Multiple Regression and Validity Estimation in One Sample

@article{Claudy1978MultipleRA,
  title={Multiple Regression and Validity Estimation in One Sample},
  author={John G. Claudy},
  journal={Applied Psychological Measurement},
  year={1978},
  volume={2},
  pages={595 - 607}
}
  • John G. Claudy
  • Published 1 October 1978
  • Mathematics
  • Applied Psychological Measurement
This study empirically investigated equations for estimating the value of the multiple correlation co efficient in the population underlying a sample and the value of the population validity coefficient of a sample regression equation. In addition to pre viously published estimation equations, several new procedures, including an empirically derived equa tion, were evaluated using 16 independent popula tions. Overall, the empirical equation was superior to any of the previously published… 

Figures and Tables from this paper

Estimation of the Squared Cross-Validity Coefficient in the Context of Best Subset Regression
A monte carlo study was conducted to examine the performance of several strategies for estimating the squared cross-validity coefficient of a sample regres sion equation in the context of best subset
Methodology Review: Estimation of Population Validity and Cross-Validity, and the Use of Equal Weights in Prediction
In multiple regression, optimal linear weights are obtained using an ordinary least squares (OLS) procedure. However, these linear weighted combinations of predictors may not optimally predict the
Estimating R 2 Shrinkage in Multiple Regression: A Comparison of Different Analytical Methods
Abstract The effectiveness of various analytical formulas for estimating R 2 shrinkage in multiple regression analysis was investigated. Two categories of formulas were identified: estimators of the
Estimation in Multiple Correlation/Prediction
The distinction between the square of a population correlation coefficient (ρ2) and of the true validity of a sample prediction equation (ρ v 2) as the parameters of interest in multiple correlation
Estimators of the Squared Cross-Validity Coefficient: A Monte Carlo Investigation
A monte carlo experiment was used to evaluate four procedures for estimating the population squared cross-validity of a sample least squares re gression equation. Four levels of population squared
Precision Efficacy Analysis for Regression.
When multiple linear regression is used to develop a prediction model, sample size must be large enough to ensure stable coefficients. If the derivation sample size is inadequate, the model may not
Confidence Intervals for Comparison of the Squared Multiple Correlation Coefficients of Non-nested Models
Multiple linear regression analysis is used widely to evaluate how an outcome or responsevariable is related to a set of predictors. Once a final model is specified, the interpretation of predictors
Factors that Influence Cross-validation of Hierarchical Linear Models
FACTORS THAT INFLUENCE CROSS-VALIDATION OF HIERARCHICAL LINEAR MODELS by Tracy Widman While use of hierarchical linear modeling (HLM) to predict an outcome is reasonable and desirable, employing the
The Cross-Validational Accuracy of Sample Regressions
Browne's definitive but complex formulas for the cross-validational accuracy of an OSL-estimated regression equation in the random-effects sampling model are here reworked to achieve greater
Improved Shrinkage Estimation of Squared Multiple Correlation Coefficient and Squared Cross-Validity Coefficient
The sample squared multiple correlation coefficient is widely used for describing the usefulness of a multiple linear regression model in many areas of science. In this article, the author considers
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 17 REFERENCES
EFFICIENCY OF PREDICTION WHEN A REGRESSION EQUATION FROM ONE SAMPLE IS USED IN A NEW SAMPLE
If a multiple regression equation is computed from one sample and applied to subsequent samples, the errors of prediction in the later samples will be larger than those in the first sample or those
A study of reduced rank models for multiple prediction
Abstract : The present study proceeds along both theoretical and empirical lines. First an attempt is made to work out some of the consequences of regression theory for reduced-rank models. Since, as
The Parameters of Cross-Validation
Abstract : The validation of predictor weights, derived in one sample, by computing the correlation of the weighted sum of the predictors with the criterion in new samples is called cross-validation.
Unbiased Estimation of Certain Correlation Coefficients
1. Summary and introduction. This paper deals with the unbiased estimation of the correlation of two variates having a bivariate normal distribution (Sec. 2), and of the intraclass correlation, i.e.,
I. Problems and Designs of Cross-Validation 1
THE term &dquo;cross-validation&dquo; is often loosely applied to any one of several distinct, though closely related, experimental designs. Before we get lost in a swamp of semantic confusion, it
Multiple regression in psychological research and practice.
Personnel testing
  • New York: McGrawHill,
  • 1965
Problems and designs of cross-validation. Educational and Psychological Measurement
  • 1951
The application of a regression equation to a new sample
  • 1948
...
1
2
...