The DOI matches its Crossref record.
Missing Data
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Thus, listwise deletion was used, because it is a reasonable choice for handling missing data under such circumstances, and other approaches such as imputation are only appropriate when data are reasonably assumed to be missing at random (MAR) or missing completely at random (MCAR) (Allison, 2002;Manly & Wells, 2015).”
From Competing hypotheses about online versus face-to-face course outcomes: Analysis leveraging the unique circumstances of the COVID-19 pandemic (2025) · cites this paper as Allison, 2002
In the citing paper
“The FIML-estimated models produced results that are highly similar to those produced by models estimated using listwise deletion, which suggests that listwise deletion does not produce substantially more biased estimates when the data are missing for predictor variables [51].”
From Poverty, Material Hardship, and Children’s Outcomes: A Nuanced Understanding of Material Hardship in Childhood (2022) · cites this paper as [51]
Citations
Cited by
Sorted by citations
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
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
Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 2.0
Percie du Sert, N., Ahluwalia, A., Alam, S., Avey, M. T., Baker, M., Browne, W. J., Clark, A., Cuthill, I. C., Dirnagl, U., Emerson, M., et al. (2020) · PLOS Biology · doi:10.1371/journal.pbio.3000411 · Cited by 2,677
Acock, A. (2005) · Journal of Marriage and the Family · doi:10.1111/j.1741-3737.2005.00191.x · Cited by 1,955
4. Regression with Missing Ys: An Improved Strategy for Analyzing Multiply Imputed Data
von Hippel, P. T. (2007) · Sociological Methodology · doi:10.1111/j.1467-9531.2007.00180.x · Cited by 1,495
Metadata
- Authors
- Allison, P.
- Corresponding author
- Allison, Paul
- Venue
- Sage eBooks
- Publisher
- SAGE Publications, Inc.
- Published
- 2002
- OpenAlex
- W4205701142
- Cited by
- 2,050
- References
- 0
- Citations by year
- 2026: 81 · 2025: 79 · 2024: 72 · 2023: 101 · 2022: 122 · 2021: 122 · 2020: 120 · 2019: 91 · 2018: 97 · 2017: 103 · 2016: 88 · 2015: 90 · 2014: 109 · 2013: 110 · 2012: 107
- FWCI
- 8.09
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Sage eBooks (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Physical Sciences › Mathematics › Applied Mathematics › Statistical and numerical algorithms
- Keywords
- missing data
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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