Presents and illustrates structural equation modeling (multivariate analysis with latent variables, also called causal modeling or covariance structure analysis), discussing issues and problems related to the use of this methodology, possible applications of structural equation modeling to counseling psychology research, and resources for further…

This study focuses on the relationship between teacher education and graduates' intended and actual entrance into teaching. Moreover, it explores how this relationship differs for two types of initial teacher training for secondary education. A hypothetical model of graduates' entrance into the teaching profession comprising empirically grounded…

A reanalysis of a structural equation model of the development of intellectual abilities is presented. The original model emphasized four different constructs of intelligence; the reanalysis emphasized a general factor model. (Author/JKS)

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…

Focus of this paper is on the logic of the model rather than the estimation of parameters or testing the fit of the model to data. (Authors/MB)

This paper demonstrates the feasibility of using the penalty function method to estimate parameters that are subject to a set of functional constraints in covariance structure analysis. Both types of inequality and equality constraints are studied. The approaches of maximum likelihood and generalized least squares estimation are considered.…

Presents an approach (designed for beginners) to using PROC CALIS, a Statistical Analysis System (SAS) procedure, to perform path analyses using observed variables. The approach begins with the development of a figure to illustrate the researcher's theoretical model, and then converts the figure into a PROC CALIS program. (SLD)

Covariance structure models are applied to gene expression data using a factor model, a path model, and their combination. The factor model is based on a few factors that capture most of the expression information. A common factor of a group of genes may represent a common protein factor for the transcript of the co-expressed genes, and hence, it…

The aim of this study was to validate a French version of the Cognitive Emotion Regulation Questionnaire (CERQ; Garnefski, N., Kraaij, V., & Spinhoven, P., 2001. Negative life events, cognitive emotion regulation and emotional problems. "Personality and Individual Differences, 30," 1311-1327) and to explore its relationships with impulsivity and…

Correlated Factors Modeling (CFM) is proposed as an alternative to LISREL and similar methods of estimating structural equations. CFM employs weak mathematics and is appropriate for a rapid, preliminary look at a model. The factors produced by CFM are exact linear components of their indicator variates. (Author/GDC)

A statistical model for relating latent variables from manifest variables, called a linear structural equation model, is presented. The approach is illustrated by a test theory model and longitudianl study of intelligence. (Author/JKS)

Proposes a model of kindergartners' development of literacy resources. States that, throughout the year, kindergartners completed tasks similar to those typically used in their different instructional programs, and factor analysis identified five factors for use in a path analysis. Finds various factors influenced outcome variables, represented by…

Purpose: This study explored the perceptions of teachers regarding principal support for and understanding of effective writing instruction and whether certain areas of knowledge influence principals' actions and interventions. Research Design: Researchers surveyed teacher participants using the Principal's Support for Writing Instrument and also…

The overidentification of structural equation models with latent variables is discussed. The use of two- and three-indicator models is not recommended since such models do not allow a testing of the crucial assumption of unidimensionality among indicators in most cases. Models with four or more indicators may be more sensitive to departures from…

Current practice in structural modeling of variables is limited to means and covariances based on multivariate normality assumptions. This article extends structural equation models to higher order product moments and to non-normal distributions. Areas of possible research are described. (Author/JKS)