Search

The many methodological contributions of Raymond B. Cattell to multivariate analysis are discussed in terms of contemporary issues in structural equation modeling. His factor analytic approach is compared with current modeling practices. A critical evaluation finds much of Cattell's work still innovative, technically advanced, and valuable to contemporary model builders. (Author/BS)

In the space available for this essay I can provide only a cursory review of the many methodological contributions of R. B. Cattell. I have chosen to look at these from the vantage point of contemporary issues in structural equation modeling. Cattell's factor analytic approach is compared With current modeling practices. A critical evaluation is offered which finds much of Cattell's work still innovative, still technically advanced, and still of great value to contemporary model builders. [ABSTRACT FROM AUTHOR]

This article presents some benefits and limitations of structural equation models for multivariate experiments with incomplete data. Examples from studies of latent variable path models of cognitive performances illustrate analyses with four different kinds of incomplete data: (a) latent variables, (b) omitted variables, (c) randomly missing data, and (d) non- randomly missing data. Power based cost-benefit analyses for experimental design and planning are also presented. These incomplete data approaches are closely related to models used in classical experimental design, interbattery measurement analysis, longitudinal analyses, and behavioral genetic analyses. These structural equation methods for old experimental design problems indicate some new opportunities for future multivariate research.

Some problems of multiple group factor rotation based on Cattell's "parallel proportional profiles" and "confactor rotation" are described (see Cattell, 1944, 1966, 1972). Some relations between these classic ideas and contemporary practices in structural equation modeling (e.g., LISREL) are explored. We show how the Confactor approach: (a) is related to Meredith's (1964a) selection model, (b) can be a parsimonious model for multiple group factor analyses, and (c) how this model can be fitted using standard structural equation modeling techniques. We discuss several alternative structural modeling solutions, including (d) selection of a good reference variable solution, (e) rotation of the invariant orthogonal structure by standard rotation routines, and (f) higher-order, latent paths, and latent means structural model restrictions. Mathematical and statistical properties of these models are examined using Meredith's (1964b) four group problem fitted by Jdreskog and Sdrbom's (1979, 1985) LlSREL algorithm. The benefits and limitations of this structural modeling approach to oblique Confactor resolution are examined and opportunities for future research are discussed.