Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Feb 10, 2023
Date Accepted: Jun 29, 2023
Effects of Excluding Those Who Report Having “Syndomitis” or “Chekalism” on Data Quality: Longitudinal Health Survey of a Sample from Amazon’s Mechanical Turk
ABSTRACT
Background:
Researchers have implemented a variety of approaches to increase data quality from existing online panels such as Amazon’s Mechanical Turk (MTurk).
Objective:
This study extends prior work by examining improvements in data quality and effects on mean estimates of health status by excluding respondents who endorse either or both of two fake health conditions (“Syndomitis” and “Checkalism”).
Methods:
Data were collected in 2021 from MTurk study participants, 18 years or older, with an internet protocol address in the United States who had completed a minimum of 500 previous MTurk “human intelligence tasks.” The survey included questions about demographic characteristics, health conditions (including two fake conditions), and the Patient Reported Outcomes Measurement Information System (PROMIS®)-29+2 v2.1.
Results:
Fifteen percent (n = 996 out of 6832) of the sample endorsed at least one of the two fake conditions at baseline. Those who endorsed a fake condition at baseline were more likely to be male, non-White, younger, report more health conditions, and take longer to complete the survey than those who did not endorse a fake condition. They also had lower score reliability and reported significantly worse self-reported health scores than those who did not endorse a fake condition. Excluding those who endorsed a fake condition reduced the overall mean PROMIS-29+2 v2.1 T-scores by 1-2 points and the PROPr preference-based score by 0.04.
Conclusions:
This study provides evidence that asking about fake health conditions can help to screen out respondents who may be dishonest or careless respondents. Clinical Trial: N/A
Citation
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