The Experts below are selected from a list of 15 Experts worldwide ranked by ideXlab platform
Randall J. Penfield - One of the best experts on this subject based on the ideXlab platform.
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An Alternative to Cohen's Standardized Mean Difference Effect Size: A Robust Parameter and Confidence Interval in the Two Independent Groups Case.
Psychological Methods, 2005Co-Authors: James Algina, H. J. Keselman, Randall J. PenfieldAbstract:The authors argue that a robust version of Cohen's effect size constructed by replacing population means with 20% trimmed means and the population standard deviation with the square root of a 20% Winsorized Variance is a better measure of population separation than is Cohen's effect size. The authors investigated coverage probability for confidence intervals for the new effect size measure. The confidence intervals were constructed by using the noncentral t distribution and the percentile bootstrap. Over the range of distributions and effect sizes investigated in the study, coverage probability was better for the percentile bootstrap confidence interval.
James Algina - One of the best experts on this subject based on the ideXlab platform.
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An Alternative to Cohen's Standardized Mean Difference Effect Size: A Robust Parameter and Confidence Interval in the Two Independent Groups Case.
Psychological Methods, 2005Co-Authors: James Algina, H. J. Keselman, Randall J. PenfieldAbstract:The authors argue that a robust version of Cohen's effect size constructed by replacing population means with 20% trimmed means and the population standard deviation with the square root of a 20% Winsorized Variance is a better measure of population separation than is Cohen's effect size. The authors investigated coverage probability for confidence intervals for the new effect size measure. The confidence intervals were constructed by using the noncentral t distribution and the percentile bootstrap. Over the range of distributions and effect sizes investigated in the study, coverage probability was better for the percentile bootstrap confidence interval.
H. J. Keselman - One of the best experts on this subject based on the ideXlab platform.
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An Alternative to Cohen's Standardized Mean Difference Effect Size: A Robust Parameter and Confidence Interval in the Two Independent Groups Case.
Psychological Methods, 2005Co-Authors: James Algina, H. J. Keselman, Randall J. PenfieldAbstract:The authors argue that a robust version of Cohen's effect size constructed by replacing population means with 20% trimmed means and the population standard deviation with the square root of a 20% Winsorized Variance is a better measure of population separation than is Cohen's effect size. The authors investigated coverage probability for confidence intervals for the new effect size measure. The confidence intervals were constructed by using the noncentral t distribution and the percentile bootstrap. Over the range of distributions and effect sizes investigated in the study, coverage probability was better for the percentile bootstrap confidence interval.
Penfield, Randall D. - One of the best experts on this subject based on the ideXlab platform.
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Confidence Intervals For An Effect Size When Variances Are Not Equal
DigitalCommons@WayneState, 2006Co-Authors: Algina James, Keselman H. J., Penfield, Randall D.Abstract:Confidence intervals must be robust in having nominal and actual probability coverage in close agreement. This article examined two ways of computing an effect size in a two-group problem: (a) the classic approach which divides the mean difference by a single standard deviation and (b) a variant of a method which replaces least squares values with robust trimmed means and a Winsorized Variance. Confidence intervals were determined with theoretical and bootstrap critical values. Only the method that used robust estimators and a bootstrap critical value provided generally accurate probability coverage under conditions of nonnormality and Variance heterogeneity in balanced as well as unbalanced designs
Algina James - One of the best experts on this subject based on the ideXlab platform.
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Confidence Intervals For An Effect Size When Variances Are Not Equal
DigitalCommons@WayneState, 2006Co-Authors: Algina James, Keselman H. J., Penfield, Randall D.Abstract:Confidence intervals must be robust in having nominal and actual probability coverage in close agreement. This article examined two ways of computing an effect size in a two-group problem: (a) the classic approach which divides the mean difference by a single standard deviation and (b) a variant of a method which replaces least squares values with robust trimmed means and a Winsorized Variance. Confidence intervals were determined with theoretical and bootstrap critical values. Only the method that used robust estimators and a bootstrap critical value provided generally accurate probability coverage under conditions of nonnormality and Variance heterogeneity in balanced as well as unbalanced designs
