Missing Data: Five Practical Guidelines

Newman, Daniel A.

doi:10.1177/1094428114548590

Cited by 1,411Statistical Methods and Bayesian InferencePsychometric Methodologies and TestingMeta-analysis and systematic reviews

Missing Data: Five Practical Guidelines

Newman, Daniel A.·2014·Organizational Research Methods

DOI 10.1177/1094428114548590

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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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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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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
DOI: 10.1177/1094428114548590
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
Licence: journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
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

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