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Christine E. DeMars; Paulius Satkus – Educational and Psychological Measurement, 2024
Marginal maximum likelihood, a common estimation method for item response theory models, is not inherently a Bayesian procedure. However, due to estimation difficulties, Bayesian priors are often applied to the likelihood when estimating 3PL models, especially with small samples. Little focus has been placed on choosing the priors for marginal…
Descriptors: Item Response Theory, Statistical Distributions, Error of Measurement, Bayesian Statistics
Wallin, Gabriel; Wiberg, Marie – Journal of Educational and Behavioral Statistics, 2023
This study explores the usefulness of covariates on equating test scores from nonequivalent test groups. The covariates are captured by an estimated propensity score, which is used as a proxy for latent ability to balance the test groups. The objective is to assess the sensitivity of the equated scores to various misspecifications in the…
Descriptors: Models, Error of Measurement, Robustness (Statistics), Equated Scores
Paek, Insu; Lin, Zhongtian; Chalmers, Robert Philip – Educational and Psychological Measurement, 2023
To reduce the chance of Heywood cases or nonconvergence in estimating the 2PL or the 3PL model in the marginal maximum likelihood with the expectation-maximization (MML-EM) estimation method, priors for the item slope parameter in the 2PL model or for the pseudo-guessing parameter in the 3PL model can be used and the marginal maximum a posteriori…
Descriptors: Models, Item Response Theory, Test Items, Intervals
Turner, Kyle T.; Engelhard, George, Jr. – Measurement: Interdisciplinary Research and Perspectives, 2023
The purpose of this study is to illustrate the use of functional data analysis (FDA) as a general methodology for analyzing person response functions (PRFs). Applications of FDA to psychometrics have included the estimation of item response functions and latent distributions, as well as differential item functioning. Although FDA has been…
Descriptors: Data Analysis, Item Response Theory, Psychometrics, Statistical Distributions
Sengul Avsar, Asiye; Tavsancil, Ezel – Educational Sciences: Theory and Practice, 2017
This study analysed polytomous items' psychometric properties according to nonparametric item response theory (NIRT) models. Thus, simulated datasets--three different test lengths (10, 20 and 30 items), three sample distributions (normal, right and left skewed) and three samples sizes (100, 250 and 500)--were generated by conducting 20…
Descriptors: Test Items, Psychometrics, Nonparametric Statistics, Item Response Theory
Kim, Sooyeon; Moses, Tim; Yoo, Hanwook Henry – ETS Research Report Series, 2015
The purpose of this inquiry was to investigate the effectiveness of item response theory (IRT) proficiency estimators in terms of estimation bias and error under multistage testing (MST). We chose a 2-stage MST design in which 1 adaptation to the examinees' ability levels takes place. It includes 4 modules (1 at Stage 1, 3 at Stage 2) and 3 paths…
Descriptors: Item Response Theory, Computation, Statistical Bias, Error of Measurement
Sinharay, Sandip – Journal of Educational and Behavioral Statistics, 2015
Person-fit assessment may help the researcher to obtain additional information regarding the answering behavior of persons. Although several researchers examined person fit, there is a lack of research on person-fit assessment for mixed-format tests. In this article, the lz statistic and the ?2 statistic, both of which have been used for tests…
Descriptors: Test Format, Goodness of Fit, Item Response Theory, Bayesian Statistics
Quesen, Sarah – ProQuest LLC, 2016
When studying differential item functioning (DIF) with students with disabilities (SWD) focal groups typically suffer from small sample size, whereas the reference group population is usually large. This makes it possible for a researcher to select a sample from the reference population to be similar to the focal group on the ability scale. Doing…
Descriptors: Test Items, Academic Accommodations (Disabilities), Testing Accommodations, Disabilities
Paek, Insu; Park, Hyun-Jeong; Cai, Li; Chi, Eunlim – Educational and Psychological Measurement, 2014
Typically a longitudinal growth modeling based on item response theory (IRT) requires repeated measures data from a single group with the same test design. If operational or item exposure problems are present, the same test may not be employed to collect data for longitudinal analyses and tests at multiple time points are constructed with unique…
Descriptors: Item Response Theory, Comparative Analysis, Test Items, Equated Scores
Cai, Li; Monroe, Scott – National Center for Research on Evaluation, Standards, and Student Testing (CRESST), 2014
We propose a new limited-information goodness of fit test statistic C[subscript 2] for ordinal IRT models. The construction of the new statistic lies formally between the M[subscript 2] statistic of Maydeu-Olivares and Joe (2006), which utilizes first and second order marginal probabilities, and the M*[subscript 2] statistic of Cai and Hansen…
Descriptors: Item Response Theory, Models, Goodness of Fit, Probability

Rudner, Lawrence M. – Practical Assessment, Research & Evaluation, 2001
Provides and illustrates a method to compute the expected number of misclassifications of examinees using three-parameter item response theory and two state classifications (mastery or nonmastery). The method uses the standard error and the expected examinee ability distribution. (SLD)
Descriptors: Ability, Classification, Computation, Error of Measurement

Nering, Michael L. – Applied Psychological Measurement, 1997
Evaluated the distribution of person fit within the computerized-adaptive testing (CAT) environment through simulation. Found that, within the CAT environment, these indexes tend not to follow a standard normal distribution. Person fit indexes had means and standard deviations that were quite different from the expected. (SLD)
Descriptors: Adaptive Testing, Computer Assisted Testing, Error of Measurement, Item Response Theory
Wang, Tianyou; And Others – 1996
M. J. Kolen, B. A. Hanson, and R. L. Brennan (1992) presented a procedure for assessing the conditional standard error of measurement (CSEM) of scale scores using a strong true-score model. They also investigated the ways of using nonlinear transformation from number-correct raw score to scale score to equalize the conditional standard error along…
Descriptors: Ability, Classification, Error of Measurement, Goodness of Fit