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Gorney, Kylie; Wollack, James A.; Sinharay, Sandip; Eckerly, Carol – Journal of Educational and Behavioral Statistics, 2023
Any time examinees have had access to items and/or answers prior to taking a test, the fairness of the test and validity of test score interpretations are threatened. Therefore, there is a high demand for procedures to detect both compromised items (CI) and examinees with preknowledge (EWP). In this article, we develop a procedure that uses item…
Descriptors: Scores, Test Validity, Test Items, Prior Learning
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
He, Wei; Reckase, Mark D. – Educational and Psychological Measurement, 2014
For computerized adaptive tests (CATs) to work well, they must have an item pool with sufficient numbers of good quality items. Many researchers have pointed out that, in developing item pools for CATs, not only is the item pool size important but also the distribution of item parameters and practical considerations such as content distribution…
Descriptors: Item Banks, Test Length, Computer Assisted Testing, Adaptive Testing
Wang, Zhen; Yao, Lihua – ETS Research Report Series, 2013
The current study used simulated data to investigate the properties of a newly proposed method (Yao's rater model) for modeling rater severity and its distribution under different conditions. Our study examined the effects of rater severity, distributions of rater severity, the difference between item response theory (IRT) models with rater effect…
Descriptors: Test Format, Test Items, Responses, Computation
Jiao, Hong; Wang, Shudong; He, Wei – Journal of Educational Measurement, 2013
This study demonstrated the equivalence between the Rasch testlet model and the three-level one-parameter testlet model and explored the Markov Chain Monte Carlo (MCMC) method for model parameter estimation in WINBUGS. The estimation accuracy from the MCMC method was compared with those from the marginalized maximum likelihood estimation (MMLE)…
Descriptors: Computation, Item Response Theory, Models, Monte Carlo Methods
Schuster, Christof; Yuan, Ke-Hai – Journal of Educational and Behavioral Statistics, 2011
Because of response disturbances such as guessing, cheating, or carelessness, item response models often can only approximate the "true" individual response probabilities. As a consequence, maximum-likelihood estimates of ability will be biased. Typically, the nature and extent to which response disturbances are present is unknown, and, therefore,…
Descriptors: Computation, Item Response Theory, Probability, Maximum Likelihood Statistics
Paek, Insu; Wilson, Mark – Educational and Psychological Measurement, 2011
This study elaborates the Rasch differential item functioning (DIF) model formulation under the marginal maximum likelihood estimation context. Also, the Rasch DIF model performance was examined and compared with the Mantel-Haenszel (MH) procedure in small sample and short test length conditions through simulations. The theoretically known…
Descriptors: Test Bias, Test Length, Statistical Inference, Geometric Concepts
Penfield, Randall D. – Educational and Psychological Measurement, 2007
The standard error of the maximum likelihood ability estimator is commonly estimated by evaluating the test information function at an examinee's current maximum likelihood estimate (a point estimate) of ability. Because the test information function evaluated at the point estimate may differ from the test information function evaluated at an…
Descriptors: Simulation, Adaptive Testing, Computation, Maximum Likelihood Statistics
Paek, Insu; Young, Michael J. – Applied Measurement in Education, 2005
When the item response theory (IRT) model uses the marginal maximum likelihood estimation, person parameters are usually treated as random parameters following a certain distribution as a prior distribution to estimate the structural parameters in the model. For example, both PARSCALE (Muraki & Bock, 1999) and BILOG 3 (Mislevy & Bock,…
Descriptors: Item Response Theory, Test Items, Maximum Likelihood Statistics, Test Bias
Peer reviewedSamejima, Fumiko – Psychometrika, 1993
An approximation for the bias function of the maximum likelihood estimate of the latent trait or ability is developed for the general case where item responses are discrete, which includes the dichotomous response level, the graded response level, and the nominal response level. (SLD)
Descriptors: Ability, Equations (Mathematics), Estimation (Mathematics), Item Response Theory
Van den Noortgate, Wim; De Boeck, Paul – Journal of Educational and Behavioral Statistics, 2005
Although differential item functioning (DIF) theory traditionally focuses on the behavior of individual items in two (or a few) specific groups, in educational measurement contexts, it is often plausible to regard the set of items as a random sample from a broader category. This article presents logistic mixed models that can be used to model…
Descriptors: Test Bias, Item Response Theory, Educational Assessment, Mathematical Models
Mayberry, Paul W. – 1984
A technique for detecting item bias that is responsive to attitudinal measurement considerations is a maximum likelihood factor analysis procedure comparing multivariate factor structures across various subpopulations, often referred to as SIFASP. The SIFASP technique allows for factorial model comparisons in the testing of various hypotheses…
Descriptors: Adults, Analysis of Covariance, Attitude Measures, Data Analysis
Samejima, Fumiko – 1990
Two modification formulas are presented for the test information function in order to provide better measures of local accuracies of the estimation of "theta" when maximum likelihood estimation is used to provide the estimate of ability "theta." A minimum bound of any estimator, biased or unbiased, is considered; and Formula 1…
Descriptors: Ability Identification, Adaptive Testing, Computer Assisted Testing, Elementary Secondary Education
Mishra, Shitala P. – 1983
The study investigated cultural bias in the verbal items of the Wechsler Intelligence Scale for Children-Revised (WISC-R). Six verbal subtests of the WISC-R were administered to 40 Anglo, 40 Mexican-American, and 40 Navajo intermediate grade students matched for grade level, sex, and socioeconomic status. The responses of three groups of subjects…
Descriptors: Ethnic Bias, Intelligence Tests, Intermediate Grades, Maximum Likelihood Statistics
Levine, Michael V. – 1984
Formula score theory (FST) associates each multiple choice test with a linear operator and expresses all of the real functions of item response theory as linear combinations of the operator's eigenfunctions. Hard measurement problems can then often be reformulated as easier, standard mathematical problems. For example, the problem of estimating…
Descriptors: Cognitive Ability, Estimation (Mathematics), Latent Trait Theory, Maximum Likelihood Statistics
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