Anderson, T. W. (1984). An introduction to multivariate statistical analysis (2nd ed.). New York: John Wiley.
2.
Brooks, S. (1994). Review of “Applied Multivariate Statistics for the Social Sciences.” Statistician, 43, 219-220.
3.
Browne, M. W. (1975a). A comparison of single sample and cross-validation methods for estimating the mean squared error of prediction in multiple linear regression. British Journal of Mathematical & Statistical Psychology, 28, 112-120.
4.
Browne, M. W. (1975b). Predictive validity of a linear regression equation. British Journal of Mathematical and Statistical Psychology, 28, 79-87.
5.
Burket, G. R. (1964). A study of reduced rank models for multiple prediction. Psychometric Monograph (No. 12).
6.
Cattin, P. (1980). Estimation of the predictive power of a regression model. Journal of Applied Psychology, 65, 406-414.
7.
Heckman, J. J. 1979. Sample selection bias as specification error. Econometrica, 47, 153-161.
8.
Hunter, J. E. (1997). Needed: A ban on the significance test. Psychological Science, 8, 3-7.
9.
Johnson, R. A., & Wichern, D. W. (2002). Applied multivariate statistical analysis (5th ed.). Upper Saddle River, NJ: Prentice Hall.
10.
Mardia, K. V., Kent, J. T., & Bibby, J. M. (1979). Multivariate analysis. San Diego, CA: Academic Press.
11.
Murphy, K. R. (1984). Cost-benefit considerations in choosing among cross-validation. Personnel Psychology, 37, 15-22.
12.
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.
13.
Schmidt, F. L., & Hunter, J. (1997). Eight common but false objections to the discontinuation of significance testing in the analysis of research data. In L. L. Harlow, S. A. Mulaik, &J. H. Steiger (Eds.), What if there were no significance tests? (pp. 37-64). Mahwah, NJ: Lawrence Erlbaum.
14.
Schmitt, N., Coyle, B. W., & Rauschenberger, J. (1977). A Monte Carlo evaluation of three formula estimates of cross-validated multiple correlation. Psychological Bulletin, 84, 751-758.
15.
St. John, C., & Roth, P. L. (1999). The impact of cross-validation adjustments on estimates of effect size in business policy and strategy research. Organizational Research Methods, 2, 157-174.
16.
Stein, C. (1960). Multiple regression. In I. Olkinet al. (Eds.), Contributions to probability and statistics: Essays in honor of Harold Hotelling. Stanford, CA: Stanford University Press.