Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation
Abstract
We propose a remedy for the discrepancy between the way political scientists analyze data with missing values and the recommendations of the statistics community. Methodologists and statisticians agree that “multiple imputation” is a superior approach to the problem of missing data scattered through one’s explanatory and dependent variables than the methods currently used in applied data analysis. The discrepancy occurs because the computational algorithms used to apply the best multiple imputation models have been slow, difficult to implement, impossible to run with existing commercial statistical packages, and have demanded considerable expertise. We adapt an algorithm and use it to implement a general-purpose, multiple imputation model for missing data. This algorithm is considerably faster and easier to use than the leading method recommended in the statistics literature. We also quantify the risks of current missing data practices, illustrate how to use the new procedure, and evaluate this alternative through simulated data as well as actual empirical examples. Finally, we offer easy-to-use software that implements all methods discussed.
Integrity signals
Registry
Cites
Statistics
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?
Missing data were imputed through multiple imputation with Stata using MICE, generating five imputed data sets.
“Missing data were imputed through multiple imputation with Stata using the MICE (multivariate imputation by chained equations) module (Royston, 2004).Five imputed data sets were generated.”
From Culture and Identity‐Protective Cognition: Explaining the White‐Male Effect in Risk Perception (2007) · cites this paper as King et al. (2001)
Cites
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013) · doi:10.1201/b16018 · Cited by 27,276 · see citing works
Multiple Imputation for Nonresponse in Surveys
Rubin, D. B. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316696 · Cited by 20,701 · see citing works
Multiple imputation after 18+ years
Rubin, D. B. (1996) · Journal of the American Statistical Association · doi:10.1080/01621459.1996.10476908 · Cited by 4,072 · see citing works
Models for Sample Selection Bias
Winship, C., & Mare, R. D. (1992) · Annual Review of Sociology · doi:10.1146/annurev.so.18.080192.001551 · Cited by 812 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Missing data: our view of the state of the art
Schafer, J. L., & Graham, J. W. (2002) · Psychological Methods · doi:10.1037/1082-989x.7.2.147 · Cited by 11,638 · see citing works
Missing data analysis: making it work in the real world
Graham, J. W. (2009) · Annual Review of Psychology · doi:10.1146/annurev.psych.58.110405.085530 · Cited by 6,163 · see citing works
Causal Inference without Balance Checking: Coarsened Exact Matching
Iacus, S. M., King, G., & Porro, G. (2012) · Political Analysis · doi:10.1093/pan/mpr013 · Cited by 3,563 · see citing works
Self-citationHow Many Imputations are Really Needed? Some Practical Clarifications of Multiple Imputation Theory
Graham, J. W., Olchowski, A. E., & Gilreath, T. D. (2007) · Prevention Science · doi:10.1007/s11121-007-0070-9 · Cited by 2,795 · see citing works
The Politics of Authoritarian Rule
Svolik, M. W. (2012) · doi:10.1017/cbo9781139176040 · Cited by 2,611 · see citing works
A comparison of inclusive and restrictive strategies in modern missing data procedures.
Collins, L. M., Schafer, J. L., & Kam, C.-M. (2001) · Psychological Methods · doi:10.1037/1082-989x.6.4.330 · Cited by 2,389 · see citing works
Fowler, J. H., & Christakis, N. A. (2008) · BMJ · doi:10.1136/bmj.a2338 · Cited by 1,990 · see citing works
Cultural cognition of scientific consensus.
Kahan, D. M., Jenkins‐Smith, H., & Braman, D. (2011) · Journal of Risk Research · doi:10.1080/13669877.2010.511246 · Cited by 1,686 · see citing works
What to do about missing values in time series cross- section data
Honaker, J., & King, G. (2010) · American Journal of Political Science · doi:10.1111/j.1540-5907.2010.00447.x · Cited by 966 · see citing works
Self-citationEffects of Volunteering on the Well-Being of Older Adults
Morrow-Howell, N., Hinterlong, J., Rozario, P. A., & Tang, F. (2003) · The Journals of Gerontology: Series B · doi:10.1093/geronb/58.3.s137 · Cited by 923 · see citing works
Motivated numeracy and enlightened self-government
KAHAN, D. M., PETERS, E., DAWSON, E. C., & SLOVIC, P. (2017) · Behavioural Public Policy · doi:10.1017/bpp.2016.2 · Cited by 883 · see citing works
Health metrics and evaluation: strengthening the science
Murray, C. J., & Frenk, J. (2008) · The Lancet · doi:10.1016/s0140-6736(08)60526-7 · Cited by 849 · see citing works
Suicide and Friendships Among American Adolescents
Bearman, P. S., & Moody, J. (2004) · American Journal of Public Health · doi:10.2105/ajph.94.1.89 · Cited by 760 · see citing works
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 · see citing works
Why is the developed world obese?
Bleich, S. N., Cutler, D., Murray, C., & Adams, A. (2008) · Annual Review of Public Health · doi:10.1146/annurev.publhealth.29.020907.090954 · Cited by 404 · see citing works
Stuck in the middle: The use and interpretation of mid-points in items on questionnaires
Nadler, J. T., Weston, R., & Voyles, E. C. (2015) · The Journal of General Psychology · doi:10.1080/00221309.2014.994590 · Cited by 396 · see citing works
Can the International Criminal Court Deter Atrocity?
