Matrix summaries improve research reports: Secondary analyses using published literature

Educational Researcher

Volumn 38, Issue 5, 2009, Pages 343-352

  Zientek, Linda Reichwein ^a   Thompson, Bruce ^b  

^a SAM HOUSTON STATE UNIVERSITY   (United States)

^b TEXAS A AND M UNIVERSITY   (United States)

Author keywords

Correlation; General linear model; Reporting standards; Secondary data analyses

Indexed keywords

EID: 77949318026     PISSN: 0013189X     EISSN: 1935102X     Source Type: Journal    
DOI: 10.3102/0013189X09339056     Document Type: Article

References (55)

1. Aiken, L. S., West, S. G., & Millsap, R. E. (2008). Doctoral training in statistics, measurement, and methodology in psychology: Replication and extension of Aiken, West, Sechrest, and Reno's (1990) survey of PhD programs in North America. American Psychologist, 63, 32-50.
2. Aiken, L. S., West, S. G., Sechrest, L., & Reno, R. R. (1990). Graduate training in statistics, methodology, and measurement in psychology: A survey of PhD programs in North America. American Psychologist, 45, 721-734.
3. American Educational Research Association. (2006). Standards for reporting on empirical social science research in AERA publications. Educational Researcher, 35(6), 33-40.
4. American Psychological Association. (2001). Publication manual of the American Psychological Association (5th ed.). Washington, DC: Author.
5. American Statistical Association. (2007). Using statistics effectively in mathematics education research. Alexandria, VA: Author.
6. Bagozzi, R. P., Fornell, C., & Larcker, D. F. (1981). Canonical correlation analysis as a special case of a structural relations model. Multivariate Behavioral Research, 16, 437-454.
7. Bray, J. H., & Maxwell, S. E. (1985). Multivariate analysis of variance. Thousand Oaks, CA: Sage.
8. Campbell, K. T., & Taylor, D. L. (1996). Canonical correlation analysis as a general linear model: A heuristic lesson for teachers and students. Journal of Experimental Education, 64, 157-171.
9. Cattell, R. B. (1966). The scree test for the number of factors. Multivariate Behavioral Research, 1, 245-276.
10. Cohen, J. (1968). Multiple regression as a general data-analytic system. Psychological Bulletin, 70, 426-433.
11. Cudeck, R. (1989). Analysis of correlation matrices using covariance structure models. Psychological Bulletin, 105, 317-327.
12. Cumming, G., & Finch, S. (2001). A primer on the understanding, use and calculation of confidence intervals that are based on central and noncentral distributions. Educational and Psychological Measurement, 61, 532-575.
13. Edgington, E. S. (1964). A tabulation of inferential statistics used in psychology journals. American Psychologist, 19, 202-203.
14. Edgington, E. S. (1974). A new tabulation of statistical procedures used in APA journals. American Psychologist, 29, 25-26.
15. Elmore, P. B., & Woehlke, P. L. (1988). Statistical methods employed in American Educational Research Journal, Educational Researcher, and Review of Educational Research from 1978 to 1987. Educational Researcher, 17(9), 19-20.
16. Fan, X. (1997). Canonical correlation analysis and structural equation modeling: What do they have in common? Structural Equation Modeling, 4, 65-79.
17. Feuer, M. J., Towne, L., & Shavelson, R. J. (2002). Scientific culture and educational research. Educational Researcher, 31(8), 4-14.
18. Fidler, F., & Thompson, B. (2001). Computing correct confidence intervals for ANOVA fixed and random-effects effect sizes. Educational and Psychological Measurement, 61, 575-604.
19. Fish, L. J. (1988). Why multivariate methods are usually vital. Measurement and Evaluation in Counseling and Development, 21, 130-137.
20. Frederick, B. N. (1999). Partitioning variance in the multivariate case: A step-by-step guide to canonical commonality analysis. In B. Thompson (Ed.), Advances in social science methodology (Vol. 5, pp. 305-318). Stamford, CT: JAI.
21. Goodwin, L. D., & Goodwin, W. L. (1985). Statistical techniques in AERJ articles, 1979-1983: The preparation of graduate students to read the educational research literature. Educational Researcher, 14(2), 5-11.
22. Gorsuch, R. L. (1983). Factor analysis (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum.
23. Graham, J. M. (2008). The general linear model as structural equation modeling. Journal of Educational and Behavioral Statistics, 33, 485-506.
24. Guttman, L. (1954). Some necessary conditions for common-factor analysis. Psychometrika, 19, 149-161.
25. Henson, R. K., & Roberts, J. K. (2006). use of exploratory factor analysis in published research: Common errors and some comment on improved practice. Educational and Psychological Measurement, 66, 393-416.
26. Henson, R. K., & Williams, C. (2006, April). Doctoral training in research methodology: A national survey of education-related degrees. Paper presented at the annual meeting of the American Educational Research Association, San Francisco.
