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Missing Data: Five Practical Guidelines
Abstract
Missing data (a) reside at three missing data levels of analysis (item-, construct-, and person-level), (b) arise from three missing data mechanisms (missing completely at random, missing at random, and missing not at random) that range from completely random to systematic missingness, (c) can engender two missing data problems (biased parameter estimates and inaccurate hypothesis tests/inaccurate standard errors/low power), and (d) mandate a choice from among several missing data treatments (listwise deletion, pairwise deletion, single imputation, maximum likelihood, and multiple imputation). Whereas all missing data treatments are imperfect and are rooted in particular statistical assumptions, some missing data treatments are worse than others, on average (i.e., they lead to more bias in parameter estimates and less accurate hypothesis tests). Social scientists still routinely choose the more biased and error-prone techniques (listwise and pairwise deletion), likely due to poor familiarity with and misconceptions about the less biased/less error-prone techniques (maximum likelihood and multiple imputation). The current user-friendly review provides five easy-to-understand practical guidelines, with the goal of reducing missing data bias and error in the reporting of research results. Syntax is provided for correlation, multiple regression, and structural equation modeling with missing data.
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“FIML chooses parameter estimates that maximise this likelihood function based on complete cases, thereby providing robust parameter estimates, despite missing data.40”
From Variant connective tissue (joint hypermobility) and its relevance to depression and anxiety in adolescents: a cohort-based case–control study (2022) · cites this paper as [40]
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Metadata
- Authors
- Newman, D. A.
- Affiliations
- University of Illinois Urbana-Champaign (US)
- Corresponding author
- Newman, Daniel A.
- ORCID
- Newman, Daniel A.
- Venue
- Organizational Research Methods
- Publisher
- SAGE Publications
- Published
- 2014-09-26
- Volume
- 17
- Issue
- 4
- Pages
- 372-411
- ISSN
- 1094-4281 (ISSN-L)
- 1552-7425
- OpenAlex
- W2047813468
- Cited by
- 1,411
- References
- 66
- Citations by year
- 2026: 188 · 2025: 225 · 2024: 234 · 2023: 174 · 2022: 153 · 2021: 141 · 2020: 96 · 2019: 68 · 2018: 50 · 2017: 34 · 2016: 29 · 2015: 17 · 2014: 1
- FWCI
- 28.54
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Organizational Research Methods (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- missing data, missing data mechanisms, missing completely at random, missing at random, missing not at random, listwise deletion, pairwise deletion, single imputation, maximum likelihood estimation, multiple imputation, missing data bias, statistical power, structural equation modeling, multiple linear regression, correlation
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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