In this paper the parameter identifiability and estimation of a general dynamic structural model under indirect observation is considered from a system theoretic perspective. (Author/LMO)

The extension of regression estimation and poststratification to factor analysis is considered. These methods may be used either to improve the efficiency of estimation or to adjust for the effects of nonrandom selection. The estimation procedure may be formulated in a LISTREL framework. (Author/LMO)

A model is proposed for empirically testing the appropriateness of using tetrachoric correlations for a set of dichotomous variables. A testing method is proposed that uses information from trivariate marginal tables. Assumptions are tested with real and simulated data. (SLD)

An item-response model for binary response data exhibiting the bi-factor structure is derived, and a practical parameter estimation method is developed. The model, which provides an alternate to the traditional conditionally independent unidimensional item response theory model, is illustrated with an example from psychiatric research. (SLD)

The usefulness of path analysis as a means of better understanding various linear models is demonstrated. First, two linear models are presented in matrix form using linear structural relations (LISREL) notation. The two models, regression and factor analysis, are shown to be identical although the research question and data matrix to which these…

Confirmatory factor analysis was applied to test second- and higher-order factor models in the areas of structure of abilities, allometry, and the separation of specific and error variance estimates. The estimation of validity and reliability, second-order models within factor analysis models, and the concept of discriminability were also studied.…

A simple procedure for establishing convergent and discriminant validity is presented, as an alternative to the LISREL-based nested model used by R. P. Bagozzi (1978) and K. F. Widaman (1985). Ordinary least-squares regression is used, with the correlation between measures as the dependent variable. (SLD)

Across conditions, differences between population parameters defined by common factor analysis and component analysis are demonstrated. Implications for data analytic and theoretical issues related to choice of analytic model are discussed. Results suggest that principal components analysis should not be used to obtain parameters reflecting latent…

The dynamic factor model proposed by P. C. Molenaar (1985) is exhibited, and a dynamic nonstationary factor model (DNFM) is constructed with latent factor series that have time-varying mean functions. The use of a DNFM is illustrated using data from a television viewing habits study. (SLD)

Sufficient conditions are derived for the consistency and asymptotic normality of the least squares estimator of a trilinear decomposition model for multiway data analysis. The limiting covariance matrix is computed. (Author/SLD)

Based on the usual factor analysis model, this paper investigates the relationship between improper solutions and the number of factors. The properties of the noniterative estimation method of M. Ihara and Y. Kano in exploratory factor analysis are also discussed. The estimators were compared in a Monte Carlo experiment. (TJH)

Asymptotic distributions of the estimators of communalities are derived for the maximum likelihood method in factor analysis. It is shown that equating the asymptotic standard error of the communality estimate to the unique variance estimate is not correct for the unstandardized case. Monte Carlo simulations illustrate the study. (SLD)

Two algorithms that automatically select subsets of variables (PACE algorithm) and reference variables (Fabin estimators), respectively, used for the noniterative estimators are presented. The PACE algorithm is based on a nonsymmetric matrix sweep operator. A Monte Carlo experiment compares the relative performance of these estimators and others.…

Principal component, image component, three types of factor score estimates, and one scale score method were compared over different levels of variables, saturations, sample sizes, variable to component ratios, and pattern rotations. There were virtually no overall differences among methods, with the average correlation between matched scores…

A method for the estimation of covariance structure models under polynomial constraints (such as quadratic constraints) is presented. Estimation is on maximum likelihood principles, and the test statistics, parameter estimates, and standard errors are based on a statistical theory which takes the constraints into account. (Author/JKS)