Sample size and power estimates for a confirmatory factor analytic model in exercise and sport:a monte carlo approach

Research Quarterly for Exercise and Sport

Volumn 82, Issue 3, 2011, Pages 412-423

  Myers, Nicholas D ^a   Ahn, Soyeon ^a   Jin, Ying ^a  

^a UNIVERSITY OF MIAMI   (United States)

Author keywords

Coaching efficacy; Misspecification; Ordinal; Simulation

Indexed keywords

ARTICLE; EXERCISE; FACTORIAL ANALYSIS; HUMAN; MONTE CARLO METHOD; SAMPLE SIZE; SPORT;

EXERCISE; FACTOR ANALYSIS, STATISTICAL; HUMANS; MONTE CARLO METHOD; SAMPLE SIZE; SPORTS;

EID: 80053978908     PISSN: 02701367     EISSN: 21683824     Source Type: Journal    
DOI: 10.1080/02701367.2011.10599773     Document Type: Article

References (36)

1. Asparouhov, T., & Muthen, B. O. (2010). Simple second order chi-square correction. Retrieved from Mplus website: http:// www.statmodel.com/download/WLSMV_new_chi21.pdf
2. Bandalos, D. L. (2006). The use of Monte Carlo studies in structural equation modeling research. In R. C. Serlin (Series Ed.), G. R. Hancock, & R. O. Mueller (Vol. Eds.), Structural equation modeling: A second course (pp. 385–462). Greenwich, CT: Information Age.
3. Bandalos, D. L. (2008). Is parceling really necessary? A comparison of results from item parceling and categorical variable methodology. Structural Equation Modeling, 15, 211–240.
4. Beauducel, A., & Herzberg, P. Y (2006). On the performance of maximum likelihood versus means and variance adjusted weighted least squares estimation in CFA. Structural Equation Modeling, 13, 186–203.
5. Boomsma, A. (1985). Nonconvergence, improper solutions, and starting values in LISREL maximum likelihood estimation. Psychometriha, 50, 229–242.
6. Browne, M. W. (1984). Asymptotic distribution-free methods in the analysis of covariance structures. British Journal of Mathematical and Statistical Psychology, 37, 62–83.
7. Cohen, J. (1988). Statistical power for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum.
8. DiStefano, C. (2002). The impact of categorization with confirmatory factor analysis. Structural Equation Modeling 9, 327–346.
9. Dolan, C. V. (1994). Factor analysis of variables with 2, 3, 5, and 7, response categories: A comparison of categorical variable estimators using simulated data. British Journal of Mathematical and Statistical Psychology, 47, 309–326.
10. Feltz, D. L., & Chase, M. A. (1998). The measurement of self-efficacy and confidence in sport. In J. L. Duda (Ed.), Advancements in sport and exercise psychology measurement (pp. 65–80). Morgantown, WV: Fitness Information Technology.
11. Feltz, D. L., Chase, M. A., Moritz, S. E., & Sullivan, P. J. (1999). A conceptual model of coaching efficacy: Preliminary investigation and instrument development. Journal of Educational Psychology, 91, 765–776.
12. Feltz, D. L., Short, S. E., & Sullivan, P. J. (2008). Self-efficacy in sport. Champaign, IL: Human Kinetics.
13. Finney, S. J., & DiStefano, C. (2006). Nonnormal and categorical data in structural equation modeling. In R. C. Serlin (Series Ed.), G. R. Hancock, & R. O. Mueller (Vol. Eds.), Structural equation modeling: A second course (pp. 269–313). Greenwich, CT: Information Age.
14. Flora, D. B., & Curran, P. J. (2004). An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data. Psychological Methods, 9, 466–491.
15. Gagne, P., & Hancock, G. R. (2006). Measurement model quality, sample size, and solution propriety in confirmatory factor models. Multivariate Behavioral Research, 41, 65–83.
