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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“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
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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
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 ›
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