CoChat
Sign Up
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
Open this paper in CoChat — free account

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.

Overview

Integrity signals

Registry

Crossref · 10.1111/j.1540-5907.2010.00447.x

Cites

71 of 71 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

Citations

Cites

Research works printed but not linked: 1

  1. 10.1017/CBO9780511754203 · DOI · Paper page

Cited by

Sorted by citations

See all 966

Related

See all

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

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