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Waller, Niels G.; Jones, Jeff A. – Psychometrika, 2009
In a multiple regression analysis with three or more predictors, every set of alternate weights belongs to an infinite class of "fungible weights" (Waller, Psychometrica, "in press") that yields identical "SSE" (sum of squared errors) and R[superscript 2] values. When the R[superscript 2] using the alternate weights is a fixed value, fungible…
Descriptors: Multiple Regression Analysis, Predictor Variables, Algebra, Geometric Concepts
Takane, Yoshio; Jung, Sunho – Psychometrika, 2008
Methods of incorporating a ridge type of regularization into partial redundancy analysis (PRA), constrained redundancy analysis (CRA), and partial and constrained redundancy analysis (PCRA) were discussed. The usefulness of ridge estimation in reducing mean square error (MSE) has been recognized in multiple regression analysis for some time,…
Descriptors: Predictor Variables, Multiple Regression Analysis, Least Squares Statistics, Data Analysis

Rozeboom, William W. – Psychometrika, 1979
For idealized item configurations, equal item weights are often virtually as good for a particular predictive purpose as the item weights that are theoretically optimal. What has not been clear, however, is what happens to the similarity when the item configuration's variance structure is complex. (Author/CTM)
Descriptors: Multiple Regression Analysis, Predictor Variables, Scoring Formulas, Weighted Scores

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