Crowdsourced methods of data collection such as Amazon Mechanical Turk (MTurk) have been widely adopted in addiction science. Recent reports suggest an increase in poor quality data on MTurk, posing a challenge to the validity of findings. However, empirical investigations of data quality in addiction-related samples are lacking. In this study of individuals with alcohol use disorder (AUD), we compared poor quality delay discounting data to randomly generated data. A reanalysis of prior published delay discounting data was conducted comparing included, excluded, and randomly generated data samples. Nonsystematic criteria were implemented as a measure of data quality. The excluded data was statistically different from the included sample but did not differ from randomly generated data on multiple metrics. Moreover, a response bias was identified in the excluded data. This study provides empirical evidence that poor quality delay discounting data in an AUD sample is not statistically different from randomly generated data, suggesting data quality concerns on MTurk persist in addiction samples. These findings support the use of rigorous methods of a priori defined criteria to remove poor quality data post hoc. Additionally, it highlights that the use of nonsystematic delay discounting criteria to remove poor quality data is rigorous and not simply a way of removing data that does not conform to an expected theoretical model. Public Health SignificanceThis study provides empirical evidence that poor quality delay discounting data does not differ from random responding in a sample of individuals with alcohol use disorder. This highlights that previous reports of poor quality data on Amazon Mechanical Turk extend to addiction-related samples. Thus, the use of rigorous data quality controls and exclusion of poor quality data are warranted to ensure highquality scientific findings.

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Objectives Banning vaping products may have unintended outcomes, such as increased demand for illegal products. This study experimentally examined the effects of a vaping ban and a flavored vaping ban on the probability of purchasing illicit vaping products, and factors affecting purchasing from a hypothetical illegal marketplace. Methods A crowdsourced sample of exclusive cigarette smokers, exclusive e-cigarette users, and frequent dual users (n=150) completed hypothetical purchasing trials in an Experimental Tobacco Marketplace under three conditions (no ban, vaping ban, flavored vaping ban). Participants chose to purchase in a hypothetical legal experimental tobacco marketplace (LETM) or illegal experimental tobacco marketplace (IETM). Vaping products were available in each marketplace depending on the condition. Other tobacco products were always available in the LETM. A hypothetical illicit purchase task with five fine amounts assessed the effect of monetary penalties. Results Participants from all groups were more likely to purchase from the IETM when product availability in the LETM was more restricted, with e-cigarette users being most affected. The likelihood of purchasing illegal products was systematically decreased as monetary penalties associated with the IETM increased, with e-cigarette users showing greater persistence in defending their illicit purchases. Conclusions Restricting vaping products from the marketplace may shift preference towards purchasing vaping products in the illegal marketplace. Nevertheless, penalties imposed on consumer's behavior might be effective in preventing illicit trade. The IETM is a methodological extension that supports the utility and flexibility of the ETM as a framework for understanding the impact of different tobacco regulatory policies.