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Cited by 966Statistical Methods and Bayesian InferenceSpatial and Panel Data AnalysisPolitical Science Research and Education

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 access
Honaker, James, King, Gary·2010·American Journal of Political Science
DOI 10.1111/j.1540-5907.2010.00447.x
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Abstract

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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  1. 10.1017/CBO9780511754203 · DOI · Paper page

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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
DOI
10.1111/j.1540-5907.2010.00447.x
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
Licence
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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