Sample Size Requirements for Structural Equation Models: An Evaluation of Power, Bias, and Solution Propriety

Educational and Psychological Measurement

Volumn 73, Issue 6, 2013, Pages 913-934

  Wolf, Erika J ^a,b   Harrington, Kelly M ^a,b   Clark, Shaunna L ^c   Miller, Mark W ^a,b  

^a VA BOSTON HEALTHCARE SYSTEM   (United States)

^b BOSTON UNIVERSITY SCHOOL OF MEDICINE   (United States)

^c VIRGINIA COMMONWEALTH UNIVERSITY   (United States)

Author keywords

bias; confirmatory factor analysis; Monte Carlo simulation; sample size; solution propriety; statistical power; structural equation modeling

Indexed keywords

EID: 84886428907     PISSN: 00131644     EISSN: 15523888     Source Type: Journal    
DOI: 10.1177/0013164413495237     Document Type: Article

References (37)

1. Alhija F. N.-A.,Wisenbaker J.A Monte Carlo study investigation the impact of item parceling strategies on parameter estimates and their standard errors in CFA.Structural Equation Modeling. 2006;13:204-228.
2. Bentler P. M.,Chou C. H.Practical issues in structural modeling.Sociological Methods & Research. 1987;16:78-117.
3. Bollen K. A.Structural equations with latent variables. New York, NY: John Wiley; 1989:.
4. Boomsma A.Systems under indirection observation: Causality, structure, prediction (Part I). Joreskog K. G.Wold H., ed. Amsterdam, Netherlands: North Holland; 1982:149-173.
5. Boomsma A.Nonconvergence, improper solutions, and starting values in LISREL maximum likelihood estimation.Psychometrika. 1985;50:229-242.
6. Brown T. A.Confirmatory factor analysis for applied research. New York, NY: Guilford Press; 2006:.
7. Cohen J.Statistical power analysis for the behavioral sciences. 1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.Hillsdale, NJ: Erlbaum; 1988:.
8. Cole D. A.,Maxwell S. E.Testing meditational models with longitudinal data: Questions and tips in the use of structural equation modeling.Journal of Abnormal Psychology. 2003;112:558-577.
9. Davey A.,Salva J.Estimating statistical power with incomplete data.Organizational Research Methods. 2009a;12:320-346.
10. Davey A.,Salva J.Statistical power analysis with missing data: A structural equation modeling approach. New York, NY: Routledge; 2009b:.
11. Dolan C.,van der Sluis S.,Grasman R.A note on normal theory power calculation in SEM with data missing completely at random.Structural Equation Modeling. 2005;12:245-262.
12. Enders C. K.The impact of nonnormality on full information maximum-likelihood estimation for structural equation models with missing data.Psychological Methods. 2001;6:352-370.
13. Enders C. K.,Bandalos D. L.The relative performance of full information maximum likelihood estimation for missing data in structural equation models.Structural Equation Modeling. 2001;8:430-457.
14. Gagné P.,Hancock G. R.Measurement model quality, sample size, and solution propriety in confirmatory factor models.Multivariate Behavioral Research. 2006;41:65-83.
15. Hancock G. R.,Lawrence F. R.,Nevitt J.Type I error and power of latent mean methods and MANOVA in factorially invariant and noninvariant latent variable systems.Structural Equation Modeling. 2000;7:534-556.
16. Hertzog C.,von Oertzen T.,Ghisletta P.,Lindenberger U.Evaluating the power of latent growth curve models to detect individual differences in change.Structural Equation Modeling. 2008;15:541-563.
17. Jackson D. L.Sample size and number of parameter estimates in maximum likelihood confirmatory factor analysis: A Monte Carlo investigation.Structural Equation Modeling. 2001;8:205-223.
18. Kelley K.,Maxwell S. E.Sample size for multiple regression: Obtaining regression coefficients that are accurate, not simply significant.Psychological Methods. 2003;8:305-321.
19. Kim K. H.The relation among fit indexes, power, and sample size in structural equation modeling.Structural Equation Modeling. 2005;12:368-390.
20. Little R. J.,Rubin D. B.The analysis of social science data with missing values.Sociological Methods and Research. 1989;18:292-326.
21. MacCallum R. C.,Browne M. W.,Sugawara H. M.Power analysis and determination of sample size for covariance structural modeling.Psychological Methods. 1996;1:130-149.
22. MacCallum R. C.,Lee T.,Browne M. W.The issue of isopower in power analysis for tests of structural equation models.Structural Equation Modeling. 2010;17:23-41.
23. MacCallum R. C.,Widaman K. F.,Zhang S.,Hong S.Sample size in factor analysis.Psychological Methods. 1999;4:84-99.
24. Marcoulides G. A.Evaluation of confirmatory factor analytic and structural equation models using goodness-of-fit indices.Psychological Reports. 1990;67:669-670.
25. Marcoulides G. A.,Chin W.New perspectives in partial least squares and related methods. Abdi H.Chin W.Vinzi V. E.Russolillo G.Trinchera L., ed. Berlin, Germany: Springer-Verlag; in press:.
26. Marcoulides G. A.,Saunders C.PLS: A silver bullet?.MIS Quarterly. 2006;30:iii-ix.
27. Marsh H. W.,Hau K.-T.,Balla J. R.,Grayson D.Is more ever too much? The number of indicators per factor in confirmatory factor analysis.Multivariate Behavioral Research. 1998;33:181-220.
28. Maxwell S. E.The persistence of underpowered studies in psychological research: Causes, consequences, and remedies.Psychological Methods. 2004;9:147-163.
29. Maxwell S. E.,Kelley K.,Rausch J. R.Sample size planning for statistical power and accuracy in parameter estimation.Annual Review of Psychology. 2008;59:537-563.
30. MuthénB. (2002). Using Mplus Monte Carlo simulations in practice: A note on assessing estimation quality and power in latent variable models (Mplus Web Notes, No. 1). Retrieved from http://www.statmodel.com/download/webnotes/mc1.pdf.
31. MuthénB.AsparouhovT. (2002). Using Mplus Monte Carlo simulations in practice: A note on non-normal missing data in latent variable models (Mplus Web Notes, No. 2). Retrieved from http://www.statmodel.com/download/webnotes/mc2.pdf.
32. Muthén L. K.,Muthén B. O.Mplus user's guide. 1998-2008). Mplus user's guide (5th ed.). Los Angeles, CA: Muthén & Muthén.Los Angeles, CA: Muthén & Muthén; 1998-2008:.
33. Muthén L. K.,Muthén B. O.How to use a Monte Carlo study to decide on sample size and determine power.Structural Equation Modeling. 2002;4:599-620.
34. Nunnally J. C.Psychometric theory. New York, NY: McGraw-Hill; 1967:.
35. Paxton P.,Curran P. J.,Bollen K. A.,Kirby J.,Bhen F.Monte Carlo experiments: Design and implementation.Structural Equation Modeling. 2001;8:287-312.
36. Satorra A.,Saris W. E.Power of the likelihood ratio test in covariance structure analysis.Psychometrika. 1985;50:83-90.
37. Velicer W. F.,Fava J. L.Effects of variable and subject sampling on factor pattern recovery.Psychological Methods. 1998;3:231-251.