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Direct linkDelafontaine, Jolien; Chen, Changsheng; Park, Jung Yeon; Van den Noortgate, Wim – Large-scale Assessments in Education, 2022
In cognitive diagnosis assessment (CDA), the impact of misspecified item-attribute relations (or "Q-matrix") designed by subject-matter experts has been a great challenge to real-world applications. This study examined parameter estimation of the CDA with the expert-designed Q-matrix and two refined Q-matrices for international…
Descriptors: Q Methodology, Matrices, Cognitive Measurement, Diagnostic Tests
Sinharay, Sandip – Journal of Educational Measurement, 2017
Person-fit assessment (PFA) is concerned with uncovering atypical test performance as reflected in the pattern of scores on individual items on a test. Existing person-fit statistics (PFSs) include both parametric and nonparametric statistics. Comparison of PFSs has been a popular research topic in PFA, but almost all comparisons have employed…
Descriptors: Goodness of Fit, Testing, Test Items, Scores
Berger, Moritz; Tutz, Gerhard – Journal of Educational and Behavioral Statistics, 2016
Detection of differential item functioning (DIF) by use of the logistic modeling approach has a long tradition. One big advantage of the approach is that it can be used to investigate nonuniform (NUDIF) as well as uniform DIF (UDIF). The classical approach allows one to detect DIF by distinguishing between multiple groups. We propose an…
Descriptors: Test Bias, Regression (Statistics), Nonparametric Statistics, Statistical Analysis
Download full textSengul 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
Golino, Hudson F.; Gomes, Cristiano M. A. – International Journal of Research & Method in Education, 2016
This paper presents a non-parametric imputation technique, named random forest, from the machine learning field. The random forest procedure has two main tuning parameters: the number of trees grown in the prediction and the number of predictors used. Fifty experimental conditions were created in the imputation procedure, with different…
Descriptors: Item Response Theory, Regression (Statistics), Difficulty Level, Goodness of Fit
Lee, Young-Sun; Wollack, James A.; Douglas, Jeffrey – Educational and Psychological Measurement, 2009
The purpose of this study was to assess the model fit of a 2PL through comparison with the nonparametric item characteristic curve (ICC) estimation procedures. Results indicate that three nonparametric procedures implemented produced ICCs that are similar to that of the 2PL for items simulated to fit the 2PL. However for misfitting items,…
Descriptors: Nonparametric Statistics, Item Response Theory, Test Items, Simulation
Emons, Wilco H. M. – Applied Psychological Measurement, 2008
Person-fit methods are used to uncover atypical test performance as reflected in the pattern of scores on individual items in a test. Unlike parametric person-fit statistics, nonparametric person-fit statistics do not require fitting a parametric test theory model. This study investigates the effectiveness of generalizations of nonparametric…
Descriptors: Simulation, Nonparametric Statistics, Item Response Theory, Goodness of Fit
Wells, Craig S.; Bolt, Daniel M. – Applied Measurement in Education, 2008
Tests of model misfit are often performed to validate the use of a particular model in item response theory. Douglas and Cohen (2001) introduced a general nonparametric approach for detecting misfit under the two-parameter logistic model. However, the statistical properties of their approach, and empirical comparisons to other methods, have not…
Descriptors: Test Length, Test Items, Monte Carlo Methods, Nonparametric Statistics
