The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
A review of techniques for treating missing data in OM survey research
Abstract
The treatment of missing data has been overlooked by the OM literature, while other fields such as marketing, organizational behavior, economics, statistics and psychometrics have paid more attention to the issue. A review of 103 survey‐based articles published in the Journal of Operations Management between 1993 and 2001 shows that listwise deletion, which is often the least accurate technique of dealing with missing data, is heavily utilized by OM researchers. The paper also discusses the research implications of missing data, types of missing data and concludes with recommendations on which techniques should be used under different circumstances in order to improve the treatment of missing data in OM survey research.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Newman, D. A. (2003) · Organizational Research Methods · doi:10.1177/1094428103254673 · Cited by 626 · cited 2×
“001 statistics (Aldrin and Damsleth, 1989; Stinebrickner, 1999) and psychometrics (Brown, 1983; Fichman and Cummings, 2003; Newman, 2003) have paid more attention to the issue.”
BackgroundInfluentialMissing Data in Multiple Item Scales: A Monte Carlo Analysis of Missing Data Techniques
Roth, P. L., Switzer, F. S., & Switzer, D. M. (1999) · Organizational Research Methods · doi:10.1177/109442819923001 · Cited by 344 · cited 8×
“Second, missing data may result in biased estimates (Madlow et al., 1983; Roth et al., 1999) in several ways.”
BackgroundMethodInfluential
Cited by
Missing value imputation: a review and analysis of the literature (2006–2017)
Lin, W.-C., & Tsai, C.-F. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-019-09709-4 · Cited by 612
Comparison of performance of data imputation methods for numeric dataset
Jadhav, A., Pramod, D., & Ramanathan, K. (2019) · Applied Artificial Intelligence · doi:10.1080/08839514.2019.1637138 · Cited by 471
Wetherall, K., Robb, K. A., & O'Connor, R. C. (2019) · Suicide and Life-Threatening Behavior · doi:10.1111/sltb.12434 · Cited by 77
Newman, M. G., Rackoff, G. N., Zhu, Y., & Kim, H. (2023) · Journal of Anxiety Disorders · doi:10.1016/j.janxdis.2022.102662 · Cited by 42
Kassab, M., DeFranco, J., & Graciano Neto, V. (2018) · 2018 IEEE International Professional Communication Conference (ProComm) · doi:10.1109/procomm.2018.00033 · Cited by 15
Metadata
- Authors
- Tsikriktsis, N.
- Venue
- Journal of Operations Management
- Publisher
- Wiley
- Published
- 2005-05-12
- Volume
- 24
- Issue
- 1
- Pages
- 53-62
- ISSN
- 0272-6963 (ISSN-L)
- 1873-1317
- OpenAlex
- W1989203151
- Cited by
- 311
- References
- 2
- FWCI
- 4.84
- Type
- journal article
- Language
- en
- Open access
- closed
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 ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