16. Gentle, J. E. (2003). Random number generation and Monte Carlo methods (2nd ed.). New York: Springer.
17. Gentle, J. E. (2005). Monte Carlo simulation. In B. S. Everitt & D. C. Howell (Eds.), Encyclopedia of statistics in behavioral science (Vol. 3, pp. 1264–1271). Hoboken, NJ: Wiley.
18. Hancock, G. R. (2006). Power analysis in covariance structure modeling. In R. C. Serlin (Series Ed.), G. R. Hancock, & R. O. Mueller (Vol. Eds.), Structuralequaticm modeling: A second course (pp. 69–115). Greenwich, CT: Information Age.
19. Hau, K-T., & Marsh, H. W. (2004). The use of item parcels in structural equation modeling: Non-normal data and small sample sizes. British Journal of Mathematical and Statistical Psychology, 57, 327–351.
20. Jackson, D. L. (2001). Sample size and number of parameter estimates in maximum likelihood confirmatory factor analysis: A Monte Carlo investigation. Structural Equation Modeling 8, 205–223.
21. Jackson, D. L. (2003). Revisiting sample size and number of parameter estimates: Some support for the N:q hypothesis. Structural Equation Modeling, 10, 128–141.
22. Kaplan, D. (1988). The impact of specification error on the estimation, testing, and improvement of structural equation models. Multivariate Behavioral Research, 23, 467–482.
23. MacCallum, R. (1986). Specification searches in covariance structure modeling. Psychological Bulletin, 100, 107–120.
24. MacCallum, R. (2003). Working with imperfect models. Multivariate Behavioral Research, 38, 113–139.
25. MacCallum, R., & Tucker, L. R. (1991). Representing sources of error in the common factor model: Implications for theory and practice. Psychological Bulletin, 100, 502–511.
26. Marsh, H. W., Hau, K.-T., Balla, J. R., & Grayson, D. (1998). Is more ever too much? The number of indicators per factor in confirmatory factor analysis. Multivariate Behavioral Research, 33, 181–220.
27. Muthen, B. O. (1984). A general structural equation model with dichotomous, ordered categorical, and continuous latent variable indicators. Psychormetriha, 49, 115–132.
28. Muthen, B. O. (1993). Goodness of fit with categorical and other non-normal variables. In K. A. Bollen, & J. S. Long (Eds.), Testing structural equation models (pp. 205–243). Newbury Park, CA: Sage.
29. Muthen, B. O., & Kaplan, D. (1985). A comparison of some methodologies for the factor analysis of non-normal Likert variables. British Journal of Mathematicaland Statistical Psychology, 38, 171–189.
30. Muthen, L. K., & Muthen, B. O. (1998–2010). Mplus user’s guide (6th ed.). Los Angeles, CA: Authors.
31. Muthen, L. K., & Muthen, B. O. (2002). How to use a Monte Carlo study to decide on sample size and determine power. Structural Equation Modeling, 9, 599–620.
32. Myers, N. D., Feltz, D. L., Chase, M. A., Reckase, M. D., & Hancock, G. R. (2008). The Coaching Efficacy Scale II-High School Teams. Educational and Psychological Measurement, 68, 1059–1076.
33. Myers, N. D., Feltz, D. L., & Wolfe, E. W. (2008). A confirmatory study of rating scale category effectiveness for the Coaching Efficacy Scale. Research Quarterly for Exercise and Sport, 19), 300–311.
34. Myers, N. D., Wolfe, E. W., & Feltz, D. L. (2005). An evaluation of the psychometric properties of the Coaching Efficacy Scale for American coaches. Measurement in PhysicalEducation and Exercise Science, 9, 135–160.
35. Paxton, P., Curran, P.J., Bollen, K. A., Kirby, J., & Chen, F. (2001). Monte Carlo experiments: Design and implementation. Structural Equation Modeling, 8, 287–312.
36. Thurstone, L. L. (1930). The learning function. Journal of General Psychology, 3, 469–470.