The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
The impact of nonnormality on full information maximum-likelihood estimation for structural equation models with missing data.
Abstract
A Monte Carlo simulation examined full information maximum-likelihood estimation (FIML) in structural equation models with nonnormal indicator variables. The impacts of 4 independent variables were examined (missing data algorithm, missing data rate, sample size, and distribution shape) on 4 outcome measures (parameter estimate bias, parameter estimate efficiency, standard error coverage, and model rejection rates). Across missing completely at random and missing at random patterns, FIML parameter estimates involved less bias and were generally more efficient than those of ad hoc missing data techniques. However, similar to complete-data maximum-likelihood estimation in structural equation modeling, standard errors were negatively biased and model rejection rates were inflated. Simulation results suggest that recently developed correctives for missing data (e.g., rescaled statistics and the bootstrap) can mitigate problems that stem from nonnormal data.
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Cites
Statistical Analysis With Missing Data
Lahiff, M., Little, R. J. A., & Rubin, D. B. (1989) · Journal of the American Statistical Association · doi:10.2307/2289883 · Cited by 17,265
RUBIN, D. B. (1976) · Biometrika · doi:10.1093/biomet/63.3.581 · Cited by 9,925
Analysis of Incomplete Multivariate Data
Schafer, J. L. (1997) · doi:10.1201/9781439821862 · Cited by 6,012 · cited 1×
“In addition, specialized software is readily available for performing multiple imputation (Schafer, 1997), a related procedure.”
MethodAnalysis of Incomplete Multivariate Data
Booth, D. E. (2000) · Technometrics · doi:10.1080/00401706.2000.10486013 · Cited by 5,627
Curran, P. J., West, S. G., & Finch, J. F. (1996) · Psychological Methods · doi:10.1037/1082-989x.1.1.16 · Cited by 5,512
Enders, C., & Bandalos, D. (2001) · Structural Equation Modeling: A Multidisciplinary Journal · doi:10.1207/s15328007sem0803_5 · Cited by 4,978
Self-citationStatistical Analysis With Missing Data
Ghosh, S. (1988) · Technometrics · doi:10.1080/00401706.1988.10488446 · Cited by 4,576
A comparison of inclusive and restrictive strategies in modern missing data procedures.
Collins, L. M., Schafer, J. L., & Kam, C.-M. (2001) · Psychological Methods · doi:10.1037/1082-989x.6.4.330 · Cited by 2,389
Bootstrapping goodness-of-fit measures in structural equation models.
Bollen, K. A., & Stine, R. A. (1992) · Sociological Methods & Research · doi:10.1177/0049124192021002004 · Cited by 1,831
A comparison of some methodologies for the factor analysis of non‐normal Likert variables
Muthén, B., & Kaplan, D. (1985) · British Journal of Mathematical and Statistical Psychology · doi:10.1111/j.2044-8317.1985.tb00832.x · Cited by 1,778
Research works printed but not linked: 7
- 10.2307/2280845 · doi:10.2307/2280845 · Paper page
- Arbuckle, 1999, Amos (Version 4.0)
- 10.1037/0033-2909.112.2.351 · doi:10.1037/0033-2909.112.2.351 · Paper page
- Joreskog, 1993, PRELIS 2 user's reference guide
- 10.2307/1165388 · doi:10.2307/1165388
- Muthen, 1998, Mplus
- West, 1995, Structural equations with non-normal variables: Problems and remedies., 56
Cited by
Missing data: our view of the state of the art
Schafer, J. L., & Graham, J. W. (2002) · Psychological Methods · doi:10.1037/1082-989x.7.2.147 · Cited by 11,638
Missing data analysis: making it work in the real world
Graham, J. W. (2009) · Annual Review of Psychology · doi:10.1146/annurev.psych.58.110405.085530 · Cited by 6,163
Wolf, E. J., Harrington, K. M., Clark, S. L., & Miller, M. W. (2013) · Educational and Psychological Measurement · doi:10.1177/0013164413495237 · Cited by 3,931
Reporting practices in confirmatory factor analysis: an overview and some recommendations
Jackson, D. L., Gillaspy, J. A., & Purc-Stephenson, R. (2009) · Psychological Methods · doi:10.1037/a0014694 · Cited by 2,242
Antonakis, J., Avolio, B. J., & Sivasubramaniam, N. (2003) · The Leadership Quarterly · doi:10.1016/s1048-9843(03)00030-4 · Cited by 1,533
Metadata
- Authors
- Enders, C. K.
- Venue
- Psychological Methods
- Published
- 2001
- Volume
- 6
- Issue
- 4
- Pages
- 352-370
- ISSN
- 1082-989X (ISSN-L)
- 1939-1463
- OpenAlex
- W2085816378
- Cited by
- 932
- References
- 41
- FWCI
- 8.19
- Type
- journal article
- Language
- en
- Open access
- closed
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