Pairwise deletion for missing data in structural equation models: Nonpositive definite matrices, parameter estimates, goodness of fit, and adjusted sample sizes

Structural Equation Modeling

Volumn 5, Issue 1, 1998, Pages 22-36

  Marsh, Herbert W ^a  

^a UNIVERSITY OF WESTERN SYDNEY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0040731105     PISSN: 10705511     EISSN: None     Source Type: Journal    
DOI: 10.1080/10705519809540087     Document Type: Article

References (20)

1. Allison, P. D. (1987). Estimation of linear models with incomplete data. In C. Clogg (Ed.), Sociological methodology 1987 (pp. 71-103). San Francisco: Jossey-Bass.
2. Arbuckle, J. L. (1995). Amos for Windows. Analysis of moment structures. Version 3.5. Chicago: SmallWaters.
3. Arbuckle, J. L. (1996). Full information estimation in the presence of incomplete data. In G. A. Marcouldies & R. E. Schumacker (Eds.), Advanced structural equation modeling techniques (pp. 243-277). Mahwah, NJ: Lawrence Erlbaum Associates, Inc.
4. Bentler, P. M. (1992). EQS structural equations program manual. Los Angeles: BMDP Statistical Software.
5. Bollen, K. A. (1989). Structural equations with latent variables. New York: Wiley.
6. Brown, C. H. (1983). Asymptotic comparison of missing data procedures for estimating factor loadings. Psychometrika, 48, 269-291.
7. Brown, R. L. (1994). Efficacy of the indirect approach for estimating structural equation models with missing data: A comparison of five methods. Structural Equation Modeling, I, 287-316.
8. Gerbing, D. W., & Anderson, J. C. (1987). Improper solutions in the analysis of covariance structures: Their interpretability and a comparison of alternative specifications. Psychometrika, 52, 99-111.
9. Gerbing, D. W., & Anderson, J. C. (1993). Monte Carlo evaluations of goodness-of-fit indices for structural equation models. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 40-65). Newbury Park, CA: Sage.
10. Jöreskog, K. G., & Sörbom, D. (1993). L1SREL 8: Structural equation modeling with the SIMPUS command language. Chicago: Scientific Software International.
11. Kaplan, D. (1995). The impact of BIB spiraling-induced missing data patterns on goodness-of-fit tests in factor analysis. Journal of Educational and Behavioral Statistics, 20, 69-82.
12. Little, R. J. A., & Rubin, D. B. (1987). Statistical analysis with missing data. New York: Wiley.
13. Marsh, H. W., & Balla, J. R. (1994). Goodness-of-fit indices in confirmatory factor analysis: The effect of sample size and model complexity. Quality & Quantity, 28, 185-217.
14. Marsh, H. W., Balla, J. R., & McDonald, R. P. (1988). Goodness-of-fit indices in confirmatory factor analysis: The effect of sample size. Psychological Bulletin, 102, 391-410.
15. McDonald, R. P., & Marsh, H. W. (1990). Choosing a multivariate model: Noncentrality and goodnessof- fit. Psychological Bulletin, 107, 247-255.
16. Muthén, B., Kaplan, D., & Hollis, M. (1987). On structural equation modeling with data that are not missing completely at random. Psychometrika, 52, 431-462.
17. Neale, M. C. (1994). Mx: Statistical modeling (2nd ed.). Richmond, VA: Medical College of Virginia, Department of Psychiatry.
18. Rubin, D. B. (1976). Inference and missing data. Biometrika, 63, 581-592.
19. SPSS. (1993). SPSS reference guide. Chicago: Author.
20. Wothke, W. (1993). Nonpositive definite matrices in structural modeling. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 256-293). Newbury Park, CA: Sage.