The squared cross-validity coefficient is a measure of the predictive validity of a sample linear prediction equation. It provides a more realistic assessment of the usefulness of the equation than the squared multiple-correlation coefficient. The squared cross-validity coefficient cannot be larger than the squared multiple-correlation coefficient; its size is affected by the number of predictor variables and the size of the sample. Sample-size tables are presented that should result in very small discrepancies between the squared multiple correlation and the squared cross-validity correlation, thus facilitating the selection of sample size for predictive studies.
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References
1.
Browne, M. W. (1975). Predictive validity of a linear regression equation. British Journal of Mathematical and Statistical Psychology, 28, 79–87.
2.
Cattin, P. (1980a). Estimation of a regression model. Journal of Applied Psychology, 65, 407–414.
3.
Cattin, P. (1980b). Note on the estimation of the squared cross-validated multiple correlation of a regression model. Psychological Bulletin, 87, 63–65.
4.
Darlington, R. B. (1978). Reduced-variance regression. Psychological Bulletin, 85, 1238–1255.
5.
Drasgow, F. , Dorans, N. J., & Tucker, L. R. (1979). Estimators of the squared cross-validity coefficient: A monte carlo investigation. Applied Psychological Measurement, 3, 171–192.
6.
Raju, N. S. , Bilgic, R., Edwards, J. E., & Fleer, P. F. (1997). Methodology review: Estimation of population validity and cross-validity, and the use of equal weights in prediction. Applied Psychological Measurement, 21, 291–305.
7.
Raju, N. S. , Bilgic, R., Edwards, J. E., & Fleer, P. F. (1999). Accuracy of population validity and cross-validity estimation: An empirical comparison of formula-based, traditional empirical, and equal weights procedures. Applied Psychological Measurement, 23, 99–115.
