Crowdsourcing and personality measurement equivalence: A warning about countries whose primary language is not English

By Jennifer Feitosa, Dana L Joseph, Daniel A. Newman. 2015

118 citations recorded by the source corpus.

Subfields: Computer Science Applications, Information Systems and Management, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/6c196004e9a1712c5b44fef0e79aada54bce6074

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In Computer Science Applications

In Information Systems and Management

In Sociology and Political Science

Citations in the corpus

Crowdsourcing Consumer Research (2017, background): “…researchers to reach different samples and control for how these samples are reached, researchers may make particularly valid inferences as they make cross-cultural comparisons, provided that language barriers do not impair measurement equivalence across samples (Feitosa, Joseph, and Newman 2015).”
Systems Perspective of Amazon Mechanical Turk for Organizational Research: Review and Recommendations (2017, background): “18 Berinsky et al., 2012; Feitosa et al., 2015.”
A Tale of Two Sample Sources: Do Results from Online Panel Data and Conventional Data Converge? (2018, methodology): “Select for desired sample attributes Select respondents based on self-identified attributes and\or IP addresses (Feitosa et al., 2015).”
Do Teacher Status, Gender, and Years of Teaching Experience Impact Job Satisfaction and Work Engagement? (2020, result): “…researchers to recruit participants (Buhrmester et al., 2011), and a number of studies suggested that the quality of data collected through crowdsourcing was as good or better than the quality of data collected using more traditional survey methods (Behrend et al., 2011; Feitosa et al., 2015).”
Conducting Clinical Research Using Crowdsourced Convenience Samples. (2016, background): “…participation to US samples when international workers are not necessary, as increased error undermines the psychometric properties of instruments (Feitosa et al. 2015) and raises serious questions about the interpretability of cultural differences observed using English language materials.”
MTurk Research: Review and Recommendations (2020, background): “…including the number of participants excluded for each (Cheung, Burns, Sinclair, & Sliter, 2017), as well as decisions regarding sampling from particular countries, measurement equivalence when testing non-native English speakers, and non-naivete (Chandler et al., 2014; Feitosa et al., 2015).”
Data quality of platforms and panels for online behavioral research (2021, unknown): “Participants were asked 1 It is important to note that some research has highlighted that even with the use of US-only filter on MTurk, as much as 5 – 10% of the sample may be recruited from outside the United States (Feitosa et al., 2015( 2 The rates were chosen based on the average hourly rate on ”
Dispositional Sources of Managerial Discretion: CEO Ideology, CEO Personality, and Firm Strategies (2018, background): “Several studies have demonstrated measurement equivalence in the assessment of personality between MTurk and non-MTurk samples, particularly when participation is restricted to native English-speaking countries such as the U.S. (Behrend et al., 2011; Feitosa, Joseph, and Newman, 2015).”
Amazon's Mechanical Turk: A Comment (2017, background): “Feitosa, Joseph, and Newman (2015) found that even when MTurk country-of-origin screening is used and a U.S.-based audience is identified, international MTurkers will set up a variety of fake addresses and accounts to allow themselves to appear to be U.S. respondents.”