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The Wikipedia Gender Gap Revisited: Characterizing Survey Response Bias with Propensity Score Estimation

Overview of attention for article published in PLOS ONE, June 2013
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23 news outlets
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5 blogs
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45 X users
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29 Wikipedia pages

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138 Dimensions

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100 Mendeley
Title
The Wikipedia Gender Gap Revisited: Characterizing Survey Response Bias with Propensity Score Estimation
Published in
PLOS ONE, June 2013
DOI 10.1371/journal.pone.0065782
Pubmed ID
Authors

Benjamin Mako Hill, Aaron Shaw

Abstract

Opt-in surveys are the most widespread method used to study participation in online communities, but produce biased results in the absence of adjustments for non-response. A 2008 survey conducted by the Wikimedia Foundation and United Nations University at Maastricht is the source of a frequently cited statistic that less than 13% of Wikipedia contributors are female. However, the same study suggested that only 39.9% of Wikipedia readers in the US were female - a finding contradicted by a representative survey of American adults by the Pew Research Center conducted less than two months later. Combining these two datasets through an application and extension of a propensity score estimation technique used to model survey non-response bias, we construct revised estimates, contingent on explicit assumptions, for several of the Wikimedia Foundation and United Nations University at Maastricht claims about Wikipedia editors. We estimate that the proportion of female US adult editors was 27.5% higher than the original study reported (22.7%, versus 17.8%), and that the total proportion of female editors was 26.8% higher (16.1%, versus 12.7%).

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X Demographics

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Mendeley readers

Mendeley readers

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Geographical breakdown

Country Count As %
United States 4 4%
United Kingdom 1 1%
Spain 1 1%
Italy 1 1%
Unknown 93 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 25 25%
Student > Master 19 19%
Student > Bachelor 9 9%
Researcher 8 8%
Professor > Associate Professor 6 6%
Other 19 19%
Unknown 14 14%
Readers by discipline Count As %
Social Sciences 34 34%
Computer Science 16 16%
Arts and Humanities 7 7%
Agricultural and Biological Sciences 6 6%
Business, Management and Accounting 4 4%
Other 14 14%
Unknown 19 19%