The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
Missing Data in Educational Research: A Review of Reporting Practices and Suggestions for Improvement
Abstract
Missing data analyses have received considerable recent attention in the methodological literature, and two “modern” methods, multiple imputation and maximum likelihood estimation, are recommended. The goals of this article are to (a) provide an overview of missing-data theory, maximum likelihood estimation, and multiple imputation; (b) conduct a methodological review of missing-data reporting practices in 23 applied research journals; and (c) provide a demonstration of multiple imputation and maximum likelihood estimation using the Longitudinal Study of American Youth data. The results indicated that explicit discussions of missing data increased substantially between 1999 and 2003, but the use of maximum likelihood estimation or multiple imputation was rare; the studies relied almost exclusively on listwise and pairwise deletion.
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Cites
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Multiple Imputation for Nonresponse in Surveys
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Lahiff, M., Little, R. J. A., & Rubin, D. B. (1989) · Journal of the American Statistical Association · doi:10.2307/2289883 · Cited by 17,265
Hierarchical Linear Models: Applications and Data Analysis Methods
Davidian, M. (2003) · Journal of the American Statistical Association · doi:10.1198/jasa.2003.s288 · Cited by 13,723
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
RUBIN, D. B. (1976) · Biometrika · doi:10.1093/biomet/63.3.581 · Cited by 9,925
A test of missing completely at random for multivariate data with missing values
Little, R. J. A. (1988) · Journal of the American Statistical Association · doi:10.1080/01621459.1988.10478722 · Cited by 8,963
Statistical Analysis with Missing Data
Little, R. J. A., & Rubin, D. B. (2002) · Wiley Series in Probability and Statistics · doi:10.1002/9781119013563 · Cited by 8,473
Journal of The American Statistical Association
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Cited by
Multiple imputation of discrete and continuous data by fully conditional specification
van Buuren, S. (2007) · Statistical Methods in Medical Research · doi:10.1177/0962280206074463 · Cited by 2,827
Principled missing data methods for researchers
Dong, Y., & Peng, C.-Y. J. (2013) · SpringerPlus · doi:10.1186/2193-1801-2-222 · Cited by 2,208
Teachers’ Mathematical Knowledge, Cognitive Activation in the Classroom, and Student Progress
Baumert, J., Kunter, M., Blum, W., Brunner, M., Voss, T., Jordan, A., Klusmann, U., Krauss, S., Neubrand, M., & Tsai, Y.-M. (2010) · American Educational Research Journal · doi:10.3102/0002831209345157 · Cited by 2,100
An introduction to modern missing data analyses
Baraldi, A. N., & Enders, C. K. (2010) · Journal of School Psychology · doi:10.1016/j.jsp.2009.10.001 · Cited by 1,452
Self-citationMissing Data: Five Practical Guidelines
Newman, D. A. (2014) · Organizational Research Methods · doi:10.1177/1094428114548590 · Cited by 1,411
Metadata
- Authors
- Peugh, J. L., & Enders, C. K.
- Venue
- Review of Educational Research
- Published
- 2004-12
- Volume
- 74
- Issue
- 4
- Pages
- 525-556
- ISSN
- 0034-6543 (ISSN-L)
- 1935-1046
- OpenAlex
- W2130836428
- Cited by
- 1,112
- References
- 49
- FWCI
- 7.87
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
