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Showing 1 to 15 of 58 results Save | Export
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Levin, Joel R.; Ferron, John M.; Gafurov, Boris S. – Journal of Education for Students Placed at Risk, 2022
The present simulation study examined the statistical properties (namely, Type I error and statistical power) of various novel randomized single-case multiple-baseline designs and associated randomized-test analyses for comparing the A- to B-phase immediate abrupt outcome changes in two independent intervention conditions. It was found that with…
Descriptors: Statistical Analysis, Error of Measurement, Intervention, Program Effectiveness
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Mohammed, M. A.; Ibrahim, A. I. N.; Siri, Z.; Noor, N. F. M. – Sociological Methods & Research, 2019
In this article, a numerical method integrated with statistical data simulation technique is introduced to solve a nonlinear system of ordinary differential equations with multiple random variable coefficients. The utilization of Monte Carlo simulation with central divided difference formula of finite difference (FD) method is repeated n times to…
Descriptors: Monte Carlo Methods, Calculus, Sampling, Simulation
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Olivera-Aguilar, Margarita; Rikoon, Samuel H.; Gonzalez, Oscar; Kisbu-Sakarya, Yasemin; MacKinnon, David P. – Educational and Psychological Measurement, 2018
When testing a statistical mediation model, it is assumed that factorial measurement invariance holds for the mediating construct across levels of the independent variable X. The consequences of failing to address the violations of measurement invariance in mediation models are largely unknown. The purpose of the present study was to…
Descriptors: Error of Measurement, Statistical Analysis, Factor Analysis, Simulation
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Fish, Laurel J.; Halcoussis, Dennis; Phillips, G. Michael – American Journal of Business Education, 2017
The Monte Carlo method and related multiple imputation methods are traditionally used in math, physics and science to estimate and analyze data and are now becoming standard tools in analyzing business and financial problems. However, few sources explain the application of the Monte Carlo method for individuals and business professionals who are…
Descriptors: Statistical Analysis, Monte Carlo Methods, Spreadsheets, Simulation
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Andersson, Björn; Xin, Tao – Educational and Psychological Measurement, 2018
In applications of item response theory (IRT), an estimate of the reliability of the ability estimates or sum scores is often reported. However, analytical expressions for the standard errors of the estimators of the reliability coefficients are not available in the literature and therefore the variability associated with the estimated reliability…
Descriptors: Item Response Theory, Test Reliability, Test Items, Scores
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Man, Kaiwen; Harring, Jeffery R.; Ouyang, Yunbo; Thomas, Sarah L. – International Journal of Testing, 2018
Many important high-stakes decisions--college admission, academic performance evaluation, and even job promotion--depend on accurate and reliable scores from valid large-scale assessments. However, examinees sometimes cheat by copying answers from other test-takers or practicing with test items ahead of time, which can undermine the effectiveness…
Descriptors: Reaction Time, High Stakes Tests, Test Wiseness, Cheating
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Dimitrov, Dimiter M. – Measurement and Evaluation in Counseling and Development, 2017
This article offers an approach to examining differential item functioning (DIF) under its item response theory (IRT) treatment in the framework of confirmatory factor analysis (CFA). The approach is based on integrating IRT- and CFA-based testing of DIF and using bias-corrected bootstrap confidence intervals with a syntax code in Mplus.
Descriptors: Test Bias, Item Response Theory, Factor Analysis, Evaluation Methods
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Lee, Wooyeol; Cho, Sun-Joo – Applied Measurement in Education, 2017
Utilizing a longitudinal item response model, this study investigated the effect of item parameter drift (IPD) on item parameters and person scores via a Monte Carlo study. Item parameter recovery was investigated for various IPD patterns in terms of bias and root mean-square error (RMSE), and percentage of time the 95% confidence interval covered…
Descriptors: Item Response Theory, Test Items, Bias, Computation
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Moeyaert, Mariola; Ugille, Maaike; Ferron, John M.; Beretvas, S. Natasha; Van den Noortgate, Wim – Journal of Experimental Education, 2016
The impact of misspecifying covariance matrices at the second and third levels of the three-level model is evaluated. Results indicate that ignoring existing covariance has no effect on the treatment effect estimate. In addition, the between-case variance estimates are unbiased when covariance is either modeled or ignored. If the research interest…
Descriptors: Hierarchical Linear Modeling, Monte Carlo Methods, Computation, Statistical Bias
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Coulombe, Patrick; Selig, James P.; Delaney, Harold D. – International Journal of Behavioral Development, 2016
Researchers often collect longitudinal data to model change over time in a phenomenon of interest. Inevitably, there will be some variation across individuals in specific time intervals between assessments. In this simulation study of growth curve modeling, we investigate how ignoring individual differences in time points when modeling change over…
Descriptors: Individual Differences, Longitudinal Studies, Simulation, Change
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Guyon, Hervé; Tensaout, Mouloud – Measurement: Interdisciplinary Research and Perspectives, 2016
In this article, the authors extend the results of Aguirre-Urreta, Rönkkö, and Marakas (2016) concerning the omission of a relevant causal indicator by testing the validity of the assumption that causal indicators are entirely superfluous to the measurement model and discuss the implications for measurement theory. Contrary to common wisdom…
Descriptors: Causal Models, Structural Equation Models, Formative Evaluation, Measurement
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Olvera Astivia, Oscar L.; Zumbo, Bruno D. – Educational and Psychological Measurement, 2015
To further understand the properties of data-generation algorithms for multivariate, nonnormal data, two Monte Carlo simulation studies comparing the Vale and Maurelli method and the Headrick fifth-order polynomial method were implemented. Combinations of skewness and kurtosis found in four published articles were run and attention was…
Descriptors: Data, Simulation, Monte Carlo Methods, Comparative Analysis
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Jin, Ying; Eason, Hershel – Journal of Educational Issues, 2016
The effects of mean ability difference (MAD) and short tests on the performance of various DIF methods have been studied extensively in previous simulation studies. Their effects, however, have not been studied under multilevel data structure. MAD was frequently observed in large-scale cross-country comparison studies where the primary sampling…
Descriptors: Test Bias, Simulation, Hierarchical Linear Modeling, Comparative Analysis
Sweet, Tracy M. – Journal of Educational and Behavioral Statistics, 2015
Social networks in education commonly involve some form of grouping, such as friendship cliques or teacher departments, and blockmodels are a type of statistical social network model that accommodate these grouping or blocks by assuming different within-group tie probabilities than between-group tie probabilities. We describe a class of models,…
Descriptors: Social Networks, Statistical Analysis, Probability, Models
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Devlieger, Ines; Mayer, Axel; Rosseel, Yves – Educational and Psychological Measurement, 2016
In this article, an overview is given of four methods to perform factor score regression (FSR), namely regression FSR, Bartlett FSR, the bias avoiding method of Skrondal and Laake, and the bias correcting method of Croon. The bias correcting method is extended to include a reliable standard error. The four methods are compared with each other and…
Descriptors: Regression (Statistics), Comparative Analysis, Structural Equation Models, Monte Carlo Methods
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