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Joakim Wallmark; James O. Ramsay; Juan Li; Marie Wiberg – Journal of Educational and Behavioral Statistics, 2024
Item response theory (IRT) models the relationship between the possible scores on a test item against a test taker's attainment of the latent trait that the item is intended to measure. In this study, we compare two models for tests with polytomously scored items: the optimal scoring (OS) model, a nonparametric IRT model based on the principles of…
Descriptors: Item Response Theory, Test Items, Models, Scoring
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Jordan M. Wheeler; Allan S. Cohen; Shiyu Wang – Journal of Educational and Behavioral Statistics, 2024
Topic models are mathematical and statistical models used to analyze textual data. The objective of topic models is to gain information about the latent semantic space of a set of related textual data. The semantic space of a set of textual data contains the relationship between documents and words and how they are used. Topic models are becoming…
Descriptors: Semantics, Educational Assessment, Evaluators, Reliability
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Na Shan; Ping-Feng Xu – Journal of Educational and Behavioral Statistics, 2025
The detection of differential item functioning (DIF) is important in psychological and behavioral sciences. Standard DIF detection methods perform an item-by-item test iteratively, often assuming that all items except the one under investigation are DIF-free. This article proposes a Bayesian adaptive Lasso method to detect DIF in graded response…
Descriptors: Bayesian Statistics, Item Response Theory, Adolescents, Longitudinal Studies
Wang, Chun; Nydick, Steven W. – Journal of Educational and Behavioral Statistics, 2020
Recent work on measuring growth with categorical outcome variables has combined the item response theory (IRT) measurement model with the latent growth curve model and extended the assessment of growth to multidimensional IRT models and higher order IRT models. However, there is a lack of synthetic studies that clearly evaluate the strength and…
Descriptors: Item Response Theory, Longitudinal Studies, Comparative Analysis, Models
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Monroe, Scott – Journal of Educational and Behavioral Statistics, 2019
In item response theory (IRT) modeling, the Fisher information matrix is used for numerous inferential procedures such as estimating parameter standard errors, constructing test statistics, and facilitating test scoring. In principal, these procedures may be carried out using either the expected information or the observed information. However, in…
Descriptors: Item Response Theory, Error of Measurement, Scoring, Inferences
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von Davier, Matthias; Khorramdel, Lale; He, Qiwei; Shin, Hyo Jeong; Chen, Haiwen – Journal of Educational and Behavioral Statistics, 2019
International large-scale assessments (ILSAs) transitioned from paper-based assessments to computer-based assessments (CBAs) facilitating the use of new item types and more effective data collection tools. This allows implementation of more complex test designs and to collect process and response time (RT) data. These new data types can be used to…
Descriptors: International Assessment, Computer Assisted Testing, Psychometrics, Item Response Theory
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Ranger, Jochen; Kuhn, Jörg-Tobias – Journal of Educational and Behavioral Statistics, 2018
Diffusion-based item response theory models for responses and response times in tests have attracted increased attention recently in psychometrics. Analyzing response time data, however, is delicate as response times are often contaminated by unusual observations. This can have serious effects on the validity of statistical inference. In this…
Descriptors: Item Response Theory, Computation, Robustness (Statistics), Reaction Time
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Monroe, Scott – Journal of Educational and Behavioral Statistics, 2021
This research proposes a new statistic for testing latent variable distribution fit for unidimensional item response theory (IRT) models. If the typical assumption of normality is violated, then item parameter estimates will be biased, and dependent quantities such as IRT score estimates will be adversely affected. The proposed statistic compares…
Descriptors: Item Response Theory, Simulation, Scores, Comparative Analysis
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Sinharay, Sandip – Journal of Educational and Behavioral Statistics, 2016
Meijer and van Krimpen-Stoop noted that the number of person-fit statistics (PFSs) that have been designed for computerized adaptive tests (CATs) is relatively modest. This article partially addresses that concern by suggesting three new PFSs for CATs. The statistics are based on tests for a change point and can be used to detect an abrupt change…
Descriptors: Computer Assisted Testing, Adaptive Testing, Item Response Theory, Goodness of Fit
Casabianca, Jodi M.; Lewis, Charles – Journal of Educational and Behavioral Statistics, 2015
Loglinear smoothing (LLS) estimates the latent trait distribution while making fewer assumptions about its form and maintaining parsimony, thus leading to more precise item response theory (IRT) item parameter estimates than standard marginal maximum likelihood (MML). This article provides the expectation-maximization algorithm for MML estimation…
Descriptors: Item Response Theory, Maximum Likelihood Statistics, Computation, Comparative Analysis
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Marianti, Sukaesi; Fox, Jean-Paul; Avetisyan, Marianna; Veldkamp, Bernard P.; Tijmstra, Jesper – Journal of Educational and Behavioral Statistics, 2014
Many standardized tests are now administered via computer rather than paper-and-pencil format. In a computer-based testing environment, it is possible to record not only the test taker's response to each question (item) but also the amount of time spent by the test taker in considering and answering each item. Response times (RTs) provide…
Descriptors: Reaction Time, Response Style (Tests), Computer Assisted Testing, Bayesian Statistics
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Debeer, Dries; Buchholz, Janine; Hartig, Johannes; Janssen, Rianne – Journal of Educational and Behavioral Statistics, 2014
In this article, the change in examinee effort during an assessment, which we will refer to as persistence, is modeled as an effect of item position. A multilevel extension is proposed to analyze hierarchically structured data and decompose the individual differences in persistence. Data from the 2009 Program of International Student Achievement…
Descriptors: Reading Tests, International Programs, Testing Programs, Individual Differences
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Guo, Hongwen; Sinharay, Sandip – Journal of Educational and Behavioral Statistics, 2011
Nonparametric or kernel regression estimation of item response curves (IRCs) is often used in item analysis in testing programs. These estimates are biased when the observed scores are used as the regressor because the observed scores are contaminated by measurement error. Accuracy of this estimation is a concern theoretically and operationally.…
Descriptors: Testing Programs, Measurement, Item Analysis, Error of Measurement
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Cho, Sun-Joo; Cohen, Allan S. – Journal of Educational and Behavioral Statistics, 2010
Mixture item response theory models have been suggested as a potentially useful methodology for identifying latent groups formed along secondary, possibly nuisance dimensions. In this article, we describe a multilevel mixture item response theory (IRT) model (MMixIRTM) that allows for the possibility that this nuisance dimensionality may function…
Descriptors: Simulation, Mathematics Tests, Item Response Theory, Student Behavior
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Kim, Seonghoon; Kolen, Michael J. – Journal of Educational and Behavioral Statistics, 2007
Under item response theory, the characteristic curve methods (Haebara and Stocking-Lord methods) are used to link two ability scales from separate calibrations. The linking methods use their respective criterion functions that can be defined differently according to the symmetry- and distribution-related schemes. The symmetry-related scheme…
Descriptors: Measures (Individuals), Item Response Theory, Simulation, Comparative Analysis
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