An Exploration of Stress: Leveraging Online Data from Crowdsourcing Platforms

James Roddy,Samantha Robinson

doi:10.3389/frai.2021.591529

Abstract

Background: Early detection of community health risk factors such as stress is of great interest to health policymakers, but representative data collection is often expensive and time-consuming. It is important to investigate the use of alternative means of data collection such as crowdsourcing platforms. Methods: An online sample of Amazon Mechanical Turk (MTurk) workers (N = 500) filled out, for themselves and their child, demographic information and the 10-item Perceived Stress Scale (PSS-10), designed to measure the degree to which situations in one’s life are appraised as stressful. Internal consistency reliability of the PSS-10 was examined via Cronbach’s alpha. Analysis of variance (ANOVA) was utilized to explore trends in the average perceived stress of both adults and their children. Last, Rasch trees were utilized to detect differential item functioning (DIF) in the set of PSS-10 items. Results: The PSS-10 showed adequate internal consistency reliability (Cronbach’s alpha = 0.73). ANOVA results suggested that stress scores significantly differed by education (p = 0.024), employment status (p = 0.0004), and social media usage (p = 0.015). Rasch trees, a recursive partitioning technique based on the Rasch model, indicated that items on the PSS-10 displayed DIF attributable to physical health for adults and social media usage for children. Conclusion: The key conclusion is that this data collection scheme shows promise, allowing public health officials to examine health risk factors such as perceived stress quickly and cost effectively.

Highlights

Despite the continued media coverage and public interest in those epidemics that dominate current health policy initiatives, stress is an often-overlooked health risk factor
Taking note of the proliferation and widespread use of crowdsourced online samples in consumer and social science research recruited from popular platforms such as Amazon Mechanical Turk (MTurk), this study proposes that leveraging of survey data from such platforms can provide policymakers with cross-sectional snapshots of health risk efficiently and at low cost
This finding is in line with previous research findings suggesting that MTurk workers’ demographic information is relatively comparable to the general population of survey respondents (Goodman and Paolacci, 2017)

Summary

Introduction

Despite the continued media coverage and public interest in those epidemics that dominate current health policy initiatives (e.g., opioid abuse, obesity, and mental illness), stress is an often-overlooked health risk factor. Detection of stress in adults and children, prior to the development of certain adverse health effects, could allow policymakers to shape the future health of their communities by providing early access to services and interventions. Detection of stress as a health risk factor is often difficult due to the cost prohibitive nature of data collection. Taking note of the proliferation and widespread use of crowdsourced online samples in consumer and social science research recruited from popular platforms such as Amazon Mechanical Turk (MTurk), this study proposes that leveraging of survey data from such platforms (when done carefully) can provide policymakers with cross-sectional snapshots of health risk efficiently and at low cost. Detection of community health risk factors such as stress is of great interest to health policymakers, but representative data collection is often expensive and time-consuming. It is important to investigate the use of alternative means of data collection such as crowdsourcing platforms

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Results

Conclusion