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…

Decomposable models, which allow for the interdependence of structure among observable variables, are described. Each model is fully characterized by a set of conditional interdependence restrictions and can be visualized with an undirected as well as a special type of directed graph. (Author/JKS)

The sampling variability and zeta-values of parameter estimates for misspecified structural equation models were examined. A Monte Carlo study was used. Results are discussed in terms of asymptotic theory and the implications for the practice of structural equation models. (SLD)

An empirical sampling approach was used to assess the accuracy of a Taylor approximation for the estimation of sampling errors. The sampling errors were in the statistics involved in estimating a path model based on medium-sized samples gathered using five sample designs commonly used in educational research. (TJH)

Path Analysis of Covariance Matrix (PACM) is described as a way to separately estimate measurement and structural models using standard least squares procedures. PACM was empirically compared to simultaneous maximum likelihood estimation and use of the LISREL computer program, and its advantages are identified. (SLD)

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)

Direct and indirect effects in decomposed zero-order correlations among variables in causal models are considered. Under certain circumstances, the components of the decompositions could be interpreted as direct, indirect, and spurious causal effects, plus a component called joint associations. The sum of the direct and indirect effects is the…

Latent Variables Path (LVP) models have been found to be a helpful device for the formulation and validation of complex theories in educational sciences. The estimation of parameters for LVP models can use the Maximum Likelihood (ML) or the Partial Least Squares (PLS) method. This paper focusses on the PLS method introduced by H. Wold, and…

This study examined the effect of type of correlation matrix on the robustness of LISREL maximum likelihood and unweighted least squares structural parameter estimates for models with categorical manifest variables. Two types of correlation matrices were analyzed; one containing Pearson product-moment correlations and one containing tetrachoric,…

Newman (1984) examined the causal relations between math self-concept and math achievement in an 8-year longitudinal study using Linear Structural Relations (LISREL) analyses. He concluded that math self-concept did not influence subsequent math achievement. However, the study suffered in that math self-concept was inferred from a single-item…