The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
The Performance of the Full Information Maximum Likelihood Estimator in Multiple Regression Models With Missing Data
Abstract
A Monte Carlo simulation examined the performance of a recently available full information maximum likelihood (FIML) estimator in a multiple regression model with missing data. The effects of four independent variables were examined (missing data technique, missing data rate, sample size, and correlation magnitude) on three outcome measures: regression coefficient bias, R 2 bias, and regression coefficient sampling variability. Three missing data patterns were examined based on Rubin’s missing data theory: missing completely at random, missing at random, and a nonrandom pattern. Results indicated that FIML estimation was superior to the three ad hoc techniques (listwise deletion, pairwise deletion, and mean imputation) across the conditions studied. FIML parameter estimates generally had less bias and less sampling variability than the three ad hoc methods.
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Cites
RUBIN, D. B. (1976) · Biometrika · doi:10.1093/biomet/63.3.581 · Cited by 9,925
Journal of The American Statistical Association
Fienberg, S. E. (2005) · Encyclopedia of Statistical Sciences · doi:10.1002/0471667196.ess1317 · Cited by 6,125
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
(1982) · Cited by 3,737
Statistical Power Analysis for the Behavioral Sciences
(1977) · doi:10.1016/c2013-0-10517-x · Cited by 2,457
Multiple Imputation for Multivariate Missing-Data Problems: A Data Analyst's Perspective
Schafer, J. L., & Olsen, M. K. (1998) · Multivariate Behavioral Research · doi:10.1207/s15327906mbr3304_5 · Cited by 1,518
Advanced Structural Equation Modeling
(2013) · Psychology Press eBooks · doi:10.4324/9781315827414 · Cited by 1,331
A Primer on Maximum Likelihood Algorithms Available for Use With Missing Data
Enders, C. K. (2001) · Structural Equation Modeling: A Multidisciplinary Journal · doi:10.1207/s15328007sem0801_7 · Cited by 1,027
Self-citationMISSING DATA: A CONCEPTUAL REVIEW FOR APPLIED PSYCHOLOGISTS
ROTH, P. L. (1994) · Personnel Psychology · doi:10.1111/j.1744-6570.1994.tb01736.x · Cited by 901
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Benner, A. D., & Graham, S. (2009) · Child Development · doi:10.1111/j.1467-8624.2009.01265.x · Cited by 411
Sleep disturbances in fibromyalgia syndrome: Relationship to pain and depression
Bigatti, S. M., Hernandez, A. M., Cronan, T. A., & Rand, K. L. (2008) · Arthritis Care & Research · doi:10.1002/art.23828 · Cited by 402
A prospective evaluation of the directionality of the depression–inflammation relationship
Stewart, J. C., Rand, K. L., Muldoon, M. F., & Kamarck, T. W. (2009) · Brain, Behavior, and Immunity · doi:10.1016/j.bbi.2009.04.011 · Cited by 390
“Parameters were estimated by full information maximum likelihood, which uses all of the observed data and is superior to traditional methods of handling missing data (Enders, 2001).”
MethodBenner, A. D., & Graham, S. (2013) · Developmental Psychology · doi:10.1037/a0030557 · Cited by 384
Metadata
- Authors
- Enders, C. K.
- Venue
- Educational and Psychological Measurement
- Publisher
- SAGE Publications
- Published
- 2001-10-01
- Volume
- 61
- Issue
- 5
- Pages
- 713-740
- ISSN
- 0013-1644 (ISSN-L)
- 0000-0000
- OpenAlex
- W4253699692
- Cited by
- 787
- References
- 25
- FWCI
- 1.64
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
