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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
Multilevel modeling has become a common analytic technique across a variety of disciplines including medicine and the social and behavioral sciences. However, because many researchers who use multilevel modeling in their research do not report if the data were screened for potential violations of distributional assumptions and outliers, it is unclear if(More)
This study describes an investigation of the intellectual property policies of 42 public and private Carnegie Doctoral Research – Extensive Universities. Using a policy analysis framework based on earlier work by Lape (1992) and Packard (2001), policy differences between public and private universities and policy changes across time were analyzed and(More)
OBJECTIVES We assessed how frequently researchers reported the use of statistical techniques that take into account the complex sampling structure of survey data and sample weights in published peer-reviewed articles using data from 3 commonly used adolescent health surveys. METHODS We performed a systematic review of 1003 published empirical research(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)
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)
Whereas general sample size guidelines have been suggested when estimating multilevel models, they are only generalizable to a relatively limited number of data conditions and model structures, both of which are not very feasible for the applied researcher. In an effort to expand our understanding of two-level multilevel models under less than ideal(More)
Missing data are usually not the focus of any given study but researchers frequently encounter missing data when conducting empirical research. Missing data for Likert-type response scales, whose items are often combined to make summative scales, are particularly problematic because of the nature of the constructs typically measured, such as attitudes and(More)
This paper discusses a SAS® macro that provides three approaches to statistical inferences about Mahalanobis distance. Mahalanobis distance is useful as a multivariate effect size, being an extension of the standardized mean difference (i.e., Cohen's d). This program calculates three point estimates of D 2 (a sample estimate, a jackknife estimate, and an(More)