A general linear model framework is used to suggest that structure coefficients ought to be interpreted in structural equation modeling confirmatory factor analysis (CFA) studies in which factors are correlated. Two heuristic data sets make the discussion concrete, and two additional studies illustrate the benefits of CFA structure coefficients.…

Presents a construction method for all factors that satisfy the assumptions of the model for factor analysis, including partially determined factors where certain error variances are zero. Illustrates that variable elimination can have a large effect on the seriousness of factor indeterminacy. (SLD)

Provides example applications of constrained principal component analysis (CPCA) that illustrate the method on a variety of contexts common to psychological research. Two new analyses, decompositions into finer components and fitting higher order structures, are presented, followed by an illustration of CPCA on contingency tables and the CPCA of…

Third step in development of perceptual inventory of factors associated with marijuana use involved administering inventory to 60 parents who were members of community anti-drug group. Factor analysis revealed 5-factor solution that used all 34 items. Scales were labeled Parental Limitations, Societal Issues, Inherent Predispositions,…

Properties of dichotomous Guttman-scalable items are described. Both the elements and eigenvalues of the Pearson correlation matrix of such items can be expressed as simple functions of the number of items if the score distribution is uniform and there is an equal number of items at each difficulty level. (SLD)

A method of assessing goodness-of-fit for a single factor model is presented. Indices of fit sensitive to the way that correlation matrices are generated are derived from the factor analysis literature. It is proposed that the cumulative distribution function be evaluated for other values of "p" and "m." (TJH)

A cross-validation method is described for selecting the significant components from a principal components analysis, and properties of the method are discussed. Parallels are drawn with other related methods in covariance structure modeling, and some comparisons among methods are illustrated with two data sets previously analyzed. (SLD)

Some problems of multiple-group factor rotation based on the parallel proportional profiles and confactor rotation of R. B. Cattell are described, and several alternative modeling solutions are proposed. Benefits and limitations of the structural-modeling approach to oblique confactor resolution are examined, and opportunities for research are…

In matrix comparison, the performance of four vector matching indices (the coefficient of congruence, the Pearson product moment correlation, the "s"-statistic, and kappa) was evaluated. Advantages and disadvantages of each index are discussed, and the performance of each was assessed within the framework of principal components…

PARAFAC is a generalization of principal components analysis in a factor score matrix and in a factor loadings matrix. How PARAFAC behaves when applied to positive manifold data is examined, and a constrained PARAFAC method is offered for use when PARAFAC does not produce a positive manifold solution. (SLD)

Using explicit formulas for the information matrix of maximum likelihood factor analysis under multivariate normal theory, gross and net information for estimating the parameters in a covariance structure gained by adding the associated mean structure are defined. (Author/SLD)

Examined the factor structure of the Organizational Identification Questionnaire (G. Cheney, 1982), widely used to assess organizational identification. Analysis of results from 369 social-service employers yields four first-order and two second-order components. Contains 33 references. (SLD)

Explores the relationship between the higher-order factor model and the hierarchical factor model and shows that the Schmid-Leiman transformation process (J. Schmid and J. Leiman, 1957) produces constrained hierarchical factor solutions. Shows that the two models are not mathematically equivalent unless appropriate direct effects are added. (SLD)

Presents frequently encountered measurement misconceptions and various measurement "rules." Origins of the misconceptions and rules are described, along with the reasons why they are problematic. Alternate approaches or considerations are given. Misconceptions discussed pertain to the estimation of internal consistency reliability and item…

Discusses the conceptual/theoretical design, statistical, and reporting issues in choosing factor analysis for research. Provides questions to consider when planning, analyzing, or reporting an exploratory factor analysis study. (SK)