriptors: Bayesian Statistics, Correlation, Elementary Secondary Education, Evaluation Methods
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Sinharay, Sandip – Journal of Educational and Behavioral Statistics, 2006
Bayesian networks are frequently used in educational assessments primarily for learning about students' knowledge and skills. There is a lack of works on assessing fit of Bayesian networks. This article employs the posterior predictive model checking method, a popular Bayesian model checking tool, to assess fit of simple Bayesian networks. A…
Descriptors: Models, Educational Assessment, Diagnostic Tests, Evaluation Methods