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      How to improve representativeness and cost-effectiveness in samples recruited through meta: A comparison of advertisement tools

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          Abstract

          The use of paid advertisements on social media, in particular Meta platforms, to create samples for online survey research is becoming increasingly common. In addition to researchers working on hard-to-reach populations, Meta’s promise of unmediated, quick, and cheap access to a large pool of survey takers across the world is appealing also for researchers who want to create diverse samples of national populations for cheaper prices. Yet the design of Meta’s advertisement optimization algorithm complicates the use of Meta advertisements for this purpose, as it generates a trade-off between cost-effectiveness and sample representativeness. In this paper, we rely on original online surveys conducted in the United Kingdom, Turkey, Spain, and the Czech Republic to explore how two primary tools determining the audience of Meta advertisements, i.e., campaign objectives and demographic targeting, affect the recruitment process, response quality, and sample characteristics. In addition to documenting the trade-offs between the cost and representativeness in Meta samples, our paper also shows that researchers can create high-quality, cost-efficient, and diverse samples if they use the right combination of Meta advertisement tools.

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          Most cited references27

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          Recruiting large online samples in the United States and India: Facebook, Mechanical Turk, and Qualtrics

          This article examines online recruitment via Facebook, Mechanical Turk (MTurk), and Qualtrics panels in India and the United States. It compares over 7300 respondents—1000 or more from each source and country—to nationally representative benchmarks in terms of demographics, political attitudes and knowledge, cooperation, and experimental replication. In the United States, MTurk offers the cheapest and fastest recruitment, Qualtrics is most demographically and politically representative, and Facebook facilitates targeted sampling. The India samples look much less like the population, though Facebook offers broad geographical coverage. We find online convenience samples often provide valid inferences into how partisanship moderates treatment effects. Yet they are typically unrepresentative on such political variables, which has implications for the external validity of sample average treatment effects.
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            National Surveys Via Rdd Telephone Interviewing Versus the Internet: Comparing Sample Representativeness and Response Quality

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              Straightlining: Overview of Measurement, Comparison of Indicators, and Effects in Mail–Web Mixed-Mode Surveys

              Straightlining occurs when survey respondents give identical (or nearly identical) answers to items in a battery of questions using the same response scale, which may reduce data quality. Despite its potential importance, research examining straightlining does not use a standard measurement technique. Further, while mixed-mode studies are increasing in prevalence, few studies compare straightlining behavior in mail versus web surveys. Our article has the following goals: (1) describe and evaluate methods for detecting straightlining and (2) examine effects of education and mode of administration on straightlining in two mail/web mixed-mode surveys. Data for Study 1 are from a 2010 survey of alcohol beliefs and consumption in which 7,200 young adults were sampled from driver’s license records in Wisconsin and randomly assigned to mail–web (mail followed by web) or web–mail (web followed by mail) treatments. Data for Study 2 are from a 2013 survey about a public university and its affiliated health organizations that used an address-based sample and randomly assigned households to one of the three experimental groups: mail-only, web-only, and web–mail. We identify and examine five methods for measuring straightlining: simple nondifferentiation, mean root of pairs, maximum identical rating, standard deviation of battery, and scale point variation. The overall results replicate previously reported findings of a negative association between education and straightlining behaviors except for the standard deviation of battery measure in Study 1. Controlling for gender, race/ethnicity, and education, mode of administration was not significantly related to straightlining for any of the measures, suggesting straightlining behavior is stable across mail and web forms of self-administration.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: SupervisionRole: Writing – original draft
                Role: Data curationRole: MethodologyRole: VisualizationRole: Writing – original draft
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLOS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2023
                8 February 2023
                : 18
                : 2
                : e0281243
                Affiliations
                [001] School of Social and Political Sciences, University of Glasgow, Glasgow, United Kingdom
                University of Pittsburgh, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0002-1294-6771
                https://orcid.org/0000-0002-4877-4617
                Article
                PONE-D-21-40688
                10.1371/journal.pone.0281243
                9907806
                36753470
                05c40ef1-1c2d-4bc7-bf77-fc8a70c8a5a3
                © 2023 Neundorf, Öztürk

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 9 January 2022
                : 18 January 2023
                Page count
                Figures: 2, Tables: 6, Pages: 18
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100010663, H2020 European Research Council;
                Award ID: 865305
                Award Recipient :
                AN Grant number: 865305 European Research council The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Social Sciences
                Sociology
                Communications
                Marketing
                Advertising
                Research and Analysis Methods
                Research Design
                Survey Research
                Surveys
                People and Places
                Geographical Locations
                Asia
                Turkey (Country)
                People and Places
                Geographical Locations
                Europe
                Turkey (Country)
                People and places
                Geographical locations
                Europe
                European Union
                Spain
                Social Sciences
                Sociology
                Communications
                Social Communication
                Social Media
                Facebook
                Computer and Information Sciences
                Network Analysis
                Social Networks
                Social Media
                Facebook
                Social Sciences
                Sociology
                Social Networks
                Social Media
                Facebook
                People and places
                Geographical locations
                Europe
                European Union
                Czech Republic
                Social Sciences
                Sociology
                Communications
                Social Communication
                Social Media
                Computer and Information Sciences
                Network Analysis
                Social Networks
                Social Media
                Social Sciences
                Sociology
                Social Networks
                Social Media
                People and Places
                Population Groupings
                Educational Status
                Graduates
                Custom metadata
                Data relevant to this work are available from the Harvard Dataverse at DOI: 10.7910/DVN/YEOX5N ( https://doi.org/10.7910/DVN/YEOX5N).

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