The DOI matches its Crossref record; 71 of the 71 references it cites were found in a registry or an index.
What to do about missing values in time series cross- section data
Open accessAbstract
Applications of modern methods for analyzing data with missing values, based primarily on multiple imputation, have in the last half‐decade become common in American politics and political behavior. Scholars in this subset of political science have thus increasingly avoided the biases and inefficiencies caused by ad hoc methods like listwise deletion and best guess imputation. However, researchers in much of comparative politics and international relations, and others with similar data, have been unable to do the same because the best available imputation methods work poorly with the time‐series cross‐section data structures common in these fields. We attempt to rectify this situation with three related developments. First, we build a multiple imputation model that allows smooth time trends, shifts across cross‐sectional units, and correlations over time and space, resulting in far more accurate imputations. Second, we enable analysts to incorporate knowledge from area studies experts via priors on individual missing cell values, rather than on difficult‐to‐interpret model parameters. Third, because these tasks could not be accomplished within existing imputation algorithms, in that they cannot handle as many variables as needed even in the simpler cross‐sectional data for which they were designed, we also develop a new algorithm that substantially expands the range of computationally feasible data types and sizes for which multiple imputation can be used. These developments also make it possible to implement the methods introduced here in freely available open source software that is considerably more reliable than existing algorithms.
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In the citing paper
“The Amelia II R package was selected due to the statistical validity of its approach in handling time-series, cross-sectional data (Honaker & King, 2010;Honaker et al., 2011).”
From A single-case experimental evaluation of a new group-based intervention to enhance adjustment to life with acquired brain injury: VaLiANT (valued living after neurological trauma) (2022) · cites this paper as Honaker & King, 2010
In the citing paper
“Given that the bootstrapping method does not allow the sample to include missing values, multiple imputation was performed (Honaker & King, 2010).”
From The reciprocal and indirect relationships between passive Facebook use, comparison on Facebook, and adolescents' body dissatisfaction (2017) · cites this paper as Honaker & King, 2010
Citations
Cites
Multiple Imputation for Nonresponse in Surveys
Rubin, D. B. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316696 · Cited by 20,701
Statistical Analysis With Missing Data
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Statistical Analysis with Missing Data
Little, R. J. A., & Rubin, D. B. (2002) · Wiley Series in Probability and Statistics · doi:10.1002/9781119013563 · Cited by 8,473
Hsiao, C. (2022) · doi:10.1017/9781009057745 · Cited by 7,869
Ethnicity, Insurgency, and Civil War
FEARON, J. D., & LAITIN, D. D. (2003) · American Political Science Review · doi:10.1017/s0003055403000534 · Cited by 7,366
What To Do (and Not to Do) with Time-Series Cross-Section Data
Beck, N., & Katz, J. N. (1995) · American Political Science Review · doi:10.2307/2082979 · Cited by 6,840
Greed and grievance in civil war
Collier, P. D. (2004) · Oxford Economic Papers · doi:10.1093/oep/gpf064 · Cited by 6,295
Analysis of Incomplete Multivariate Data
Schafer, J. L. (1997) · doi:10.1201/9781439821862 · Cited by 6,012
Analysis of Incomplete Multivariate Data
Booth, D. E., & Schafer, J. L. (2000) · Technometrics · doi:10.2307/1271466 · Cited by 5,627
Research works printed but not linked: 1
- 10.1017/CBO9780511754203 · DOI · Paper page
Cited by
Sorted by citations
The Politics of Authoritarian Rule
Svolik, M. W. (2012) · doi:10.1017/cbo9781139176040 · Cited by 2,611
Amelia II: A Program for Missing Data
Honaker, J., King, G., & Blackwell, M. (2011) · Journal of Statistical Software · doi:10.18637/jss.v045.i07 · Cited by 2,552
Self-citationBeyens, I., Frison, E., & Eggermont, S. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.05.083 · Cited by 847
Modeling Dynamics in Time-Series–Cross-Section Political Economy Data
Beck, N., & Katz, J. N. (2011) · Annual Review of Political Science · doi:10.1146/annurev-polisci-071510-103222 · Cited by 825
Shih, V., Adolph, C., & Liu, M. (2012) · American Political Science Review · doi:10.1017/s0003055411000566 · Cited by 819
Metadata
- Authors
- Honaker, J., & King, G.
- Affiliations
- Pennsylvania State University (US)
- Harvard University (US)
- ORCID
- King, Gary
- Venue
- American Journal of Political Science
- Publisher
- Wiley
- Published
- 2010-04
- Volume
- 54
- Issue
- 2
- Pages
- 561-581
- ISSN
- 0092-5853 (ISSN-L)
- 1540-5907
- OpenAlex
- W2125291718
- Cited by
- 966
- References
- 71
- Citations by year
- 2026: 44 · 2025: 41 · 2024: 27 · 2023: 41 · 2022: 45 · 2021: 60 · 2020: 64 · 2019: 77 · 2018: 77 · 2017: 54 · 2016: 55 · 2015: 90 · 2014: 83 · 2013: 74 · 2012: 68
- FWCI
- 48.73
- Type
- journal article
- Language
- en
- Open access
- green
- nrs.harvard.edu
- Locations
- American Journal of Political Science (publishedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
- Social Sciences › Political Science and International Relations › Political Science Research and Education
- Keywords
- missing data, multiple imputation, time-series cross-section data, imputation algorithm, temporal trends, spatial correlation, listwise deletion, open source software
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