Playing statistical Ouija board with commonality analysis: Good questions, wrong assumptions

Applied Neuropsychology

Volumn 15, Issue 1, 2008, Pages 44-53

  Schneider, W Joel ^a  

^a ILLINOIS STATE UNIVERSITY   (United States)

Author keywords

Commonality analysis; General factor of intelligence

Indexed keywords

ANALYSIS OF VARIANCE; ATTENTION; CORRELATION COEFFICIENT; HUMAN; INTELLIGENCE QUOTIENT; REVIEW; TEST RETEST RELIABILITY;

DATA INTERPRETATION, STATISTICAL; HUMANS; MODELS, STATISTICAL; REGRESSION ANALYSIS; RESEARCH DESIGN;

EID: 41449093532     PISSN: 09084282     EISSN: 15324826     Source Type: Journal    
DOI: 10.1080/09084280801917566     Document Type: Review

References (27)

1. Amado, A. J. (2003). Partitioning predicted variance into constituent parts: A primer on regression commonality analysis. Research in the Schools, 10, 91-97.
2. Cohen, J., Cohen, P., Aiken, L. S., & West, S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.) Mahweh, NJ: Lawrence Erlbaum Associates.
3. Dana, J., & Dawes, R. (2007). Comment on Fiorello et al. Interpreting Intelligence Test Results for Children with Disabilities: Is Global Intelligence Relevant? Applied Neuropsychology, 14, 21-25.
4. Duncan, J., Seitz, R. J., Kolodny, J., Bor, D., Herzog, H., Ahmed, A., et al. (2002). A neural basis for general intelligence. Science, 289, 457-460.
5. Faust, D. (2007). Some global and specific thoughts about some global and specific issues. Applied Neuropsychology, 14, 26-36.
6. Fiorello, C. A., Hale, J. B., Holdnack, J. A., Kavanagh, J. A., Terrell, J., & Long, L. (2007). Interpreting intelligence test results for children with disabilities: Is global intelligence relevant? Applied Neuropsychology, 21, 2-12.
7. Fiorello, C. A., Hale, J. B., McGrath, M., Ryan, K., & Quinn, S. (2002). IQ interpretation for children with flat and variable test profiles. Learning and Individual Differences, 13, 115-125.
8. Flanagan, D. P., McGrew, K. S., & Ortiz, S. O. (1999). The Wechsler Intelligence Scales and Gf-Gc theory. Boston, MA: Allyn and Bacon.
9. Glutting, J. J., Watkins, M. W., Konold, T. R., & McDermott, P. A. (2006). Distinctions without a difference: The utility of observed versus latent factors from the WISC-IV in estimating reading and math achievement on the WIAT-II. Journal of Special Education, 40, 103-114.
10. Glutting, J. J., Youngstrom, E. A., Ward, T., Ward, S., & Hale, R. L. (1997). Incremental efficacy of WISC-III factor scores in predicting achievement: What do they tell us? Psychological Assessment, 9, 295-301.
11. Hale, J. B., & Fiorello, C. A. (2004). School neuropsychology: A practitioner's handbook. New York: Guilford Press.
12. Hale, J. B., Fiorello, C. A., Bertin, M., & Sherman, R. (2003). Predicting math competency through neuropsychological interpretation of WISC-III variance components. Journal of Psychoeducational Assessment, 21, 358-380.
13. Hale, J. B., Fiorello, C. A., Kavanagh, J. A., Holdnack, J. A., & Aloe, A. M. (2007). Is the demise of IQ interpretation justified? A response to special issue authors. Applied Neuropsychology, 21, 37-51.
14. Hale, J. B., Fiorello, C. A., Kavanagh, J. A., Hoeppner, J. B., & Gaither, R. A. (2001). WISC-III predictors of academic achievement for children with learning disabilities: Are global and factor scores comparable? School Psychology Quarterly, 16, 31-55.
15. Jensen, A. R. (1998). The g factor. Westport, CT: Praeger.
16. Mayes, S. D., & Calhoun, S. L. (2004). Similarities and differences in Wechsler Intelligence Scale for Children - Third Edition (WISC-III) profiles: Support for subtest analysis in clinical referrals. The Clinical Neuropsychologist, 18, 559-572.
17. McGrew, K. S., Flanagan, D. P., Keith, T. Z., & Vanderwood, M. (1997). Beyond g: The impact of Gf-Gc specific cognitive abilities research on the future use and interpretation of intelligence tests in the schools. School Psychology Review, 26, 189-210.
18. Newton, R. G., & Spurrell, D. J. (1967). A development of multiple regression for the analysis of routine data. Applied Statistics, 16, 51-65.
19. Pedhazur, E. J. (1982). Multiple regression in behavioral research: Explanation and prediction (2nd ed., pp. 199-211). Orlando, FL: Holt, Rinehart, & Winston.
20. Pedhazur, E. J. (1997). Multiple regression in behavioral research: Explanation and prediction (3rd ed., pp. 261-280). Orlando, FL: Holt, Rinehart, & Winston.
21. Seibold, D. R., & McPhee, R. D. (1979). Commonality analysis: A method for decomposing explained variance in multiple regression analyses. Human Communication Research, 5, 355-365.
22. Spearman, C. (1904). General intelligence, objectively determined and measured. American Journal of Psychology, 15, 201-293.
23. Spearman, C. (1910). Correlation calculated with faulty data. British Journal of Psychology, 3, 271-295.
24. Thompson, B. (1994). Guidelines for authors. Educational and Psychological Measurement, 54, 837-847.
25. Thomson, G. H. (1939). The factorial analysis of human ability. London: University of London Press.
26. Watkins, M. W., Glutting, J. J., & Lei, P. (2007). Validity of the full-scale IQ when there is significant variability among WISC-III and WISC-IV factor scores. Applied Neuropsychology, 14, 13-20.
27. Zientek, L. R., & Thompson, B. (2006). Commonality analysis: Partitioning variance to facilitate better understanding of data. Journal of Early Intervention, 28, 299-307.