Missing Data

Allison, Paul

doi:10.4135/9781412985079

Cited by 2,050Statistical Methods and Bayesian InferenceStatistical Methods and ApplicationsStatistical and numerical algorithms

Missing Data

Allison, Paul·2002·Sage eBooks

DOI 10.4135/9781412985079

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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).
“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
The FIML-estimated models produced results 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.
“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]

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Authors: Allison, P.
Corresponding author: Allison, Paul
Venue: Sage eBooks
Publisher: SAGE Publications, Inc.
Published: 2002
DOI: 10.4135/9781412985079
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

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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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Missing Data

Integrity signals

Claims the paper makes

Claims other papers make about this one

Cited by

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Record

About these checks