Evaluating the fit of a structural equation model via bootstrap requires a transformation of the data so that the null hypothesis holds exactly in the sample. For complete data, such a transformation was proposed by Beran and Srivastava (1985) for general covariance structure models and applied to structural equation modeling by Bollen and Stine…

It is argued that ordinal statistical methods are often more appropriate than their more common counterparts because conclusions will be unaffected by monotonic transformation of the variables; they are more statistically robust when used appropriately; and they often correspond more closely to the researcher's goals. (SLD)

The problem of correlating variables from different scale types is discussed. A general correlation coefficient, based on symmetrization theory, is derived. The coefficient is invariant over permitted transformations of the variables for their respective (possibly nonequivalent) scale types. (Author/JKS)

The general transformation approach to time series analysis is extended to the analysis of multiple unit data by the development of a patterned transformation matrix. The procedure includes alternatives for special cases and requires only minor revisions in existing computer software. (SLD)

Outlined is a model for transformation of one factor matrix to congruence with a second or target matrix in which the correlations among the transformed factors are constrained to certain pre-specified values. Procedures are developed for implementing the model, and are illustrated with example factor solutions. (Author/RC)

Populations of factorially simple and complex data were generated with first the oblique and orthogonal factor models, and then solutions based on special cases of the general orthomax criterion were compared on the basis of these characteristics. The results are discussed and implications noted. (DEP)

A method is proposed for partially analyzing additive ipsative data (PAID). Transforming the PAID according to a developed equation preserves the density of the transformed data, and maximum likelihood estimation can be carried out as usual. Simulation results show that the original structural parameters can be accurately estimated from PAID. (SLD)