The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Missing Data in Multiple Item Scales: A Monte Carlo Analysis of Missing Data Techniques
Abstract
Researchers in many fields use multiple item scales to measure important variables such as attitudes and personality traits, but find that some respondents failed to complete certain items. Past missing data research focuses on missing entire instruments, and is of limited help because there are few variables to help impute missing scores and the variables are often not highly related to each other. Multiple item scales offer the unique opportunity to impute missing values from other correlated items designed to measure the same construct. A Monte Carlo analysis was conducted to compare several missing data techniques. The techniques included listwise deletion, regression imputation, hot-deck imputation, and two forms of mean substitution. Results suggest that regression imputation and substituting the mean response of a person to other items on a scale are very promising approaches. Furthermore, the imputation techniques often outperformed listwise deletion.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“For the PSWQ, IES-R and DASS-21, individual mean substitution was used up to a maximum of 30% of missing items (Roth et al., 1999). Less than 1% of data was replaced using this approach.”
From Parental Anxiety and Posttraumatic Stress Symptoms in Pediatric Food Allergy. (2021) · cites this paper as Roth et al., 1999
Citations
Cites
Tabachnick, B. G., & Fidell, L. S. (1983) · Cited by 79,882
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Multiple Imputation for Nonresponse in Surveys
Rubin, D. B. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316696 · Cited by 20,701
THE BIG FIVE PERSONALITY DIMENSIONS AND JOB PERFORMANCE: A META‐ANALYSIS
BARRICK, M. R., & MOUNT, M. K. (1991) · Personnel Psychology · doi:10.1111/j.1744-6570.1991.tb00688.x · Cited by 8,689
Williams, L. J., & Anderson, S. E. (1991) · Journal of Management · doi:10.1177/014920639101700305 · Cited by 6,380
Journal of The American Statistical Association
Fienberg, S. E. (2004) · Encyclopedia of Statistical Sciences · doi:10.1002/0471667196.ess1317 · Cited by 6,289
Statistical Analysis With Missing Data
Ghosh, S., Little, R. J. A., & Rubin, D. B. (1988) · Technometrics · doi:10.2307/1269814 · Cited by 4,730
Maximum likelihood estimation from incomplete data via the EM algorithm
Dempster, A. P. (1977) · Journal of the Royal Statistical Society Series A (Statistics in Society) · Cited by 4,543
Role Conflict and Ambiguity in Complex Organizations
Rizzo, J. R., House, R. J., & Lirtzman, S. I. (1970) · Administrative Science Quarterly · doi:10.2307/2391486 · Cited by 3,700
A Review of Scale Development Practices in the Study of Organizations
Hinkin, T. R. (1995) · Journal of Management · doi:10.1177/014920639502100509 · Cited by 3,193
Research works printed but not linked: 12
- Bailar, B. A., 1982, Incomplete data in sample surveys: Proceedings of the symposium, 299
- Baker, K., 1989, Journal of Market Research Society, 31, 153
- Dawes, R. M., 1983, Judgment under uncertainty: Heuristics & biases
- Ford, B. L., 1983, Incomplete data in sample surveys. Volume II: Theory and bibliographies, 185
- Goodman, J. S., 1996, Journal of Management-Research Methods & Analysis, 22, 627
- Hackman, J. R., 1980, Work redesign
- Hunter, J. E., Ninth Annual Conference for Industrial and Organizational Psychology
- Madow, W. G., 1983, Incomplete data in sample surveys. Volume I: Report and case studies
- Madow, W. G., 1983, Incomplete data in sample surveys. Volume I: Report and case studies, 3
- McDonald, R. A., Southern Academy of Management meetings
- Rizvi, M. H., 1983, Incomplete data in sample surveys. Volume III: Proceedings of the symposium, 351
- Roth, P. L., Journal of Business & Psychology
Cited by
Sorted by citations
Psychometric validity of the 22‐item Sinonasal Outcome Test
Hopkins, C. L., Gillett, S., Slack, R., Lund, V. J., & Browne, J. P. (2009) · Clinical Otolaryngology · doi:10.1111/j.1749-4486.2009.01995.x · Cited by 1,647
Missing Data: Five Practical Guidelines
Newman, D. A. (2014) · Organizational Research Methods · doi:10.1177/1094428114548590 · Cited by 1,411
“This is like the practice that Roth, Switzer, and Switzer (1999) recommend, which they referred to as ‘‘mean substitution across items (and within an individual)’’ or ‘‘meanperson imputation’’ (pp. 214, 222; also see Downey & King, 1998), although using the technique as I am describing it here…”
BackgroundMethodDrnevich, P. L., & Kriauciunas, A. P. (2011) · Strategic Management Journal · doi:10.1002/smj.882 · Cited by 681
Plouffe, C. R., Hulland, J. S., & Vandenbosch, M. (2001) · Information Systems Research · doi:10.1287/isre.12.2.208.9697 · Cited by 690
“To retain these cases for further analysis, we followed the data imputation procedures recommended by Hair et al. (1995) and Roth et al. (1999).”
MethodMurovec, N., & Prodan, I. (2009) · Technovation · doi:10.1016/j.technovation.2009.05.010 · Cited by 474
Metadata
- Authors
- Roth, P. L., Switzer, F. S., & Switzer, D. M.
- Affiliations
- Clemson University (US)
- Venue
- Organizational Research Methods
- Publisher
- SAGE Publications
- Published
- 1999-07
- Volume
- 2
- Issue
- 3
- Pages
- 211-232
- ISSN
- 1094-4281 (ISSN-L)
- 1552-7425
- OpenAlex
- W2122169890
- Cited by
- 344
- References
- 39
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 11 · 2023: 8 · 2022: 17 · 2021: 14 · 2020: 16 · 2019: 20 · 2018: 28 · 2017: 17 · 2016: 15 · 2015: 19 · 2014: 19 · 2013: 21 · 2012: 26
- FWCI
- 2.99
- 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 › Sociology and Political Science › Survey Methodology and Nonresponse
- Keywords
- missing data, regression imputation, hot deck imputation, mean substitution, listwise deletion, imputation accuracy, attitudes, personality traits
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
