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Xiao, Jiaying; Bulut, Okan – Educational and Psychological Measurement, 2020
Large amounts of missing data could distort item parameter estimation and lead to biased ability estimates in educational assessments. Therefore, missing responses should be handled properly before estimating any parameters. In this study, two Monte Carlo simulation studies were conducted to compare the performance of four methods in handling…
Descriptors: Data, Computation, Ability, Maximum Likelihood Statistics
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Johnson, Wendy; Deary, Ian J.; Bouchard, Thomas J., Jr. – Educational and Psychological Measurement, 2018
Most study samples show less variability in key variables than do their source populations due most often to indirect selection into study participation associated with a wide range of personal and circumstantial characteristics. Formulas exist to correct the distortions of population-level correlations created. Formula accuracy has been tested…
Descriptors: Correlation, Sampling, Statistical Distributions, Accuracy
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Suero, Manuel; Privado, Jesús; Botella, Juan – Psicologica: International Journal of Methodology and Experimental Psychology, 2017
A simulation study is presented to evaluate and compare three methods to estimate the variance of the estimates of the parameters d and "C" of the signal detection theory (SDT). Several methods have been proposed to calculate the variance of their estimators, "d'" and "c." Those methods have been mostly assessed by…
Descriptors: Evaluation Methods, Theories, Simulation, Statistical Analysis
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Pfaffel, Andreas; Schober, Barbara; Spiel, Christiane – Practical Assessment, Research & Evaluation, 2016
A common methodological problem in the evaluation of the predictive validity of selection methods, e.g. in educational and employment selection, is that the correlation between predictor and criterion is biased. Thorndike's (1949) formulas are commonly used to correct for this biased correlation. An alternative approach is to view the selection…
Descriptors: Comparative Analysis, Correlation, Statistical Bias, Maximum Likelihood Statistics
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Yang, Ji Seung; Zheng, Xiaying – Journal of Educational and Behavioral Statistics, 2018
The purpose of this article is to introduce and review the capability and performance of the Stata item response theory (IRT) package that is available from Stata v.14, 2015. Using a simulated data set and a publicly available item response data set extracted from Programme of International Student Assessment, we review the IRT package from…
Descriptors: Item Response Theory, Item Analysis, Computer Software, Statistical Analysis
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Pfaffel, Andreas; Spiel, Christiane – Practical Assessment, Research & Evaluation, 2016
Approaches to correcting correlation coefficients for range restriction have been developed under the framework of large sample theory. The accuracy of missing data techniques for correcting correlation coefficients for range restriction has thus far only been investigated with relatively large samples. However, researchers and evaluators are…
Descriptors: Correlation, Sample Size, Error of Measurement, Accuracy
Rothenbusch, Sandra; Voss, Thamar; Golle, Jessika; Zettler, Ingo – Gifted Child Quarterly, 2018
This study investigated teacher and parent ratings of teacher-nominated gifted elementary school students' verbal abilities, mathematical abilities, deductive reasoning, creative thinking, and engagement, and connected these ratings to school grades. Teacher and parent ratings were compared with regard to accuracy levels and halo effects.…
Descriptors: Academically Gifted, Teacher Attitudes, Elementary School Teachers, Elementary School Students
Ostrow, Korinn; Donnelly, Chistopher; Heffernan, Neil – International Educational Data Mining Society, 2015
As adaptive tutoring systems grow increasingly popular for the completion of classwork and homework, it is crucial to assess the manner in which students are scored within these platforms. The majority of systems, including ASSISTments, return the binary correctness of a student's first attempt at solving each problem. Yet for many teachers,…
Descriptors: Intelligent Tutoring Systems, Scoring, Testing, Credits