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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Citations
Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
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A test of missing completely at random for multivariate data with missing values
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Best practices for missing data management in counseling psychology
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“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.”
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Reminders make people adhere better to a self-help sleep intervention
Horsch, C., Spruit, S., Lancee, J., van Eijk, R., Beun, R. J., Neerincx, M., & Brinkman, W.-P. (2017) · Health and Technology · doi:10.1007/s12553-016-0167-x · Cited by 44
A DaQL to Monitor Data Quality in Machine Learning Applications
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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.
