{"title":"EXPRESS: Response Satisficing across Online Data Sources: Effects of Satisficing on Data Quality and Policy-Relevant Results","authors":"Christopher Berry, Scot Burton","doi":"10.1177/07439156241268707","DOIUrl":null,"url":null,"abstract":"The use of crowdsourced data has become extremely popular in marketing and public policy research. However, there are concerns about the validity of studies that source data from crowdsourcing platforms such as Amazon’s Mechanical Turk (MTurk). Using five different online sample sources, including multiple MTurk samples and professionally managed panels, we address issues related to online data quality and its effects on results for a policy-based 2 x 2 between subjects’ experiment. We show that survey response satisficing, as well as multitasking, is related to attention check performance measures beyond demographic differences, and there are substantial differences across the five different online data sources. We specifically identify segments of high and low response satisficers using a multiitem measure and show that there are critical differences in the policy-relevant results for the experiment for these segments of online respondents. Findings suggest implications for concerns about failures to replicate results in the policy and consumer well-being, business, and social science literatures. We offer some suggestions for attempting to reduce problematic effects of response satisficing and data quality that are shown to differ substantially across the sample sources examined.","PeriodicalId":51437,"journal":{"name":"Journal of Public Policy & Marketing","volume":null,"pages":null},"PeriodicalIF":5.1000,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Public Policy & Marketing","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1177/07439156241268707","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS","Score":null,"Total":0}
引用次数: 0
Abstract
The use of crowdsourced data has become extremely popular in marketing and public policy research. However, there are concerns about the validity of studies that source data from crowdsourcing platforms such as Amazon’s Mechanical Turk (MTurk). Using five different online sample sources, including multiple MTurk samples and professionally managed panels, we address issues related to online data quality and its effects on results for a policy-based 2 x 2 between subjects’ experiment. We show that survey response satisficing, as well as multitasking, is related to attention check performance measures beyond demographic differences, and there are substantial differences across the five different online data sources. We specifically identify segments of high and low response satisficers using a multiitem measure and show that there are critical differences in the policy-relevant results for the experiment for these segments of online respondents. Findings suggest implications for concerns about failures to replicate results in the policy and consumer well-being, business, and social science literatures. We offer some suggestions for attempting to reduce problematic effects of response satisficing and data quality that are shown to differ substantially across the sample sources examined.
期刊介绍:
Journal of Public Policy & Marketing welcomes manuscripts from diverse disciplines to offer a range of perspectives. We encourage submissions from individuals with varied backgrounds, such as marketing, communications, economics, consumer affairs, law, public policy, sociology, psychology, anthropology, or philosophy. The journal prioritizes well-documented, well-reasoned, balanced, and relevant manuscripts, regardless of the author's field of expertise.