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Direct linkEric C. Hedberg – Grantee Submission, 2023
In cluster randomized evaluations, a treatment or intervention is randomly assigned to a set of clusters each with constituent individual units of observations (e.g., student units that attend schools, which are assigned to treatment). One consideration of these designs is how many units are needed per cluster to achieve adequate statistical…
Descriptors: Statistical Analysis, Multivariate Analysis, Randomized Controlled Trials, Research Design
E. C. Hedberg – American Journal of Evaluation, 2023
In cluster randomized evaluations, a treatment or intervention is randomly assigned to a set of clusters each with constituent individual units of observations (e.g., student units that attend schools, which are assigned to treatment). One consideration of these designs is how many units are needed per cluster to achieve adequate statistical…
Descriptors: Statistical Analysis, Multivariate Analysis, Randomized Controlled Trials, Research Design
Kim, Rae-Seon; Becker, Betsy Jane – Multivariate Behavioral Research, 2010
We examined the degree of dependence between standardized-mean-difference effect sizes in multiple-treatment studies in meta-analysis in terms of the correlation formula provided by Gleser and Olkin (1994). To explore the impact of group size and the values of the true multiple-treatment effect sizes, we simplified the formula for the correlation…
Descriptors: Effect Size, Meta Analysis, Correlation, Control Groups
Peer reviewedDawson-Sunders, Beth K. – Educational and Psychological Measurement, 1982
The canonical redundancy statistic, an estimate of the amount of shared variance between two sets of variables, exhibits an amount of bias similar to that of the first squared canonical correlation coefficient. Two formulae, Wherry and Olkin-Pratt, adequately correct the bias of the redundancy statistic. (Author/BW)
Descriptors: Correlation, Mathematical Formulas, Multivariate Analysis, Statistical Bias
Rosenthal, James A. – Springer, 2011
Written by a social worker for social work students, this is a nuts and bolts guide to statistics that presents complex calculations and concepts in clear, easy-to-understand language. It includes numerous examples, data sets, and issues that students will encounter in social work practice. The first section introduces basic concepts and terms to…
Descriptors: Statistics, Data Interpretation, Social Work, Social Science Research
Huberty, Carl J.; Smith, Jerry D. – 1980
Linear classification functions (LCFs) arise in a predictive discriminant analysis for the purpose of classifying experimental units into criterion groups. The relative contribution of the response variables to classification accuracy may be based on LCF-variable correlations for each group. It is proved that, if the raw response measures are…
Descriptors: Analysis of Covariance, Classification, Correlation, Criteria
