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Yuan, Ke-Hai; Chan, Wai – Psychometrika, 2011
The paper obtains consistent standard errors (SE) and biases of order O(1/n) for the sample standardized regression coefficients with both random and given predictors. Analytical results indicate that the formulas for SEs given in popular text books are consistent only when the population value of the regression coefficient is zero. The sample…
Descriptors: Statistical Bias, Error of Measurement, Regression (Statistics), Predictor Variables
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Takane, Yoshio; Hwang, Heungsun – Psychometrika, 2005
Lazraq and Cleroux (Psychometrika, 2002, 411-419) proposed a test for identifying the number of significant components in redundancy analysis. This test, however, is ill-conceived. A major problem is that it regards each redundancy component as if it were a single observed predictor variable, which cannot be justified except for the rare…
Descriptors: Redundancy, Monte Carlo Methods, Predictor Variables, Psychometrics
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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
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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