27. Huberty, C. (1994). Applied discriminant analysis. New York: Wiley.
28. Kieffer, K. M., Reese, R. J., & Thompson, B. (2001). Statistical techniques employed in AERJ and JCP articles from 1988 to 1997: A methodological review. Journal of Experimental Education, 69, 280-309.
29. Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed.). New York: Guilford.
30. Knapp, T. R. (1978). Canonical correlation analysis: A general parametric significance testing system. Psychological Bulletin, 85, 410-416.
31. Marsh, H., Dowson, M., Pietsch, J., & Walker, R. (2004). Why multicollinearity matters: A reexamination of relations between self-efficacy, self-concept, and achievement. Journal of Educational Psychology, 96, 518-522.
32. Mood, A. R. (1969). Macro-analysis of the American educational system. Operations Research, 17, 770-784.
33. Nimon, K., Lewis, M., Kane, R., & Haynes, R. M. (2008). An R package to compute commonality coefficients in the multiple regression case: An introduction to the package and a practical example. Behavior Research Methods, 40, 457-466.
34. Pajares, F., & Graham, L. (1999). Self-efficacy, motivation constructs, and mathematics performance of entering middle school students. Contemporary Educational Psychology, 24, 124-139.
35. Pedhazur, E. J. (1997). Multiple regression in behavioral research: Explanation and prediction (3rd ed.). Fort Worth, TX: Harcourt Brace.
36. Snyder, P. A., & Thompson, B. (1998). use of tests of statistical significance and other analytic choices in a school psychology journal: Review of practices and suggested alternatives. School Psychology Quarterly, 13, 335-348.
37. Steinmayr, R., & Spinath, B. (2009). The importance of motivation as a predictor of school achievement. Learning and Individual Differences, 19, 80-90.
38. Stevens, J. P. (2002). Applied multivariate statistics for the social sciences (4th ed.). Mahwah, NJ: Lawrence Erlbaum.
39. Tatsuoka, M. M. (1971). Significance tests: Univariate and multivariate. Champaign, IL: Institute for Personality and Ability Testing.
40. Thompson, B. (1984). Canonical correlation analysis: Uses and interpretations. Thousand Oaks, CA: Sage.
41. Thompson, B. (1991). A primer on the logic and use of canonical correlation analysis. Measurement and Evaluation in Counseling and Development, 24, 80-95.
42. Thompson, B. (1995). Stepwise regression and stepwise discriminant analysis need not apply here: A guidelines editorial. Educational and Psychological Measurement, 55, 525-534.
43. Thompson, B. (2000a). Canonical correlation analysis. In L. Grimm & P. Yarnold (Eds.), Reading and understanding more multivariate statistics (pp. 285-316). Washington, DC: American Psychological Association.
44. Thompson, B. (2000b). Ten commandments of structural equation modeling. In L. Grimm & P. Yarnold (Eds.), Reading and understanding more multivariate statistics (pp. 261-284). Washington, DC: American Psychological Association.
45. Thompson, B. (2002). What future quantitative social science research could look like: Confidence intervals for effect sizes. Educational Researcher, 31(3), 24-32.
46. Thompson, B. (2004). Exploratory and confirmatory factor analysis: Understanding concepts and applications. Washington, DC: American Psychological Association.
47. Thompson, B. (2006a). Foundations of behavioral statistics: An insightbased approach. New York: Guilford.
48. Thompson, B. (2006b). Research synthesis: Effect sizes. In J. Green, G. Camilli, & P. B. Elmore (Eds.). Handbook of complementary methods in education research (pp. 583-603). Washington, DC: American Educational Research Association.
49. Thompson, B. (2008). Standards in conducting and publishing research in education. Mid-Western Educational Researcher, 21(1), 10-16. Available at http://www.coe.tamu.edu/bthompson/mwera.htm
50. Thompson, B., & Snyder, P. A. (1998). Statistical significance and reliability analyses in recent JCD research articles. Journal of Counseling and Development, 76, 436-441.
51. Wentzel, K. (2002). Are effective teachers like good parents? Teaching styles and student adjustment in early adolescence. Child Development, 73, 287-301.
52. Wilkins, J. L. M. (2008). The relationship among elementary teachers' content knowledge, attitudes, beliefs, and practices. Journal of Mathematics Teacher Education, 11, 139-164.
53. Willson, V. L. (1980). Research techniques in AERJ articles: 1969 to 1978. Educational Researcher, 9(6), 5-10.
54. Zientek, L. R., Capraro, M. M., & Capraro, R. M. (2008). Reporting practices in quantitative teacher education research: One look at the evidence cited in the AERA panel report. Educational Researcher, 37(4), 208-216.
55. Zientek, L. R., & Thompson, B. (2006). Commonality analysis: Partitioning variance to facilitate better understanding of data. Journal of Early Intervention, 28, 299-307.