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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

Freeman, Linton – Psychometrika, 1976
This note extends and elaborates Hubert's attempt to provide an interpretation of Freeman's measure of association, theta. The theta measure is used in a contingency table when observations are ordered on one variable and unordered on the other. No attempt is made to explore the distribution of theta. (Author/RC)
Descriptors: Correlation, Prediction, Probability, Statistical Analysis

Levin, Joseph – Psychometrika, 1972
Analyzes some properties of the correction for range formula in the three variable case, (x, y, and z). (AG)
Descriptors: Correlation, Mathematics, Prediction, Predictor Variables
Haberman, Shelby J. – Psychometrika, 2006
When a simple random sample of size n is employed to establish a classification rule for prediction of a polytomous variable by an independent variable, the best achievable rate of misclassification is higher than the corresponding best achievable rate if the conditional probability distribution is known for the predicted variable given the…
Descriptors: Bias, Computation, Sample Size, Classification

Thissen, David; Wainer, Howard – Psychometrika, 1976
A new measure of correlation and a measure of scale are proposed which are substantially more robust than their least squares counterparts. Increased robustness may also be obtained by use of equal regression weights, or knowledge of the theoretical structure of the weights. (Author/HG)
Descriptors: Correlation, Least Squares Statistics, Monte Carlo Methods, Nonparametric Statistics

Gleason, Terry C.; Staelin, Richard – Psychometrika, 1975
Presents a new approach for estimating missing observations together with the results of a Monte Carlo study of the relative strengths and weaknesses of this technique and three other available methods. These techniques are then examined with respect to their ability to use incomplete data to estimate the correlation matrix obtained using a full…
Descriptors: Comparative Analysis, Correlation, Data Analysis, Matrices