In this paper, we show that for some structural equation models (SEM), the classical chi-square goodness-of-fit test is unable to detect the presence of nonlinear terms in the model. As an example, we consider a regression model with latent variables and interactions terms. Not only the model test has zero power against that type of…

A theoretical framework is developed in which the effects of some common forms of violation of assumptions of linearity of regression and homoscedasticity can be investigated. Simple expressions are derived for the restricted and corrected correlations in terms of the target (unrestricted) correlation in these situations. (SLD)

A globally optimal solution is presented for a class of functions composed of a linear regression function and a penalty function for the sums of squared regression weights. A completing-the-squares approach is used, rather than calculus, because it yields global minimality easily in two of three cases examined. (SLD)

An algorithm for assessing the correspondence of one or more attribute rating variables to a symmetric matrix of dissimilarities is presented. It is useful as an alternative to fitting property variables into a multidimensional scaling space. The relation between the matrix and the variables is determined by evaluating pairs of pairs relations.…

The use of semiparametric models may require incorporating additional functions which do not vary across distributions and may require expressing the models in terms of the joint distribution of response class and response time. (Author/LMO)

The extension of regression estimation and poststratification to factor analysis is considered. These methods may be used either to improve the efficiency of estimation or to adjust for the effects of nonrandom selection. The estimation procedure may be formulated in a LISTREL framework. (Author/LMO)

A general model for two-level multivariate data, with responses possibly missing at random, is described. The model combines regressions on fixed explanatory variables with structured residual covariance matrices. The likelihood function is reduced to a form enabling computational methods for estimating the model to be devised. (Author)

R. A. Bailey and J. C. Gower explored approximating a symmetric matrix "B" by another, "C," in the least squares sense when the squared discrepancies for diagonal elements receive specific nonunit weights. A solution is proposed where "C" is constrained to be positive semidefinite and of a fixed rank. (SLD)

Canonical weights and structure correlations are used to construct low dimensional views of the relationships between two sets of variables. These views, in the form of biplots, display familiar statistics: correlations between pairs of variables, and regression coefficients. (SLD)

A method is proposed to simultaneously estimate regression functions and subject membership in "k" latent classes or groups given a censored dependent variable for a cross-section of subjects. Maximum likelihood estimates are obtained using an EM algorithm. The method is illustrated through a consumer psychology application. (SLD)

Psychometricians working in factor analysis and econometricians working in regression with measurement error in all variables are both interested in the rank of dispersion matrices under variation of diagonal elements. This paper reviews both fields; points out various small errors; and presents a methodological comparision of factor analysis and…

The Markov random graphs of O. Frank and D. Strauss (1986) and the estimation strategy for these models developed by Strauss and M. Ikeda (1990) are promising contributions. This paper describes a large class of models that can be used to investigate structure in social networks and illustrates their use. (SLD)

Multivariate distributions are studied in which all bivariate regressions can be linearized by separate transformation of each of the variables. A two-stage procedure, first scaling the variables optimally and then fitting a simultaneous equations model, is studied in detail. (SLD)

A method is presented for estimating the item characteristic curve (ICC) using polynomial regression splines. Estimation of spline ICCs is described by maximizing the marginal likelihood formed by integrating ability over a beta prior distribution. Simulation results compare this approach with the joint estimation of ability and item parameters.…

A model based on latent trait theory, with maximum likelihood parameter estimates and associated asymptotic tests, is presented. Latent curve analysis is a method for representing development and is an alternative to repeated measures analysis of variance and first-order auto-regressive models. (SLD)