ABSTRACT
Repeated measures designs should be used more frequently in prevention intervention research. They are the design of choice when one or more measurements have been taken at baseline followed by one or more measurements after prevention intervention. They may be used to ask questions about differences on measurements at different points in time and between measures made on the same scale. In this presentation, prevention intervention researchers are provided with a step-by-step discussion of this design, examples of prevention intervention repeated measures designs, and a discussion of the misuses of this design.
Subject(s)
Data Interpretation, Statistical , Research Design , Substance-Related Disorders/prevention & control , Analysis of Variance , Humans , Software , Substance-Related Disorders/epidemiologyABSTRACT
Robert M. Thorndike (1976) commented on the results of a Monte Carlo study on the stability of canonical correlations, canonical weights, and canonical variate-variable correlations (Barcikowski and Stevens, 1975). In this paper each of his comments are examined by the authors of the Monte Carlo Study. In addition, a possible solution to the large number of subjects necessary for stable weights and variate-variable correlations using ridge regression procedures is suggested.
ABSTRACT
A Monte Carlo study was run to check the stability of canonical correlations, canonical weights, and canonical variate-variable correlations. Eight data matrices were selected from the literature for the canonical analyses, with the number of variables ranging from 7 to 41. The results showed that the canonical correlations are very stable upon replication. The results also indicated that there is no solid evidence for concluding that the components are superior to the coefficients, a t least not in terms of being more reliable. However, the number of subjects per variable necessary to achieve re1i:tbility in detecting the most important variables, using components or coefficients, was quite large, ranging from 42/1 to 68/1.
