Misclassification rates for four methods of group classification: Impact of predictor distribution, covariance inequality, effect size, sample size, and group size ratio

Educational and Psychological Measurement

Volumn 66, Issue 2, 2006, Pages 240-257

  Finch, W Holmes ^a   Schneider, Mercedes K ^a  

^a BALL STATE UNIVERSITY   (United States)

Author keywords

Classification; Discriminant analysis; Logistic regression; Regression trees

Indexed keywords

EID: 33644754593     PISSN: 00131644     EISSN: 15523888     Source Type: Journal    
DOI: 10.1177/0013164405278579     Document Type: Article

References (29)

1. Abedi, J. (1991). Predicting graduate academic success from undergraduate academic performance: A canonical correlation study. Educational and Psychological Measurement, 51, 151-160.
2. Arminger, G., Enache, D., & Bonne, T. (1997). Analyzing credit risk data: A comparison of logistic discrimination, classification tree analysis and freeforward networks. Computational Statistics, 12, 293-310.
3. Baird, L. L. (1975). Comparative prediction of first year graduate and professional school grades in six fields. Educational and Psychological Measurement, 35, 941-946.
4. Baron, A. E. (1991). Misclassification among methods used for multiple group discrimination: The effects of distributional properties. Statistics in Medicine, 10, 757-766.
5. Bryan, J. G. (1961). Scientific Report No. 2: Calibration of qualitative and quantitative variables for use in multiple-group discriminant analysis. Hartford, CT: The Travellers Insurance Company.
6. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum.
7. Cohen, J. (1992). A power primer. Psychological Bulletin, 112 (1). 155-159.
8. Dey, E. L., & Astin, A. W. (1993). Statistical alternatives for studying college student retention: A comparative analysis of logit, probit, and linear regression. Research in Higher Education, 34, 569-581.
9. Fan, X., & Wang, L. (1999). Comparing linear disriminant function with logistic regression for the twogroup classification problem. Journal of Experimental Education, 67 (3). 265-286.
10. Ferrer, A. J. A., & Wang, L. (1999, April 13-17). Comparing the classification accuracy among nonparametric, parametric discriminant analysis and logistic regression methods. Paper presented at the annual meeting of the American Educational Research Association, Montreal, Canada.
11. Fleishman,A. I. (1978). A methodfor simulatingnon-normaldistributions. Psychometrika, 43 (4). 521-532.
12. Headrick, T. C., & Sawilowsky, S. S. (1999). Simulating correlated multivariate nonnormal distributions: Extending the Fleishman power method. Psychometrika, 64, 25-35.
13. Hess, B., Olejnik, S., & Huberty, C. J. (2001). The efficacy of two improvement-over-chance effect sizes for two-group univariate comparisons under variance heterogeneity and nonnormality. Educational and Psychological Measurement, 61 (6). 909-936.
14. Huberty, C. J. (1994). Applied discriminant analysis. New York: John Wiley.
15. Huberty, C. J., & Curry, A. R. (1978). Linear versus quadratic multivariate classification. Multivariate Behavioral Research, 13, 237-245.
16. Johnson, R. A., & Wichern, D. W. (2002). Applied multivariate statistical analysis. Upper Saddle River, NJ: Prentice Hall.
17. Koffler, S. L. (1976, April). An evaluation and comparison of discriminant procedures for certain types of non-normal distributions. Paper presented at the annual meeting of the American Educational Research Association, San Francisco.
18. Krzanowski, W. J. (1977). The performance of Fisher's linear discriminant function under non-optimal conditions. Technometrics, 19 (2). 191-200.
19. Maxwell, S. E. (1961). Canonical variate analysis when the variates are dichotomous. Educational and Psychological Measurement, 21, 259-271.
20. McClachlan, G. J. (1992). Discriminant analysis andstatistical pattern recognition. New York: John Wiley.
21. Meshbane, A., & Morris, J. D. (1995). A method for selecting between linear and quadratic classification models in discriminant analysis. Journal of Experimental Education, 63, 263-273.
22. Meshbane, A., & Morris, J. D. (1996, April). Predictive discriminant analysis versus logistic regression in two-group classification problems. Paper presented at the annual meeting of the American Educational Research Association, New York.
23. Remus, W., & Wong, C. (1982). An evaluation of five models for the admission decision. College Student Journal, 16, 53-59.
24. Schumacher, M., Rossner, R., & Vach, W. (1996). Neural networks and logistic regression: Part I. Computational Statistics: Data Analysis, 21, 661-682.
25. Stevens, J. (1996). Appliedmultivariate statistics for the social sciences. Mahwah, NJ: Lawrence Erlbaum.
26. Tabachnick, B. G., & Fidell, L. S. (2001). Using multivariate statistics. Boston: Allyn & Bacon.
27. Williams, C. J., Lee, S. S., Fisher, R. A., & Dickerman, L. H. (1999). A comparison of statistical methods for prenatalscreeningfor down syndrome. AppliedStochastic Models in Business andIndustry, 15, 89-101.
28. Wilson, R. L., & Hardgrave, B. C. (1995). Predicting graduate student success in an MBA program: Regression versus classification. Educational and Psychological Measurement, 55 (2). 186-195.
29. Young, B. (1993, November). Quadratic versus linear rules in predictive discriminant analysis. Paper presented at the annual meeting of the Mid-South Educational Research Association, New Orleans, LA.