Guttman's assumption underlying his definition of "total images" is rejected. Partial images are not generally convergent everywhere. Even divergence everywhere is shown to be possible. The convergence type always found on partial images is convergence in quadratic mean; hence, total images are redefined as quadratic mean-limits.…

The squared multiple correlation of a variable with the remaining variables in a variable set is shown to be a function of the communalities and the squared canonical correlations between the observed variables and common factors. (Author/JKS)

Bounds for the multiple correlation of common factors with the items which comprise those factors are developed. It is then shown that under broad, but not completely general, conditions, the circumstances under which an infinite item domain does or does not perfectly determine selected subsets of its common factors. (Author/JKS)

Problems in avoiding the singularity problem in analyzing matrices for optimal scaling are addressed. Conditions are given under which the stationary points and values of a ratio of quadratic forms in two singular matrices can be obtained by a series of simple matrix operations. (Author/JKS)

In order to make the parallel analysis criterion for determining the number of factors in factor analysis easy to use, regression equations for predicting the logarithms of the latent roots of random correlation matrices, with squared multiple correlations on the diagonal, are presented. (Author/JKS)

A simple roof is presented: that the squared multiple correlation of a variable with the remaining variables in the set of variables is a lower bound to the communality of that variable. (Author/JKS)

A summary and interpretation of the recent literature on the indeterminancy of factor scores is given in simple terms. A good index of factor score determinancy is the squared multiple correlation of the factor with the observed variables. (Author)

The relationship between the factor structure of a convariance matrix and the factor structure of a partial convariance matrix when one or more variables are partialled out of the original matrix is given in this brief note. (JKS)

Multivariate selection can be represented as a linear transformation in a geometric framework. In this note this approach is extended to describe the effects of selection on regression analysis and to adjust for the effects of selection using the inverse of the linear transformation. (Author/BW)

This note proposes an alternative implementation of the regression method which should be slightly faster than the principal components methods for estimating missing data. (RC)

Several procedures are outlined for replacing missing values in multivariate analyses by regression values obtained in various ways, and for adjusting coefficients (such as factor score coefficients) when data are missing. None of the procedures are complex or expensive. (Author)

Several themes which are common to both econometrics and psychometrics are surveyed. The themes are illustrated by reference to permanent income hypotheses, simultaneous equation models, adaptive expectations and partial adjustment schemes, and by reference to test score theory, factor analysis, and time-series models. (Author)

A theorem which gives the range of possible correlations between a common factor and an external variable (not contained in the factor analysis) is presented. Analogous expressions for component theory are also derived. (Author/JKS)

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 variety of algorithms for analyzing covariance structures are considered. Additionally, two methods of estimation, maximum likelihood, and weighted least squares are considered. Comparisons are made between these algorithms and factor analysis. (Author/JKS)