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Multivariate permutation tests are described and studied which may be profitably substituted for Hotelling's one-sample P test in situations commonly arising in behavioral science research. These tests (a) may be computed even when the number of variables exceeds the number of subjects, (b) are distribution-free, (c) may be tailored for sensitivity to… (More)

Introduction As the use of multilevel models has expanded into new areas, questions have emerged concerning how well these models work under various design conditions Sample size at each level of analysis continues to be an important design condition in multilevel modeling Background

We conducted two experiments examining the effects of a self-evaluation package on the peer interactions of students described as emotionally or behaviorally disordered. Experiment 1 assessed the additive effects of various components of a self-evaluation package on the frequency of inappropriate and appropriate peer interactions. The components assessed… (More)

This article uses meta-analyses published in Psychological Bulletin from 1995 to 2005 to describe meta-analyses in psychology, including examination of statistical power, Type I errors resulting from multiple comparisons, and model choice. Retrospective power estimates indicated that univariate categorical and continuous moderators, individual moderators in… (More)

Measures of effect size are recommended to communicate information on the strength of relationships. Such information supplements the reject/fail-to-reject decision obtained in statistical hypothesis testing. Because sample effect sizes are subject to sampling error, as is any sample statistic, computing confidence intervals for these statistics is a useful… (More)

Sample size at each level is important to consider when estimating multilevel models. Although general sample size guidelines have been suggested, the nature of social science survey research (e.g., large number of level-2 units with few individuals per unit) often makes such recommendations difficult to follow. This Monte Carlo study focuses on the… (More)

- Jeffrey D Kromrey, Kristine Y Hogarty, John M Ferron, Constance V Hines, Melinda R Hess, Keselman +1 other
- 2005

KEYWORDS: meta-analysis, robustness, effect size, Monte Carlo study With the growing popularity of meta-analytic techniques to analyze and synthesize results across sets of empirical studies, have come concerns about the sensitivity of traditional tests in meta-analysis to violations of assumptions. This is particularly distressing because the tenability of… (More)

Although statistical power is often considered in the design of primary research studies, it is rarely considered in meta-analysis. Background and guidelines are provided for conducting power analysis in meta-analysis, followed by the presentation of a SAS macro that calculates power using the methods described by Hedges and Pigott (2001, 2004). Several… (More)

- Rheta E Lanehart, Patricia Rodriguez De Gil, Eun Sook Kim, Aarti P Bellara, Jeffrey D Kromrey, Reginald S Lee
- 2012

Propensity score analysis is frequently used to reduce the potential bias in estimated effects obtained from observational studies. Appropriate implementation of propensity score adjustments is a multi-step process presenting many alternatives for researchers in terms of estimation and conditioning methods. Further, evaluation of the sample data after… (More)