This article is a rejoinder to TM 502 249. Each of Thorndike's comments are examined. A possible solution to the large number of subjects necessary for stable weights and variate-variable correlations using ridge regression procedures is suggested. (RC)

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…

A new approach to partialling components is used. Like conventional partialling, this approach orthogonalizes variables by partitioning the scores or observations. Unlike conventional partialling, it yields a common component and two unique components. (Author/GDC)

Employs latent partition analysis (a multivariate technique) to reveal pre/postcognitive structure of freshman engineering students (N=70) taking general chemistry. Similarities and dissimilarities among high and low scorers are discussed. Arguments for use of this statistical techniques are presented. (DH)

Examined data from a questionnaire concerning people with chronic health, emotional, mental, physical, and sensory impairments, and four respondent demographic and experiential characteristics to determine if 150 human service providers endorsed the premise that components of society disable people who are impaired. Results confirmed that…

This paper presents a discussion of various statistical concepts and techniques in light of two propositions. The first is that researchers need to select analytical techniques that prevent them from committing Type VI errors, which are inconsistencies between the research question and the statistical analysis. The second is that many statistical…

Methods for comparing the suitability of alternative models for covariance matrices are examined. A cross-validation procedure is suggested and its properties examined. A series of examples using longitudinal data are examined. (Author/JKS)

Theta is a statistic which measures the degree to which a designated pattern successfully partitions a matrix of pre- and post-treatment ratings into regions typical of each of two treatments. In this paper, theta is extended to multivariate and multigroup cases. (Author/BW)

The available methods for analyzing and interpreting data with multivariate analysis of variance are reviewed, and guidelines for their use are presented. Causal models that underlie the various methods are presented to facilitate the use and understanding of the methods. (Author/PN)

In response to Webb and Shavelson (EJ 241 567), Nussbaum questions the relevance of the universe score variance as a meaning reliability index and whether it is useful to determine the weights which enter into a composite by means of maximum generalizability. (CM)

Answering Nussbaum's (TM 507 069) criticism, the generalizability coefficient for absolute decisions, the use of the error variance formula, composites of maximum generalizability, and covariance components are discussed as yardsticks of measurement precision with arguments for the use of each procedure to interpret data. (CM)

Known results for testing and estimation problems for patterned means and covariance matrices with explicit linear maximum likelihood estimates are applied to the block compound symmetry problem. An example involving educational testing is provided. (Author/JKS)

The MMPI results obtained from 245 adolescent males referred to the evaluation unit of a Juvenile Court were submitted to a multivariate classification system. By correlating individual subject profiles with the modal profiles, six membership groups were formed. No relationship was found between group membership and age or race. (Author)

A particular strategy for investigating effects from a multivariate analysis of variance (MANOVA) is proposed. The strategy involves multiple two-group multivariate analyses. The analysis strategy is described in detail and illustrated with real data sets. (Author/JKS)

Canonical correlation analysis is considered to be a general model for bivariate and multivariate statistical methods. Some problems involving assumptions and statistical tests for parameters exist for social science data. A resolution for these problems is presented by treating canonical correlation as a special case of linear structural…