Jo, H., & Simmons, B. A. (2016) · International Organization · doi:10.1017/s0020818316000114 · Cited by 370 · see citing works
Frantz, E., & Kendall-Taylor, A. (2014) · Journal of Peace Research · doi:10.1177/0022343313519808 · Cited by 266 · see citing works
Psychological Distress and Cancer Survival: A Follow-Up 10 Years After Diagnosis
Brown, K. W., Levy, A. R., Rosberger, Z., & Edgar, L. (2003) · Psychosomatic Medicine · doi:10.1097/01.psy.0000077503.96903.a6 · Cited by 213 · see citing works
Lackner, J. M., Jaccard, J., Krasner, S. S., Katz, L. A., Gudleski, G. D., & Holroyd, K. (2008) · Clinical Gastroenterology and Hepatology · doi:10.1016/j.cgh.2008.03.004 · Cited by 176 · see citing works
Kelders, S. M., Van Gemert-Pijnen, J. E. W. C., Werkman, A., Nijland, N., & Seydel, E. R. (2011) · Journal of Medical Internet Research · doi:10.2196/jmir.1624 · Cited by 156 · see citing works
Misgivings of medicine: African Americans' skepticism of psychiatric medication
Schnittker, J. (2003) · Journal of Health and Social Behavior · doi:10.2307/1519796 · Cited by 168 · see citing works
Traunmuller, R. (2011) · European Sociological Review · doi:10.1093/esr/jcq011 · Cited by 144 · see citing works
Culture and Identity‐Protective Cognition: Explaining the White‐Male Effect in Risk Perception
Kahan, D. M., Braman, D., Gastil, J., Slovic, P., & Mertz, C. K. (2007) · Journal of Empirical Legal Studies · doi:10.1111/j.1740-1461.2007.00097.x · Cited by 136 · see citing works
Daily weight-bearing activity does not increase the risk of diabetic foot ulcers
LEMASTER, J. W., REIBER, G. E., SMITH, D. G., HEAGERTY, P. J., & WALLACE, C. (2003) · Medicine & Science in Sports & Exercise · doi:10.1249/01.mss.0000074459.41029.75 · Cited by 130 · see citing works
The Incidental Pundit: Who Talks Politics with Whom, and Why?
Minozzi, W., Song, H., Lazer, D. M. J., Neblo, M. A., & Ognyanova, K. (2020) · American Journal of Political Science · doi:10.1111/ajps.12469 · Cited by 123 · see citing works
Farzadfar, F., Danaei, G., Namdaritabar, H., Rajaratnam, J. K., Marcus, J. R., Khosravi, A., Alikhani, S., Murray, C. J., & Ezzati, M. (2011) · Population Health Metrics · doi:10.1186/1478-7954-9-55 · Cited by 12 · see citing works
Sahdra, B. K., Ciarrochi, J., Parker, P., & Scrucca, L. (2016) · Frontiers in Psychology · doi:10.3389/fpsyg.2016.00189 · Cited by 100 · see citing works
Handling Missing Data in the Modeling of Intensive Longitudinal Data
Ji, L., Chow, S.-M., Schermerhorn, A. C., Jacobson, N. C., & Cummings, E. M. (2018) · Structural Equation Modeling: A Multidisciplinary Journal · doi:10.1080/10705511.2017.1417046 · Cited by 74 · see citing works
Information technology, Repeated Contracts, and the Number of Suppliers
Aral, S., Bakos, Y., & Brynjolfsson, E. (2018) · Management Science · doi:10.1287/mnsc.2016.2631 · Cited by 65 · see citing works
Gundelach, B., & Traunmüller, R. (2014) · Political Studies · doi:10.1111/1467-9248.12064 · Cited by 58 · see citing works
Ciarrochi, J., Sahdra, B., Marshall, S., Parker, P., & Horwath, C. (2014) · Journal of Contextual Behavioral Science · doi:10.1016/j.jcbs.2014.07.002 · Cited by 42 · see citing works
Craig, S. L., & McInroy, L. (2013) · Journal of Community Psychology · doi:10.1002/jcop.21570 · Cited by 32 · see citing works
Understanding adherence to web-based interventions
Kelders, S. M. (2012) · doi:10.3990/1.9789036534178 · Cited by 29 · see citing works
Kumagai, N., Tajika, A., Hasegawa, A., Kawanishi, N., Horikoshi, M., Shimodera, S., Kurata, K., Chino, B., & Furukawa, T. A. (2019) · BMC Psychiatry · doi:10.1186/s12888-019-2382-2 · Cited by 20 · see citing works
A Fast, Easy, and Efficient Estimator for Multiparty Electoral Data
Honaker, J., Katz, J. N., & King, G. (2002) · Political Analysis · doi:10.1093/pan/10.1.84 · Cited by 5 · see citing works
Self-citation
- Authors
- King, G., Honaker, J., Joseph, A., & Scheve, K.
- Affiliations
- Harvard University (US)
- Yale University (US)
- Venue
- American Political Science Review
- Publisher
- Cambridge University Press (CUP)
- Published
- 2001-03
- Volume
- 95
- Issue
- 1
- Pages
- 49-69
- ISSN
- 0003-0554 (ISSN-L)
- 1537-5943
- OpenAlex
- W2150924432
- Cited by
- 1,834
- References
- 4
- Citations by year
- 2026: 13 · 2025: 33 · 2024: 61 · 2023: 31 · 2022: 45 · 2021: 44 · 2020: 80 · 2019: 77 · 2018: 82 · 2017: 62 · 2016: 65 · 2015: 85 · 2014: 107 · 2013: 101 · 2012: 127
- FWCI
- 64.17
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- American Political Science Review (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Berkley Law Scholarship Repository (University of California, Berkeley) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Keywords
- multiple imputation, missing data, imputation algorithm, computational efficiency
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
