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Dimitrov, Dimiter M.; Atanasov, Dimitar V. – Educational and Psychological Measurement, 2021
This study presents a latent (item response theory--like) framework of a recently developed classical approach to test scoring, equating, and item analysis, referred to as "D"-scoring method. Specifically, (a) person and item parameters are estimated under an item response function model on the "D"-scale (from 0 to 1) using…
Descriptors: Scoring, Equated Scores, Item Analysis, Item Response Theory
Hosseinzadeh, Mostafa – ProQuest LLC, 2021
In real-world situations, multidimensional data may appear on large-scale tests or attitudinal surveys. A simple structure, multidimensional model may be used to evaluate the items, ignoring the cross-loading of some items on the secondary dimension. The purpose of this study was to investigate the influence of structure complexity magnitude of…
Descriptors: Item Response Theory, Models, Simulation, Evaluation Methods
Ippel, Lianne; Magis, David – Educational and Psychological Measurement, 2020
In dichotomous item response theory (IRT) framework, the asymptotic standard error (ASE) is the most common statistic to evaluate the precision of various ability estimators. Easy-to-use ASE formulas are readily available; however, the accuracy of some of these formulas was recently questioned and new ASE formulas were derived from a general…
Descriptors: Item Response Theory, Error of Measurement, Accuracy, Standards
Chengyu Cui; Chun Wang; Gongjun Xu – Grantee Submission, 2024
Multidimensional item response theory (MIRT) models have generated increasing interest in the psychometrics literature. Efficient approaches for estimating MIRT models with dichotomous responses have been developed, but constructing an equally efficient and robust algorithm for polytomous models has received limited attention. To address this gap,…
Descriptors: Item Response Theory, Accuracy, Simulation, Psychometrics
Lee, Woo-yeol; Cho, Sun-Joo – Journal of Educational Measurement, 2017
Cross-level invariance in a multilevel item response model can be investigated by testing whether the within-level item discriminations are equal to the between-level item discriminations. Testing the cross-level invariance assumption is important to understand constructs in multilevel data. However, in most multilevel item response model…
Descriptors: Test Items, Item Response Theory, Item Analysis, Simulation
Peer reviewedThissen, David; Wainer, Howard – Psychometrika, 1982
The mathematics required to calculate the asymptotic standard errors of the parameters of three commonly used logistic item response models is described and used to generate values for common situations. Difficulties in using maximum likelihood estimation with the three parameter model are discussed. (Author/JKS)
Descriptors: Error of Measurement, Item Analysis, Latent Trait Theory, Maximum Likelihood Statistics
Peer reviewedLord, Frederic M. – Psychometrika, 1983
Given known item parameters for a test, unbiased estimators are derived for an examinee's ability parameter, his or her proportion correct true score for the variances of these parameters and for the parallel forms reliability of the maximum likelihood estimator of the ability parameter. (Author/JKS)
Descriptors: Error of Measurement, Estimation (Mathematics), Item Analysis, Latent Trait Theory
Enders, Craig K. – Educational and Psychological Measurement, 2004
A method for incorporating maximum likelihood (ML) estimation into reliability analyses with item-level missing data is outlined. An ML estimate of the covariance matrix is first obtained using the expectation maximization (EM) algorithm, and coefficient alpha is subsequently computed using standard formulae. A simulation study demonstrated that…
Descriptors: Intervals, Simulation, Test Reliability, Computation
Patience, Wayne M.; Reckase, Mark D. – 1979
An experiment was performed with computer-generated data to investigate some of the operational characteristics of tailored testing as they are related to various provisions of the computer program and item pool. With respect to the computer program, two characteristics were varied: the size of the step of increase or decrease in item difficulty…
Descriptors: Adaptive Testing, Computer Assisted Testing, Difficulty Level, Error of Measurement

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