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Novak, Josip; Rebernjak, Blaž – Measurement: Interdisciplinary Research and Perspectives, 2023
A Monte Carlo simulation study was conducted to examine the performance of [alpha], [lambda]2, [lambda][subscript 4], [lambda][subscript 2], [omega][subscript T], GLB[subscript MRFA], and GLB[subscript Algebraic] coefficients. Population reliability, distribution shape, sample size, test length, and number of response categories were varied…
Descriptors: Monte Carlo Methods, Evaluation Methods, Reliability, Simulation
Shaojie Wang; Won-Chan Lee; Minqiang Zhang; Lixin Yuan – Applied Measurement in Education, 2024
To reduce the impact of parameter estimation errors on IRT linking results, recent work introduced two information-weighted characteristic curve methods for dichotomous items. These two methods showed outstanding performance in both simulation and pseudo-form pseudo-group analysis. The current study expands upon the concept of information…
Descriptors: Item Response Theory, Test Format, Test Length, Error of Measurement
Sedat Sen; Allan S. Cohen – Educational and Psychological Measurement, 2024
A Monte Carlo simulation study was conducted to compare fit indices used for detecting the correct latent class in three dichotomous mixture item response theory (IRT) models. Ten indices were considered: Akaike's information criterion (AIC), the corrected AIC (AICc), Bayesian information criterion (BIC), consistent AIC (CAIC), Draper's…
Descriptors: Goodness of Fit, Item Response Theory, Sample Size, Classification
Wang, Shaojie; Zhang, Minqiang; Lee, Won-Chan; Huang, Feifei; Li, Zonglong; Li, Yixing; Yu, Sufang – Journal of Educational Measurement, 2022
Traditional IRT characteristic curve linking methods ignore parameter estimation errors, which may undermine the accuracy of estimated linking constants. Two new linking methods are proposed that take into account parameter estimation errors. The item- (IWCC) and test-information-weighted characteristic curve (TWCC) methods employ weighting…
Descriptors: Item Response Theory, Error of Measurement, Accuracy, Monte Carlo Methods
Koziol, Natalie A.; Goodrich, J. Marc; Yoon, HyeonJin – Educational and Psychological Measurement, 2022
Differential item functioning (DIF) is often used to examine validity evidence of alternate form test accommodations. Unfortunately, traditional approaches for evaluating DIF are prone to selection bias. This article proposes a novel DIF framework that capitalizes on regression discontinuity design analysis to control for selection bias. A…
Descriptors: Regression (Statistics), Item Analysis, Validity, Testing Accommodations
Tulek, Onder Kamil; Kose, Ibrahim Alper – Eurasian Journal of Educational Research, 2019
Purpose: This research investigates Tests that include DIF items and which are purified from DIF items. While doing this, the ability estimations and purified DIF items are compared to understand whether there is a correlation between the estimations. Method: The researcher used to R 3.4.1 in order to compare the items and after this situation;…
Descriptors: Test Items, Item Analysis, Item Response Theory, Test Length
Lu, Ru; Guo, Hongwen; Dorans, Neil J. – ETS Research Report Series, 2021
Two families of analysis methods can be used for differential item functioning (DIF) analysis. One family is DIF analysis based on observed scores, such as the Mantel-Haenszel (MH) and the standardized proportion-correct metric for DIF procedures; the other is analysis based on latent ability, in which the statistic is a measure of departure from…
Descriptors: Robustness (Statistics), Weighted Scores, Test Items, Item Analysis
Ames, Allison J.; Leventhal, Brian C.; Ezike, Nnamdi C. – Measurement: Interdisciplinary Research and Perspectives, 2020
Data simulation and Monte Carlo simulation studies are important skills for researchers and practitioners of educational and psychological measurement, but there are few resources on the topic specific to item response theory. Even fewer resources exist on the statistical software techniques to implement simulation studies. This article presents…
Descriptors: Monte Carlo Methods, Item Response Theory, Simulation, Computer Software
Chon, Kyong Hee; Lee, Won-Chan; Ansley, Timothy N. – Applied Measurement in Education, 2013
Empirical information regarding performance of model-fit procedures has been a persistent need in measurement practice. Statistical procedures for evaluating item fit were applied to real test examples that consist of both dichotomously and polytomously scored items. The item fit statistics used in this study included the PARSCALE's G[squared],…
Descriptors: Test Format, Test Items, Item Analysis, Goodness of Fit
Wollack, James A. – Applied Measurement in Education, 2006
Many of the currently available statistical indexes to detect answer copying lack sufficient power at small [alpha] levels or when the amount of copying is relatively small. Furthermore, there is no one index that is uniformly best. Depending on the type or amount of copying, certain indexes are better than others. The purpose of this article was…
Descriptors: Statistical Analysis, Item Analysis, Test Length, Sample Size
Lautenschlager, Gary J.; Park, Dong-Gun – 1987
The effects of variations in degree of range restriction and different subgroup sample sizes on the validity of several item bias detection procedures based on Item Response Theory (IRT) were investigated in a simulation study. The degree of range restriction for each of two subpopulations was varied by cutting the specified subpopulation ability…
Descriptors: Computer Simulation, Item Analysis, Latent Trait Theory, Mathematical Models
Hutten, Leah R. – 1979
Goodness of fit of raw test score data were compared, using two latent trait models: the Rasch model and the Birnbaum three-parameter logistic model. Data were taken from various achievement tests and the Scholastic Aptitude Test (Verbal). A minimum sample size of 1,000 was required, and the minimum test length was 40 items. Results indicated that…
Descriptors: Ability Identification, Achievement Tests, College Entrance Examinations, Comparative Analysis