Albert R Wildt
Biographic Data
| ID | 266356 |
|---|---|
| NAME | Albert R Wildt |
| GIVEN NAMES | Albert R |
| FAMILY NAME | Wildt |
| SIGNATURE | WILDT A R |
| AFFILIATIONS | University of Missouri |
| VERIFIED | No |
| TOTAL WORKS | 8 |
| TOTAL CITATIONS | 3 |
| WORKS AS AUTHOR | 8 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 1975 |
| LATEST PUBLICATION YEAR | 1991 |
| H-INDEX | 1 |
Bias Approximations for Complex Estimators
In applied research designed to address conceptual as well as program and policy formulation issues, it is often important to assess the bias present in estimators of association and prediction stemming from the use of multivariate analytical procedures. However, the theoretic sampling distributions of these estimators are often unknown in applied research contexts because of their complexity and the inability to satisfy highly restrictive assump…
Approximating Confidence Intervals for Factor Loadings
Practical theoretic means for assessing the sampling variability of loadings estimated by exploratory factor analytic procedures have not been readily available in the absence of restrictive distributional assumptions. It has been necessary for researchers to interpret these point estimates (loadings) through the use of arbitrary rules-of-thumb. Under these conditions, loading interpretations may be problematic. A method is presented for exploiti…
Assessing Sampling Variation Relative to Number-of-Factors Criteria
Employment of the bootstrap method to approximate the sampling variation of eigenvalues is explicated, and its usefulness is amplified by an illustration in conjunction with two commonly used number-of-factors criteria: eigenvalues larger than one and the scree test. Confidence intervals for eigenvalues are approximated for sample correlation matrices that have ones and squared multiple correlation coefficients on the diagonals. The results demon…
Approximate Confidence Intervals for Estimates of Redundancy Between Sets of Variables
Use of redundancy analysis would be enhanced by having available a procedure that (a) evaluates the statistical significance of the estimated proportion of variance in the criterion variable set extracted by a given predictor variate, Ry i (2), as well as by all predictor variates combined, R(y 2), and (b) is lenient with respect to assumptions about the distributions of redundancy estimates. A previous testing method leaves in doubt the assessme…
Redundancy analysis
Redundancy analysis
Analysis of Covariance
Toward Understanding Attitude Structure
This article provides an alternative to the traditional method of selecting one model as representative of the entire population. The authors contend that this procedure extends the usefulness of predictive results and should contribute managerially useful information to research questions which traditionally have been resolved solely through the application of “goodness of fit” effectiveness measures
Toward Understanding Attitude Structure
This article provides an alternative to the traditional method of selecting one model as representative of the entire population. The authors contend that this procedure extends the usefulness of predictive results and should contribute managerially useful information to research questions which traditionally have been resolved solely through the application of “goodness of fit” effectiveness measures
Analysis of Covariance
Redundancy analysis
Redundancy analysis
Approximate Confidence Intervals for Estimates of Redundancy Between Sets of Variables
Use of redundancy analysis would be enhanced by having available a procedure that (a) evaluates the statistical significance of the estimated proportion of variance in the criterion variable set extracted by a given predictor variate, Ry i (2), as well as by all predictor variates combined, R(y 2), and (b) is lenient with respect to assumptions about the distributions of redundancy estimates. A previous testing method leaves in doubt the assessme…
Assessing Sampling Variation Relative to Number-of-Factors Criteria
Employment of the bootstrap method to approximate the sampling variation of eigenvalues is explicated, and its usefulness is amplified by an illustration in conjunction with two commonly used number-of-factors criteria: eigenvalues larger than one and the scree test. Confidence intervals for eigenvalues are approximated for sample correlation matrices that have ones and squared multiple correlation coefficients on the diagonals. The results demon…
Bias Approximations for Complex Estimators
In applied research designed to address conceptual as well as program and policy formulation issues, it is often important to assess the bias present in estimators of association and prediction stemming from the use of multivariate analytical procedures. However, the theoretic sampling distributions of these estimators are often unknown in applied research contexts because of their complexity and the inability to satisfy highly restrictive assump…
Approximating Confidence Intervals for Factor Loadings
Practical theoretic means for assessing the sampling variability of loadings estimated by exploratory factor analytic procedures have not been readily available in the absence of restrictive distributional assumptions. It has been necessary for researchers to interpret these point estimates (loadings) through the use of arbitrary rules-of-thumb. Under these conditions, loading interpretations may be problematic. A method is presented for exploiti…
Advanced Statistical Methods and Models (7 works) · Mathematics (7 works) · Statistics (7 works) · Computer Science (6 works) · Econometrics (6 works) · Confidence interval (3 works) · Correlation (3 works) · Multivariate analysis (3 works) · Multivariate statistics (3 works) · Psychology (3 works)