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Bentler, Peter M.; Yuan, Ke-Hai – Psychometrika, 2011
Indefinite symmetric matrices that are estimates of positive-definite population matrices occur in a variety of contexts such as correlation matrices computed from pairwise present missing data and multinormal based methods for discretized variables. This note describes a methodology for scaling selected off-diagonal rows and columns of such a…
Descriptors: Scaling, Factor Analysis, Correlation, Predictor Variables
Krijnen, Wim P. – Psychometrika, 2006
The assumptions of the model for factor analysis do not exclude a class of indeterminate covariances between factors and error variables (Grayson, 2003). The construction of all factors of the model for factor analysis is generalized to incorporate indeterminate factor-error covariances. A necessary and sufficient condition is given for…
Descriptors: Factor Analysis, Statistical Analysis, Prediction, Predictor Variables

Green, Bert F. Jr. – Psychometrika, 1976
A summary and interpretation of the recent literature on the indeterminancy of factor scores is given in simple terms. A good index of factor score determinancy is the squared multiple correlation of the factor with the observed variables. (Author)
Descriptors: Correlation, Factor Analysis, Factor Structure, Multiple Regression Analysis
Rabe-Hesketh, Sophia; Skrondal, Anders; Pickles, Andrew – Psychometrika, 2004
A unifying framework for generalized multilevel structural equation modeling is introduced. The models in the framework, called generalized linear latent and mixed models (GLLAMM), combine features of generalized linear mixed models (GLMM) and structural equation models (SEM) and consist of a response model and a structural model for the latent…
Descriptors: Psychometrics, Structural Equation Models, Item Response Theory, Predictor Variables
Rocci, Roberto; Vichi, Maurizio – Psychometrika, 2005
A new methodology is proposed for the simultaneous reduction of units, variables, and occasions of a three-mode data set. Units are partitioned into a reduced number of classes, while, simultaneously, components for variables and occasions accounting for the largest common information for the classification are identified. The model is a…
Descriptors: Factor Analysis, Classification, Least Squares Statistics, Monte Carlo Methods
Ogasawara, Haruhiko – Psychometrika, 2004
Formulas for the asymptotic biases of the parameter estimates in structural equation models are provided in the case of the Wishart maximum likelihood estimation for normally and nonnormally distributed variables. When multivariate normality is satisfied, considerable simplification is obtained for the models of unstandardized variables. Formulas…
Descriptors: Evaluation Methods, Bias, Factor Analysis, Structural Equation Models