The DOI matches its Crossref record; 64 of the 66 references it cites were found in a registry or an index.
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]
Citations
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- Mistler S. A., 2013, Proceedings of the SAS Global Forum
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Sorted by citations
Marsh, H. W., Pekrun, R., Parker, P. D., Murayama, K., Guo, J., Dicke, T., & Arens, A. K. (2019) · Journal of Educational Psychology · doi:10.1037/edu0000281 · Cited by 535
Matthews, R. A., Pineault, L., & Hong, Y.-H. (2022) · Journal of Business and Psychology · doi:10.1007/s10869-022-09813-3 · Cited by 528
Li, G., Li, L., Choi, T., & SETHI, S. P. (2019) · Journal of Operations Management · doi:10.1002/joom.1061 · Cited by 439
Robitzsch, A. (2020) · Frontiers in Education · doi:10.3389/feduc.2020.589965 · Cited by 396
Using multi‐item psychometric scales for research and practice in human resource management
Robinson, M. A. (2017) · Human Resource Management · doi:10.1002/hrm.21852 · Cited by 367
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
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