The present study compares the performance of phi coefficients and tetrachorics along two dimensions of factor recovery in binary data. These dimensions are (1) accuracy of nontrivial factor identifications; and (2) factor structure recovery given a priori knowledge of the correct number of factors to rotate. (Author/LMO)

This paper describes a variety of confirmatory factor analysis models that provide improved tests of multitrait-multimethod matrices, and compares three different approaches (the original Campbell-Fiske guidelines, an analysis of variance model, and confirmatory factor analysis models). (PN)

Canonical weights and structure correlations are used to construct low dimensional views of the relationships between two sets of variables. These views, in the form of biplots, display familiar statistics: correlations between pairs of variables, and regression coefficients. (SLD)

A modification of Horn's parallel analysis is introduced that is based on the Monte Carlo simulation of the null distributions of the eigenvalues generated from a population correlation identity matrix. This modification reduces the tendency of the parallel analysis procedure to overextract or to extract poorly defined factors. (SLD)

Several methods for the analysis of three-way data (data classified three ways) are described and shown to be variants of principal components analysis of the two-way supermatrix in which each two-way slice is strung out into a column vector. Direct fitting and fitting derived data are considered. (SLD)

The fundamental mathematical model of L. L. Thurstone's common factor analysis is reviewed, and basic covariance matrices of maximum likelihood factor analysis and alpha factor analysis are presented. The methods are compared in terms of computational and scaling contrasts. Weighting and the appropriate number of common factors are considered.…

A Computer program written in FORTRAN IV is presented which will create a population of desired size with marginally normal score vectors manifesting any desired centroid and covariance matrix. Uses and documentation are provided. (Author)

Many data analysis problems in psychology may be posed conveniently in terms which place the parameters to be estimated on one side of an equation and an expression in these parameters on the other side. A rule for improving the rate of convergence of the iterative solution of such equations is developed and applied to four problems. (Author/RC)

A constrained component analysis method, which bears a formal resemblance to the confirmatory factor analysis methods developed by K. G. Joreskog (1969) and others, is presented. In confirmatory factor analysis, the constraints allow the testing of formally structural hypotheses within a model that is falsifiable, even in its "just…

This study was undertaken to compare non-metric multidimensional scaling (MDS) and factor analysis (FA) as means of assessing dimensionality in relation to item response theory (IRT). FA assesses correlation matrices, while MDS performs an analysis of proximity measures. Seven data sets were generated; each differed from the others with respect to…

A stepwise canonical procedure, including two selection indices for variable deletion and a rule for stopping the iterative procedure, was derived as a method of selecting core variables from predictors and criteria. The procedure was applied to simulated data varying in the degree of built in structures in population correlation matrices, number…

A general purpose computer program for the calculation of a matrix of tetrachoric correlations is described. This program was developed for use in adaptive (and other) testing research for examining the unidimensionality assumption in latent trait theory, in conjunction with available factor analysis programs. Several other potential applications,…