Assessing Data Quality : An Approach and An Application
(2022) In Political Analysis 30(3). p.426-449- Abstract
Political scientists routinely face the challenge of assessing the quality (validity and reliability) of measures in order to use them in substantive research. While stand-alone assessment tools exist, researchers rarely combine them comprehensively. Further, while a large literature informs data producers, data consumers lack guidance on how to assess existing measures for use in substantive research. We delineate a three-component practical approach to data quality assessment that integrates complementary multimethod tools to assess: (1) content validity; (2) the validity and reliability of the data generation process; and (3) convergent validity. We apply our quality assessment approach to the corruption measures from the Varieties... (More)
Political scientists routinely face the challenge of assessing the quality (validity and reliability) of measures in order to use them in substantive research. While stand-alone assessment tools exist, researchers rarely combine them comprehensively. Further, while a large literature informs data producers, data consumers lack guidance on how to assess existing measures for use in substantive research. We delineate a three-component practical approach to data quality assessment that integrates complementary multimethod tools to assess: (1) content validity; (2) the validity and reliability of the data generation process; and (3) convergent validity. We apply our quality assessment approach to the corruption measures from the Varieties of Democracy (V-Dem) project, both illustrating our rubric and unearthing several quality advantages and disadvantages of the V-Dem measures, compared to other existing measures of corruption.
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- author
- McMann, Kelly ; Pemstein, Daniel ; Seim, Brigitte ; Teorell, Jan LU and Lindberg, Staffan LU
- organization
- publishing date
- 2022
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Bayesian IRT, corruption, reliability, validity
- in
- Political Analysis
- volume
- 30
- issue
- 3
- pages
- 426 - 449
- publisher
- Cambridge University Press
- external identifiers
-
- scopus:85115156693
- ISSN
- 1047-1987
- DOI
- 10.1017/pan.2021.27
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © The Author(s) 2021. Published by Cambridge University Press on behalf of the Society for Political Methodology.
- id
- edb2f56f-14d1-45f6-88fc-5e2c8a47d998
- date added to LUP
- 2021-10-12 15:34:56
- date last changed
- 2022-06-30 00:23:48
@article{edb2f56f-14d1-45f6-88fc-5e2c8a47d998, abstract = {{<p>Political scientists routinely face the challenge of assessing the quality (validity and reliability) of measures in order to use them in substantive research. While stand-alone assessment tools exist, researchers rarely combine them comprehensively. Further, while a large literature informs data producers, data consumers lack guidance on how to assess existing measures for use in substantive research. We delineate a three-component practical approach to data quality assessment that integrates complementary multimethod tools to assess: (1) content validity; (2) the validity and reliability of the data generation process; and (3) convergent validity. We apply our quality assessment approach to the corruption measures from the Varieties of Democracy (V-Dem) project, both illustrating our rubric and unearthing several quality advantages and disadvantages of the V-Dem measures, compared to other existing measures of corruption.</p>}}, author = {{McMann, Kelly and Pemstein, Daniel and Seim, Brigitte and Teorell, Jan and Lindberg, Staffan}}, issn = {{1047-1987}}, keywords = {{Bayesian IRT; corruption; reliability; validity}}, language = {{eng}}, number = {{3}}, pages = {{426--449}}, publisher = {{Cambridge University Press}}, series = {{Political Analysis}}, title = {{Assessing Data Quality : An Approach and An Application}}, url = {{http://dx.doi.org/10.1017/pan.2021.27}}, doi = {{10.1017/pan.2021.27}}, volume = {{30}}, year = {{2022}}, }