Citation

Hartman R, Moss AJ, Rabinowitz I, Bahn N, Rosenzweig C, Robinson J, Litman L. Behav. Res. Methods 2022; ePub(ePub): ePub.

Copyright

(Copyright © 2022, Holtzbrinck Springer Nature Publishing Group)

DOI

10.3758/s13428-022-01944-y

PMID

36131198

Abstract

People in online studies sometimes misrepresent themselves. Regardless of their motive for doing so, participant misrepresentation threatens the validity of research. Here, we propose and evaluate a way to verify the age of online respondents: a test of era-based knowledge. Across six studies (N = 1543), participants of various ages completed an age verification instrument. The instrument assessed familiarity with cultural phenomena (e.g., songs and TV shows) from decades past and present. We consistently found that our instrument discriminated between people of different ages. In Studies 1a and 1b, self-reported age correlated strongly with performance on the instrument (mean r = .8). In Study 2, the instrument reliably detected imposters who we knew were misrepresenting their age. For impostors, self-reported age did not correlate with performance on the instrument (r = .077). Finally, in Studies 3a, 3b, and 3c, the instrument remained robust with African Americans, people from low educational backgrounds, and recent immigrants to the United States. Thus, our instrument shows promise for verifying the age of online respondents, and, as we discuss, our approach of assessing "insider knowledge" holds great promise for verifying other identities within online studies.

Language: en

Keywords

Data quality; Age verification; Amazon Mechanical Turk; Online research