Two Statistical Analysis System (SAS) macros are presented that perform the modified principal components approach of L. R. Tucker (1966) to modeling generalized learning curves analysis up to a rotation of the components. Three SAS macros are described that rotate the factor patterns to have characteristics Tucker considered desirable. (SLD)

Using LISREL, the only types of constraints allowed are fixing parameters at a constant value and constraining parameters to be equal. In this paper, two new concepts ("phantom" and "imaginary" latent variables) are introduced which allow fairly general equality and inequality constraints on factor loadings and structural model…

A general latent variable model allows for maximum likelihood estimation with missing data. LISREL and LISCOMP programs may be used to carry out this estimation. Simulated data were generated. The proposed Full, Quasi-Likelihood estimator was found to be superior to listwise present quasi-likelihood and pairwise present approaches. (Author/GDC)

The present study compares the performance of phi coefficients and tetrachorics along two dimensions of factor recovery in binary data. These dimensions are (1) accuracy of nontrivial factor identifications; and (2) factor structure recovery given a priori knowledge of the correct number of factors to rotate. (Author/LMO)

Multi-indicator growth models were formulated as special three-level hierarchical generalized linear models to analyze growth of a trait latent variable measured by ordinal items. Items are nested within a time-point, and time-points are nested within subject. These models are special because they include factor analytic structure. This model can…

This study examined the effect of type of correlation matrix on the robustness of LISREL maximum likelihood and unweighted least squares structural parameter estimates for models with categorical variables. The analysis of mixed matrices produced estimates that closely approximated the model parameters except where dichotomous variables were…

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)

A component analytic method for analyzing multivariate longitudinal data is presented that does not make strong assumptions about the structure of the data. Central to the method are the facts that components are derived as linear composites of the observed or manifest variables and that the components must provide an adequate representation of…

The TESTFACT computer program and full-information factor analysis of test items were used in a computer simulation conducted to correct for the guessing effect. Full-information factor analysis also corrects for omitted items. The present version of TESTFACT handles up to five factors and 150 items. A preliminary smoothing of the tetrachoric…

The advantages of applying confirmatory factor analysis (CFA) to multitrait-multimethod (MTMM) data are widely recognized. However, because CFA as traditionally applied to MTMM data incorporates single indicators of each scale (i.e., each trait/method combination), important weaknesses are the failure to: (1) correct appropriately for measurement…

This study investigates the extent to which the higher order factors of symbolic and semantic content are differentiated. The main concern is an expository description of the use of confirmatory factor analysis in establishing factorial validity. Reanalyzed data originally collected in 1968 involved a sample of 197 tenth, eleventh and twelfth…

This paper discusses the use of the LISREL computer program to compare two groups of Ontario students who studied French as a second language in either a regular or an immersion program. Longitudinal data on growth in mathematical ability, from grade 4 to grade 6, are analyzed using seven different mathematical models. The paper illustrates the…