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Dimitrov, Dimiter M.; Atanasov, Dimitar V.; Luo, Yong – Measurement: Interdisciplinary Research and Perspectives, 2020
This study examines and compares four person-fit statistics (PFSs) in the framework of the "D"- scoring method (DSM): (a) van der Flier's "U3" statistic; (b) "Ud" statistic, as a modification of "U3" under the DSM; (c) "Zd" statistic, as a modification of the "Z3 (l[subscript z])"…
Descriptors: Goodness of Fit, Item Analysis, Item Response Theory, Scoring
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Abulela, Mohammed A. A.; Rios, Joseph A. – Applied Measurement in Education, 2022
When there are no personal consequences associated with test performance for examinees, rapid guessing (RG) is a concern and can differ between subgroups. To date, the impact of differential RG on item-level measurement invariance has received minimal attention. To that end, a simulation study was conducted to examine the robustness of the…
Descriptors: Comparative Analysis, Robustness (Statistics), Nonparametric Statistics, Item Analysis
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Guo, Hongwen; Zu, Jiyun; Kyllonen, Patrick; Schmitt, Neal – ETS Research Report Series, 2016
In this report, systematic applications of statistical and psychometric methods are used to develop and evaluate scoring rules in terms of test reliability. Data collected from a situational judgment test are used to facilitate the comparison. For a well-developed item with appropriate keys (i.e., the correct answers), agreement among various…
Descriptors: Scoring, Test Reliability, Statistical Analysis, Psychometrics
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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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Lei, Pui-Wa; Dunbar, Stephen B.; Kolen, Michael J. – Educational and Psychological Measurement, 2004
This study compares the parametric multiple-choice model and the nonparametric kernel smoothing approach to estimating option characteristic functions (OCCs) using an empirical criterion, the stability of curve estimates over occasions that represents random error. The potential utility of graphical OCCs in item analysis was illustrated with…
Descriptors: Nonparametric Statistics, Multiple Choice Tests, Item Analysis, Item Response Theory