The online deployment of Technology Engineered online Student Ratings of Instruction (SRIs) by colleges and universities in the United States has dynamically changed the deployment of course evaluation. This research investigation is the fourth part of a post hoc study that analytically and psychometrically examines the design, reliability, and…

Configural frequency analysis (CFA), a new method for identifying types, is illustrated numerically. Relations to latent class analysis and to factor analysis are discussed. It is suggested to use CFA as a type-defining method instead of factor analysis if the variables are linked not only by first but also by higher-order associations. (RC)

Marascuilo's and Levin's use of the term "nesting" to describe a statistical model, and application of the Scheffe tests on restricted sets of contrasts, is opposed. Changing the model during analysis of a complex set of data is both necessary and desirable. (Author/CP)

A special case of Bloxom's version of Tucker's three mode factor analysis model is developed statistically. A goodness of fit test and an empirical example are presented. (Author/JKS)

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)

A general statistical model for the multivariate analysis of mean and covariance structures is described. Matrix calculus is used to develop the statistical aspects of one new special case in detail. This special case separates the confounding of principal components and factor analysis. (DEP)

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.…

Statistical methods, called latent variable models, have been developed to provide rigorous tests of theories involving unobserved variables. This paper describes the major types of latent variable models, shows how they can be applied in educational research, and gives representative examples of their use from the literature. (Author/BW)

As useful as LISREL may be in model estimation and testing, its most significant contribution to date is the encouragement and example it gives for right thinking about research and right planning of research. The encouragement to hypothesize the best possible model for the process that is the object of study, and to plan measurements that…

A review of model-selection criteria is presented, suggesting their similarities. Some problems treated by hypothesis tests may be more expeditiously treated by the application of model-selection criteria. Multivariate analysis, cluster analysis, and factor analysis are considered. (Author/GDC)

Special cases of the factor analysis model are developed for four selection situations. Methods are suggested whereby parameters in each case can be estimated using a maximum likelihood procedure recently developed by Joreskog. (Author)

Considers the study of similarities and differences in factor structures between different groups. (AG)

An example demonstrates that Joreskog's suggested sufficient conditions for identifying unknown parameters in a confirmatory factor analytic model with correlated factors are not sufficient. Sufficient conditions are presented. (Author/JKS)

Traditionally, confirmatory factor analytic models are tested against a null model of total independence. Using randomly generated factors in a matrix of 46 aptitude tests, this approach is shown to be unlikely to reject even random factors. An alternative null model, based on a single general factor, is suggested. In addition, an index of model…

Three topics in factor analysis are covered: a) a reliability coefficient for assessing the quality of a maximum likelihood factor analysis, b) an application of three-mode factor analysis to serial learning data, showing variations in learning curves over stages of learning and individuals, and c) the use of personal probability functions to…