Abstract

Crowdsourcing has emerged as a preferred data collection methodology for advertising and social science researchers because these samples avoid the higher costs associated with professional panel data. Yet, there are ongoing concerns about data quality for online sources. This research examines differences in data quality for an advertising experiment across five popular online data sources, including professional panels and crowdsourced platforms. Effects of underlying mechanisms impacting data quality, including response satisficing, multitasking, and effort, are examined. As proposed, a serial mediation model shows that data source is, directly and indirectly, related to these antecedents of data quality. Satisficing is positively related to multitasking and negatively related to effort, and both mediators (in parallel) extend to data quality, indicating that the indirect effects on data quality through these mediating variables are significant. In general, a vetted MTurk sample (i.e., CloudResearch Approved) produces higher quality data than the other four sources. Regardless of the data source, researchers should utilize safeguards to ensure data quality. Safeguards and other strategies to obtain high-quality data from online samples are offered.

摘要

众包已成为广告和社会科学研究人员首选的数据收集方法，因为这些样本避免了与专业面板数据相关的更高成本。然而，人们一直担心在线资源的数据质量。这项研究检查了五个流行在线数据源（包括专业小组和众包平台）的广告实验的数据质量差异。研究了影响数据质量的潜在机制的影响，包括响应满意、多任务处理和努力。正如所建议的那样，串行中介模型表明数据源直接和间接地与这些数据质量的前因相关。满意度与多任务处理呈正相关，与努力程度呈负相关，并且两个中介（并行）延伸到数据质量，表明通过这些中介变量对数据质量的间接影响是显着的。一般来说，经过审查的 MTurk 样本（即 CloudResearch Approved）产生的数据质量高于其他四个来源。无论数据来源如何，研究人员都应利用安全措施来确保数据质量。提供了从在线样本中获取高质量数据的保障措施和其他策略。