The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
A Review of Methods for Missing Data
Abstract
This paper reviews methods for handling missing data in a research study. Many researchers use ad hoc methods such as complete case analysis, available case analysis (pairwise deletion), or single-value imputation. Though these methods are easily implemented, they require assumptions about the data that rarely hold in practice. Model-based methods such as maximum likelihood using the EM algorithm and multiple imputation hold more promise for dealing with difficulties caused by missing data. While model-based methods require specialized computer programs and assumptions about the nature of the missing data, these methods are appropriate for a wider range of situations than the more commonly used ad hoc methods. The paper provides an illustration of the methods using data from an intervention study designed to increase students’ ability to control their asthma symptoms.
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Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Multiple Imputation for Nonresponse in Surveys
Rubin, D. B. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316696 · Cited by 20,701
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
Multiple imputation after 18+ years
Rubin, D. B. (1996) · Journal of the American Statistical Association · doi:10.1080/01621459.1996.10476908 · Cited by 4,072
Cited by
Impacts of the COVID-19 pandemic on life of higher education students: A global perspective
Aristovnik, A., Keržič, D., Ravšelj, D., Tomaževič, N., & Umek, L. (2020) · Sustainability · doi:10.3390/su12208438 · Cited by 2,047
Best practices for missing data management in counseling psychology
Schlomer, G. L., Bauman, S., & Card, N. A. (2010) · Journal of Counseling Psychology · doi:10.1037/a0018082 · Cited by 1,835
Patient reminder and recall interventions to improve immunization rates
Jacobson Vann, J. C., Jacobson, R. M., Coyne-Beasley, T., Asafu-Adjei, J. K., & Szilagyi, P. G. (2018) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd003941.pub3 · Cited by 396
Maheswaran, H., Weich, S., Powell, J., & Stewart-Brown, S. (2012) · Health and Quality of Life Outcomes · doi:10.1186/1477-7525-10-156 · Cited by 265
“As we were evaluating the instrument and not the interventions we assumed that data was missing at random [24] and undertook a complete case analysis.”
MethodRisk factors associated with mortality at a milk-fed veal calf facility: A prospective cohort study
Renaud, D. L., Duffield, T. F., LeBlanc, S. J., Ferguson, S., Haley, D. B., & Kelton, D. F. (2018) · Journal of Dairy Science · doi:10.3168/jds.2017-13581 · Cited by 118
Metadata
- Authors
- Pigott, T. D.
- Venue
- Educational Research and Evaluation
- Publisher
- Informa UK Limited
- Published
- 2001-12
- Volume
- 7
- Issue
- 4
- Pages
- 353-383
- ISSN
- 1380-3611 (ISSN-L)
- 1744-4187
- OpenAlex
- W2116781287
- Cited by
- 650
- References
- 4
- FWCI
- 1.64
- 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.
