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Doebler, Philipp; Holling, Heinz; Bohning, Dankmar – Psychological Methods, 2012
We propose 2 related models for the meta-analysis of diagnostic tests. Both models are based on the bivariate normal distribution for transformed sensitivities and false-positive rates. Instead of using the logit as a transformation for these proportions, we employ the "t"[subscript alpha] family of transformations that contains the log, logit,…
Descriptors: Models, Meta Analysis, Diagnostic Tests, Comparative Analysis
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Cooper, Harris; Patall, Erika A. – Psychological Methods, 2009
The authors describe the relative benefits of conducting meta-analyses with (a) individual participant data (IPD) gathered from the constituent studies and (b) aggregated data (AD), or the group-level statistics (in particular, effect sizes) that appear in reports of a study's results. Given that both IPD and AD are equally available,…
Descriptors: Data Analysis, Meta Analysis, Databases, Coordination
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Le, Huy; Schmidt, Frank L. – Psychological Methods, 2006
Using computer simulation, the authors assessed the accuracy of J. E. Hunter, F. L. Schmidt, and H. Le's (2006) procedure for correcting for indirect range restriction, the most common type of range restriction, in comparison with the conventional practice of applying the Thorndike Case II correction for direct range restriction. Hunter et…
Descriptors: Computer Simulation, Predictor Variables, Correlation, Computation
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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
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Huedo-Medina, Tania B.; Sanchez-Meca, Julio; Marin-Martinez, Fulgencio; Botella, Juan – Psychological Methods, 2006
In meta-analysis, the usual way of assessing whether a set of single studies is homogeneous is by means of the Q test. However, the Q test only informs meta-analysts about the presence versus the absence of heterogeneity, but it does not report on the extent of such heterogeneity. Recently, the I[squared] index has been proposed to quantify the…
Descriptors: Meta Analysis, Intervals, Monte Carlo Methods, Comparative Analysis