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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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Murayama, Kou; Sakaki, Michiko; Yan, Veronica X.; Smith, Garry M. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2014
In order to examine metacognitive accuracy (i.e., the relationship between metacognitive judgment and memory performance), researchers often rely on by-participant analysis, where metacognitive accuracy (e.g., resolution, as measured by the gamma coefficient or signal detection measures) is computed for each participant and the computed values are…
Descriptors: Metacognition, Memory, Accuracy, Statistical Analysis
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Manolov, Rumen; Solanas, Antonio; Bulte, Isis; Onghena, Patrick – Journal of Experimental Education, 2010
This study deals with the statistical properties of a randomization test applied to an ABAB design in cases where the desirable random assignment of the points of change in phase is not possible. To obtain information about each possible data division, the authors carried out a conditional Monte Carlo simulation with 100,000 samples for each…
Descriptors: Monte Carlo Methods, Effect Size, Simulation, Evaluation Methods
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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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Hummel, Thomas J.; Feltovich, Paul J. – Multivariate Behavioral Research, 1975
Monte Carlo methods were used to investigate the robustness of techniques used in judging the magnitude of a sample correlation coefficient when observations are correlated. Empirical distributions of r, t, and Fisher's z were generated. A technique for controlling error rates in certain situations is suggested. (Author/BJG)
Descriptors: Computer Science, Correlation, Error Patterns, Monte Carlo Methods
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Kromrey, Jeffrey D.; Rendina-Gobioff, Gianna – Educational and Psychological Measurement, 2006
The performance of methods for detecting publication bias in meta-analysis was evaluated using Monte Carlo methods. Four methods of bias detection were investigated: Begg's rank correlation, Egger's regression, funnel plot regression, and trim and fill. Five factors were included in the simulation design: number of primary studies in each…
Descriptors: Comparative Analysis, Meta Analysis, Monte Carlo Methods, Correlation
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Chan, Wai; Chan, Daniel W.-L. – Psychological Methods, 2004
The standard Pearson correlation coefficient is a biased estimator of the true population correlation, ?, when the predictor and the criterion are range restricted. To correct the bias, the correlation corrected for range restriction, r-sub(c), has been recommended, and a standard formula based on asymptotic results for estimating its standard…
Descriptors: Computation, Intervals, Sample Size, Monte Carlo Methods
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Field, Andy P. – Psychological Methods, 2005
One conceptualization of meta-analysis is that studies within the meta-analysis are sampled from populations with mean effect sizes that vary (random-effects models). The consequences of not applying such models and the comparison of different methods have been hotly debated. A Monte Carlo study compared the efficacy of Hedges and Vevea's…
Descriptors: Meta Analysis, Correlation, Effect Size, Models