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Cheng, Ying; Shao, Can – Educational and Psychological Measurement, 2022
Computer-based and web-based testing have become increasingly popular in recent years. Their popularity has dramatically expanded the availability of response time data. Compared to the conventional item response data that are often dichotomous or polytomous, response time has the advantage of being continuous and can be collected in an…
Descriptors: Reaction Time, Test Wiseness, Computer Assisted Testing, Simulation
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Liu, Yue; Cheng, Ying; Liu, Hongyun – Educational and Psychological Measurement, 2020
The responses of non-effortful test-takers may have serious consequences as non-effortful responses can impair model calibration and latent trait inferences. This article introduces a mixture model, using both response accuracy and response time information, to help differentiating non-effortful and effortful individuals, and to improve item…
Descriptors: Item Response Theory, Test Wiseness, Response Style (Tests), Reaction Time
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Hong, Maxwell; Steedle, Jeffrey T.; Cheng, Ying – Educational and Psychological Measurement, 2020
Insufficient effort responding (IER) affects many forms of assessment in both educational and psychological contexts. Much research has examined different types of IER, IER's impact on the psychometric properties of test scores, and preprocessing procedures used to detect IER. However, there is a gap in the literature in terms of practical advice…
Descriptors: Responses, Psychometrics, Test Validity, Test Reliability
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Cheng, Ying; Shao, Can; Lathrop, Quinn N. – Educational and Psychological Measurement, 2016
Due to its flexibility, the multiple-indicator, multiple-causes (MIMIC) model has become an increasingly popular method for the detection of differential item functioning (DIF). In this article, we propose the mediated MIMIC model method to uncover the underlying mechanism of DIF. This method extends the usual MIMIC model by including one variable…
Descriptors: Test Bias, Models, Simulation, Sample Size
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Cheng, Ying; Patton, Jeffrey M.; Shao, Can – Educational and Psychological Measurement, 2015
a-Stratified computerized adaptive testing with b-blocking (AST), as an alternative to the widely used maximum Fisher information (MFI) item selection method, can effectively balance item pool usage while providing accurate latent trait estimates in computerized adaptive testing (CAT). However, previous comparisons of these methods have treated…
Descriptors: Computer Assisted Testing, Adaptive Testing, Test Items, Item Banks
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Patton, Jeffrey M.; Cheng, Ying; Yuan, Ke-Hai; Diao, Qi – Educational and Psychological Measurement, 2014
When item parameter estimates are used to estimate the ability parameter in item response models, the standard error (SE) of the ability estimate must be corrected to reflect the error carried over from item calibration. For maximum likelihood (ML) ability estimates, a corrected asymptotic SE is available, but it requires a long test and the…
Descriptors: Sampling, Statistical Inference, Maximum Likelihood Statistics, Computation
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Cheng, Ying; Yuan, Ke-Hai; Liu, Cheng – Educational and Psychological Measurement, 2012
Reliability of test scores is one of the most pervasive psychometric concepts in measurement. Reliability coefficients based on a unifactor model for continuous indicators include maximal reliability rho and an unweighted sum score-based omega, among many others. With increasing popularity of item response theory, a parallel reliability measure pi…
Descriptors: Reliability, Factor Analysis, Psychometrics, Item Response Theory
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Cheng, Ying – Educational and Psychological Measurement, 2010
This article proposes a new item selection method, namely, the modified maximum global discrimination index (MMGDI) method, for cognitive diagnostic computerized adaptive testing (CD-CAT). The new method captures two aspects of the appeal of an item: (a) the amount of contribution it can make toward adequate coverage of every attribute and (b) the…
Descriptors: Cognitive Tests, Diagnostic Tests, Computer Assisted Testing, Adaptive Testing