Toloka platform as a source of online survey participants: an experience of assessing data quality

Author:

Gavrilov Kirill1ORCID

Affiliation:

1. HSE University; Institute of Sociology FCTAS RAS, Moscow, Russia

Abstract

The article presents the experience of using Yandex Toloka crowdsourcing platform to recruit respondents for an online survey. Analyzing methodological publications on a similar foreign platform Amazon Mechanical Turk we put forward hypotheses about the data quality obtained via Toloka in comparison with the results collected using other convenience sample types –online panels and recruitment of respondents through social networks. Additionally, only based on the Toloka data, we assessed the indicator of respondent’s attentiveness. The main conclusion is that Toloka allows to recruit respondents quickly and at low cost, and the results are comparable in terms of quality to those obtained by other methods. In particular, respondents from Toloka almost always complete the survey, fill out questionnaires faster than other types of respondents, but less often than participants of the online panel have a tendency to “straightline” (i.e., give the same answers in a tabular question), just as often as social media respondents give answers to the open-ended question (but less frequently than online panel participants), although their responses are shorter. Only 36% of the respondents passed the attention check question, attentive participants had a longer questionnaire complete time and were less likely to be straightliners. The increase of reward did not increase the proportion of attentive respondents, but decreased the questionnaire filling out speed, increased the number of answers to the open question, and reduced the proportion of straightliners.

Publisher

Federal Center of Theoretical and Applied Sociology of the Russian Academy of Sciences (FCTAS RAS)

Reference45 articles.

1. Couper M. Web Surveys: A review of issues and approaches, Public Opinion Quarterly, 2000, 64 (4), 464–494.

2. Report of the AAPOR task force on nonprobability sampling (transl., in Russian). Moscow: FOM publ., 2016. URL: https://fom.ru/uploads/ files/FOM_AAPOR_book1.pdf (date of access: 01.07.2022).

3. Mavletova A.M. Sociological surveys on the Internet: the possibilities of building a typology (in Russian), Sotsiologiya 4M (Sociology: methodology, methods, mathematical modeling), 2010, 31, 115–134.

4. Deviatko I. F. From “Virtual Lab” to “Social Telescope”: Metaphors of Theoretical and Methodological Innovations in Online Research, in: Online research in Russia: trends and prospects (in Russian). M.: MIK, 2016. P. 19–33.

5. Chmielewski M., Kucker S. An MTurk crisis? Shifts in data quality and the impact on study results, Social Psychological and Personality Science, 2020, 11 (4), 464–473. DOI: 10.1177/1948550619875149

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3