two basic types are R type analysis which involves the correlation of pairs of scale items, and Q type analysis which involves the correlation of pairs of individuals.
Factor analytic techniques may be grouped into principal component procedures, and maximum likelihood procedures (Emory, 1992, p. 555). The maximum likelihood method of factor analysis is a procedure that obtains the best estimates of factor loadings from information provided by the correlation matrix, and is based on an assumption that the variable is explained by the designated number of factors. Principal component procedures are developed within the framework of the multivariate linear model.
Multivariate analysis procedures includes those techniques that focus on the structure of simultaneous relationships among three or more variables (Emory, 1992, p. 554). There is a wide variety of mulitvariate procedures available to the researcher. The choice among these techniques depends in part on the focus of the statistical analysis--dependence or independence. Multivariate tests of dependence include multiple regression, discriminate analysis, canonical analysis, multiple classification analysis, automatic interaction detection, and multivariate analysis of variance. Multivariate tests of independence include multidimensional scaling, latent structure analysis, latent class analysis, cluster analysis, and two variants of factor analysis.
Multivariate analysis, therefore, need not be an alternative to factor analysis because two variants of factor analysis are multivariate in character (Emory, 1992, pp. 559). Nevertheless, ther
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