It is shown that, under very general conditions, uniqueness estimates proposed independently by Guttman (1957) and by Harris (1963) provide tighter upper bounds on the unknown uniqueness values of factor analysis than do existing estimates. (Editor)

This paper made an important distinction between determinacy of common-factors and determinacy of unique-factors and examined the implications of two results previously obtained by Guttman. (Author/RK)

The differences in concept formation skills between middle and working class children in New Zealand are discussed. (CK)

Three-mode factor analysis (3MFA) and PARAFAC are methods that describe three-way data. A class of 3MFA models is introduced that falls between 3MFA and PARAFAC and contains the good properties of both approaches, including the unique axes property that has distinguished the PARAFAC model. (SLD)

Proposes an index for assessing the degree of factor simplicity in the context of principal components and exploratory factor analysis. The index does not depend on the scale of the factors, and its maximum and minimum are related only to the degree of simplicity in the loading matrix. (SLD)

Developed a general framework to estimate total test or testlet reliability with either fixed or random factors that is based on hierarchical factor analysis. Illustrates the framework with an example based on real data. (SLD)

Influence curves of some parameters under various methods of factor analysis depend on the influence curves for either the covariance or the correlation matrix used in the analysis. The differences between the two types of curves are derived, and simple formulas for the differences are presented. (SLD)

Investigated the conditions under which the matrix of factor loadings from the factor analysis model with equal unique variances will give a good approximation to the matrix of factor loadings from the regular factor analysis model. Extends the results to the image factor analysis model. Discusses implications for practice. (SLD)

General formulas are derived for assessing local influence under restrictions in which the first derivatives are still zeros, and then these results are applied to factor analysis, as the usually used restriction in factor analysis satisfies the conditions. (SLD)

Presents an approach for obtaining principal planes in factor analysis, extending the idea of rotation to simple structure to two dimensions. The principal planes give a better view of the data than planes derived from classical rotation and thus allow more reliable interpretation. (SLD)