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Cited by 10Public Policy and Administration ResearchPublic Administration and Political AnalysisEvaluation and Performance Assessment

Introduction: Experiments in Public Management Research

James, Oliver, Jilke, Sebastian, Van Ryzin, Gregg G.·2017·Experiments in Public Management Research
DOI 10.1017/9781316676912.002
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Crossref · 10.1017/9781316676912.002

Cites

1 of 1 references verified0 citation errors · 0 not checkable

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  • The more positive survey findings are most likely due to cross-sectional surveys' vulnerability to common source bias and endogeneity.

    “Most likely, this is due to the vulnerability of cross-sectional surveys to issues such as common source bias (Favero & Bullock, 2014) and endogeneity (James, Jilke, & Van Ryzin, 2017).”

    From Latent transparency and trust in government: Unexpected findings from two survey experiments (2020) · cites this paper as James, Jilke, & Van Ryzin, 2017

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Authors
James, O., Jilke, S., & Van Ryzin, G. G.
Affiliations
  • University of Exeter (GB)
  • Rutgers, The State University of New Jersey (US)
ORCID
Venue
Experiments in Public Management Research
Publisher
Cambridge University Press
Published
2017-07-13
Pages
3-19
DOI
10.1017/9781316676912.002
OpenAlex
W2741250201
Cited by
10
References
1
Citations by year
2026: 1 · 2022: 1 · 2021: 4 · 2020: 1 · 2019: 2 · 2017: 1
FWCI
7.05
Type
book chapter
Language
en
Open access
closed
Licence
www.cambridge.org/core/terms (unspecified, 14 d)
Locations
Cambridge University Press eBooks (publishedVersion)
Topics
  • Social Sciences › Public Administration › Public Policy and Administration Research
  • Social Sciences › Political Science and International Relations › Public Administration and Political Analysis
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Evaluation and Performance Assessment
Keywords
public management research, experimental study, public administration, research methodology

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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.

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