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Aydin, Burak; Leite, Walter L.; Algina, James – Educational and Psychological Measurement, 2016
We investigated methods of including covariates in two-level models for cluster randomized trials to increase power to detect the treatment effect. We compared multilevel models that included either an observed cluster mean or a latent cluster mean as a covariate, as well as the effect of including Level 1 deviation scores in the model. A Monte…
Descriptors: Error of Measurement, Predictor Variables, Randomized Controlled Trials, Experimental Groups
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Algina, James; Keselman, H. J.; Penfield, Randall D. – Educational and Psychological Measurement, 2010
The increase in the squared multiple correlation coefficient ([delta]R[superscript 2]) associated with a variable in a regression equation is a commonly used measure of importance in regression analysis. Algina, Keselman, and Penfield found that intervals based on asymptotic principles were typically very inaccurate, even though the sample size…
Descriptors: Computation, Statistical Analysis, Correlation, Statistical Inference
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Algina, James; Keselman, Harvey J.; Penfield, Randall J. – Educational and Psychological Measurement, 2008
A squared semipartial correlation coefficient ([Delta]R[superscript 2]) is the increase in the squared multiple correlation coefficient that occurs when a predictor is added to a multiple regression model. Prior research has shown that coverage probability for a confidence interval constructed by using a modified percentile bootstrap method with…
Descriptors: Intervals, Correlation, Probability, Multiple Regression Analysis
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Algina, James; Keselman, H. J. – Educational and Psychological Measurement, 2008
Applications of distribution theory for the squared multiple correlation coefficient and the squared cross-validation coefficient are reviewed, and computer programs for these applications are made available. The applications include confidence intervals, hypothesis testing, and sample size selection. (Contains 2 tables.)
Descriptors: Intervals, Sample Size, Validity, Hypothesis Testing
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Algina, James; Keselman, H. J.; Penfield, Randall D. – Educational and Psychological Measurement, 2007
The increase in the squared multiple correlation coefficient ([Delta]R[squared]) associated with a variable in a regression equation is a commonly used measure of importance in regression analysis. The coverage probability that an asymptotic and percentile bootstrap confidence interval includes [Delta][rho][squared] was investigated. As expected,…
Descriptors: Probability, Intervals, Multiple Regression Analysis, Correlation
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Algina, James; Keselman, H. J.; Penfield, Randall D. – Educational and Psychological Measurement, 2006
Kelley compared three methods for setting a confidence interval (CI) around Cohen's standardized mean difference statistic: the noncentral-"t"-based, percentile (PERC) bootstrap, and biased-corrected and accelerated (BCA) bootstrap methods under three conditions of nonnormality, eight cases of sample size, and six cases of population…
Descriptors: Effect Size, Comparative Analysis, Sample Size, Investigations
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Algina, James; Keselman, H. J.; Penfield, Randall D. – Educational and Psychological Measurement, 2005
Probability coverage for eight different confidence intervals (CIs) of measures of effect size (ES) in a two-level repeated measures design was investigated. The CIs and measures of ES differed with regard to whether they used least squares or robust estimates of central tendency and variability, whether the end critical points of the interval…
Descriptors: Probability, Intervals, Least Squares Statistics, Effect Size
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Algina, James; Keselman, H. J. – Educational and Psychological Measurement, 2003
Investigated the approximate confidence intervals for effect sizes developed by K. Bird (2002) and proposed a more accurate method developed through simulation studies. The average coverage probability for the new method was 0.959. (SLD)
Descriptors: Effect Size, Research Methodology, Simulation
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Kowalchuk, Rhonda K.; Keselman, H. J.; Algina, James; Wolfinger, Russell D. – Educational and Psychological Measurement, 2004
One approach to the analysis of repeated measures data allows researchers to model the covariance structure of their data rather than presume a certain structure, as is the case with conventional univariate and multivariate test statistics. This mixed-model approach, available through SAS PROC MIXED, was compared to a Welch-James type statistic.…
Descriptors: Interaction, Sample Size, Statistical Analysis, Evaluation Methods