MEG: Megacluster analytic strategy for multistage hierarchical grouping with relocations and replications

Educational and Psychological Measurement

Volumn 58, Issue 4, 1998, Pages 677-686

  McDermott, Paul A ^a  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0032396806     PISSN: 00131644     EISSN: None     Source Type: Journal    
DOI: 10.1177/0013164498058004010     Document Type: Article

References (22)

1. Blashfield, R. K., & Aldenderfer, M. S. (1988). The methods and problems of cluster analysis. In J. R. Nesselroade & R. B. Cattell (Eds.), Handbook of multivariate experimental psychology (2nd ed., pp. 447-473). New York: Plenum.
2. Borgen, F. H., & Barnett, D. C. (1987). Applying cluster analysis in counseling psychology research. Journal of Counseling Psychology, 34, 456-468.
3. Breckenridge, J. N. (1989). Replicating cluster analysis: Method, consistency, and validity. Multivariate Behavioral Research, 24, 147-161.
4. Calinski, T., & Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics, 3, 1-27.
5. Comrey, A. L. (1985). A method to remove outliers to improve factor analytic results. Multivariate Behavioral Research, 20, 273-281.
6. Cooper, M. C., & Milligan, G. W. (1988). The effect of error on determining the number of clusters. Proceedings of the International Workshop on Data Analysis, Decision Support, and Expert Knowledge Representation in Marketing and Related Areas of Research (pp. 319-328).
7. Duda, R. O., & Hart, P. E. (1973). Pattern classification and scene analysis. New York: John Wiley.
8. Elliot, C. D. (1990). Differential Ability Scales. San Antonio, TX: Psychological Corporation.
9. Glutting, J. J., Konold, T. R., McDermott, P. A., Kush, J. C., & Watkins, M. W. (in press). Structure and diagnostic benefits of a normative subtest taxonomy for the WISC-III standardization sample. Journal of School Psychology.
10. Holland, A. M., & McDermott, P. A. (1996). Discovering core profile types in the school-age standardization sample of the Differential Ability Scales. Journal of Psychoeducational Assessment, 14, 131-146.
11. Konold, T. R., Glutting, J. J., McDermott, P. A., & Woodcock, R. W. (in press). The development and applied benefits of a normative aptitude-achievement taxonomy on the WJ-R. Journal of Special Education.
12. McDermott, P. A., & Weiss, R. V. (1995). A normative typology of healthy, subclinical, and clinical behavior styles among American children and adolescents. Psychological Assessment, 7, 162-170.
13. Milligan, G. W. (1980). An examination of the effects of six types of error partitioning on fifteen clustering algorithms. Psychometrika, 45, 325-342.
14. Mojena, R. (1977). Hierarchical grouping methods and stopping rules - An evaluation. Computer Journal, 20, 359-363.
15. Mojena, R., & Wishart, D. (1980). Stopping rules for Ward's clustering method. In Proceedings of COMPSTAT 1980 (pp. 426-432). Wurzburg, West Germany: Physika Verlag.
16. Overall, J. E., Gibson, J. N., & Novy, D. M. (1993). Population recovery capabilities of 35 cluster analysis methods. Journal of Clinical Psychology, 49, 459-470.
17. Overall, J. E., & Magee, K. N. (1992). Replication as a rule for determining the number of clusters in hierarchical cluster analysis. Applied Psychological Measurement, 16, 119-128.
18. Sarle, W. S. (1983). Cubic clustering criterion (SAS Technical Report No. A-108). Cary, NC: SAS Institute.
19. SAS Institute. (1990). SAS/STAT (Version 6, 4th ed.). Cary, NC: Author.
20. Thorndike, R. L. (1982). Applied psychometrics. Boston: Houghton Mifflin.
21. Tryon, B. C., & Bailey, D. E. (1970). Cluster analysis. New York: McGraw-Hill.
22. Ward, J. H., Jr. (1963). Hierarchical grouping to optimize an objective function. American Statistical Journal, 58, 236-244.