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Showing 1 to 15 of 33 results Save | Export
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Tugay Kaçak; Abdullah Faruk Kiliç – International Journal of Assessment Tools in Education, 2025
Researchers continue to choose PCA in scale development and adaptation studies because it is the default setting and overestimates measurement quality. When PCA is utilized in investigations, the explained variance and factor loadings can be exaggerated. PCA, in contrast to the models given in the literature, should be investigated in…
Descriptors: Factor Analysis, Monte Carlo Methods, Mathematical Models, Sample Size
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Wang, Yan; Kim, Eunsook; Joo, Seang-Hwane; Chun, Seokjoon; Alamri, Abeer; Lee, Philseok; Stark, Stephen – Journal of Experimental Education, 2022
Multilevel latent class analysis (MLCA) has been increasingly used to investigate unobserved population heterogeneity while taking into account data dependency. Nonparametric MLCA has gained much popularity due to the advantage of classifying both individuals and clusters into latent classes. This study demonstrated the need to relax the…
Descriptors: Nonparametric Statistics, Hierarchical Linear Modeling, Monte Carlo Methods, Simulation
Kush, Joseph M.; Konold, Timothy R.; Bradshaw, Catherine P. – Grantee Submission, 2021
Multilevel structural equation (MSEM) models allow researchers to model latent factor structures at multiple levels simultaneously by decomposing within- and between-group variation. Yet the extent to which the sampling ratio (i.e., proportion of cases sampled from each group) influences the results of MSEM models remains unknown. This paper…
Descriptors: Sampling, Structural Equation Models, Factor Structure, Monte Carlo Methods
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Sinharay, Sandip – Journal of Educational Measurement, 2016
De la Torre and Deng suggested a resampling-based approach for person-fit assessment (PFA). The approach involves the use of the [math equation unavailable] statistic, a corrected expected a posteriori estimate of the examinee ability, and the Monte Carlo (MC) resampling method. The Type I error rate of the approach was closer to the nominal level…
Descriptors: Sampling, Research Methodology, Error Patterns, Monte Carlo Methods
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Bishara, Anthony J.; Hittner, James B. – Educational and Psychological Measurement, 2015
It is more common for educational and psychological data to be nonnormal than to be approximately normal. This tendency may lead to bias and error in point estimates of the Pearson correlation coefficient. In a series of Monte Carlo simulations, the Pearson correlation was examined under conditions of normal and nonnormal data, and it was compared…
Descriptors: Research Methodology, Monte Carlo Methods, Correlation, Simulation
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Pantelis, Peter C.; Kennedy, Daniel P. – Autism: The International Journal of Research and Practice, 2016
Two-phase designs in epidemiological studies of autism prevalence introduce methodological complications that can severely limit the precision of resulting estimates. If the assumptions used to derive the prevalence estimate are invalid or if the uncertainty surrounding these assumptions is not properly accounted for in the statistical inference…
Descriptors: Foreign Countries, Pervasive Developmental Disorders, Autism, Incidence
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Schoemann, Alexander M.; Miller, Patrick; Pornprasertmanit, Sunthud; Wu, Wei – International Journal of Behavioral Development, 2014
Planned missing data designs allow researchers to increase the amount and quality of data collected in a single study. Unfortunately, the effect of planned missing data designs on power is not straightforward. Under certain conditions using a planned missing design will increase power, whereas in other situations using a planned missing design…
Descriptors: Monte Carlo Methods, Simulation, Sample Size, Research Design
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Shieh, Gwowen; Jan, Show-Li – Journal of Experimental Education, 2013
The authors examined 2 approaches for determining the required sample size of Welch's test for detecting equality of means when the greatest difference between any 2 group means is given. It is shown that the actual power obtained with the sample size of the suggested approach is consistently at least as great as the nominal power. However, the…
Descriptors: Sampling, Statistical Analysis, Computation, Research Methodology
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Van Horn, M. Lee; Smith, Jessalyn; Fagan, Abigail A.; Jaki, Thomas; Feaster, Daniel J.; Masyn, Katherine; Hawkins, J. David; Howe, George – Structural Equation Modeling: A Multidisciplinary Journal, 2012
Regression mixture models, which have only recently begun to be used in applied research, are a new approach for finding differential effects. This approach comes at the cost of the assumption that error terms are normally distributed within classes. This study uses Monte Carlo simulations to explore the effects of relatively minor violations of…
Descriptors: Structural Equation Models, Home Management, Drug Abuse, Research Methodology
Dong, Nianbo – Society for Research on Educational Effectiveness, 2011
The purpose of this study is through Monte Carlo simulation to compare several propensity score methods in approximating factorial experimental design and identify best approaches in reducing bias and mean square error of parameter estimates of the main and interaction effects of two factors. Previous studies focused more on unbiased estimates of…
Descriptors: Research Design, Probability, Monte Carlo Methods, Simulation
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Young, Michael E.; Clark, M. H.; Goffus, Andrea; Hoane, Michael R. – Learning and Motivation, 2009
Morris water maze data are most commonly analyzed using repeated measures analysis of variance in which daily test sessions are analyzed as an unordered categorical variable. This approach, however, may lack power, relies heavily on post hoc tests of daily performance that can complicate interpretation, and does not target the nonlinear trends…
Descriptors: Monte Carlo Methods, Regression (Statistics), Research Methodology, Simulation
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Murphy, Daniel L.; Pituch, Keenan A. – Journal of Experimental Education, 2009
The authors examined the robustness of multilevel linear growth curve modeling to misspecification of an autoregressive moving average process. As previous research has shown (J. Ferron, R. Dailey, & Q. Yi, 2002; O. Kwok, S. G. West, & S. B. Green, 2007; S. Sivo, X. Fan, & L. Witta, 2005), estimates of the fixed effects were unbiased, and Type I…
Descriptors: Sample Size, Computation, Evaluation Methods, Longitudinal Studies
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de Winter, J. C. F.; Dodou, D.; Wieringa, P. A. – Multivariate Behavioral Research, 2009
Exploratory factor analysis (EFA) is generally regarded as a technique for large sample sizes ("N"), with N = 50 as a reasonable absolute minimum. This study offers a comprehensive overview of the conditions in which EFA can yield good quality results for "N" below 50. Simulations were carried out to estimate the minimum required "N" for different…
Descriptors: Sample Size, Factor Analysis, Enrollment, Evaluation Methods
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Jo, Booil; Asparouhov, Tihomir; Muthen, Bengt O.; Ialongo, Nicholas S.; Brown, C. Hendricks – Psychological Methods, 2008
Cluster randomized trials (CRTs) have been widely used in field experiments treating a cluster of individuals as the unit of randomization. This study focused particularly on situations where CRTs are accompanied by a common complication, namely, treatment noncompliance or, more generally, intervention nonadherence. In CRTs, compliance may be…
Descriptors: Individual Characteristics, Intervention, Statistical Inference, Inferences
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Cheung, Shu Fai; Chan, Darius K.-S. – Educational and Psychological Measurement, 2008
In meta-analysis, it is common to have dependent effect sizes, such as several effect sizes from the same sample but measured at different times. Cheung and Chan proposed the adjusted-individual and adjusted-weighted procedures to estimate the degree of dependence and incorporate this estimate in the meta-analysis. The present study extends the…
Descriptors: Effect Size, Academic Achievement, Meta Analysis, Correlation
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