Mathematics education currently emphasizes engaging students in mathematical modeling to understand problems of everyday life and society (Council of Chief State School Officers (CCSSO), 2010; English & Sriraman, 2010; Lesh & Zawojewski, 2007). The Common Core State Standards for mathematics also stress that high school students should…

A general linear model framework is used to suggest that structure coefficients ought to be interpreted in structural equation modeling confirmatory factor analysis (CFA) studies in which factors are correlated. Two heuristic data sets make the discussion concrete, and two additional studies illustrate the benefits of CFA structure coefficients.…

Demonstrates how perspectives from covariance structural modeling and generalizability theory can be combined for a comprehensive assessment of latent constructs. This approach to examining variance components is illustrated by one- and two- facet designs, and can be extended to more complex designs. (MAK)

Argues that data on siblings provide a way to account for the impact of unmeasured, omitted variables on relationships of interest because families form a sort of natural experiment, with similar experiences and common genetic heritage. Proposes a latent-variable structural equation approach to the problem, which provides estimates of both within-…

The impact of the use of data arising from balanced incomplete block (BIB) spiralled designs on the chi-square goodness-of-fit test in factor analysis is considered. Data from BIB designs posses a unique pattern of missing data that can be characterized as missing completely at random (MCAR). Standard approaches to factor analyzing such data rest…

An implementation of the Gauss-Newton algorithm for the analysis of covariance structure that is specifically adapted for high-level computer languages is reviewed. This simple method for estimating structural equation models is useful for a variety of standard models, as is illustrated. (SLD)

Describes a new approach for analyzing an individual's responses made at multiple times. Proposes that three sources of variance determine a person's current standing on a variable: trait (term does not change), state (term changes), and error (random term). Shows how structural equation modeling can be used and presents an extended example. (RJM)