Abstract #302304

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JSM 2003 Abstract #302304
Activity Number: 248
Type: Contributed
Date/Time: Tuesday, August 5, 2003 : 10:30 AM to 12:20 PM
Sponsor: General Methodology
Abstract - #302304
Title: Approaches to Structural Forms Analysis: A Comparison of Canonical Correlation and Structural Equations Modeling
Author(s): Kent A. Hendrix*+ and Suzanne Hendrix and Bruce L. Brown
Companies: NPS Pharmaceuticals and Brigham Young University and Brigham Young University
Address: 420 Chipeta Way, Salt Lake City, UT, 84108,
Keywords: SEM ; graphics ; structural forms analysis ; canonical correlation ; structural equations modeling
Abstract:

This paper demonstrates an emerging approach to data analysis: structural forms analysis. SFA uses the algorithms of traditional multivariate statistics, but to discover structure rather than to test for significance. Two methods will be compared, one based upon canonical correlation and one based upon structural equations modeling. Canonical correlation and SEM are similar in that both are methods for relating multiple sets of multivariate data to one another. SEM is both more general and also more malleable, and would seem to be the stronger of the two methods for the kind of multivariate graphing tasks called for in the structural forms approach to data. However, there are some surprising advantages of the simpler and more conservative mathematics of the older method. The two methods will be compared in analyzing a variety of "strong datasets," where strong is defined as either being experimentally tight, or having well defined internal structure, in addition to having at least two groups of dependent variables. The results will be discussed in the light of recent calls for "enhanced graphicacy" among scientists (Smith, et al., 2002).

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