Book contents
- Handbook of Research Methods in Social and Personality Psychology
- Cambridge Handbooks in Psychology
- Handbook of Research Methods in Social and Personality Psychology
- Copyright page
- Contents
- Figures
- Tables
- Contributors
- Introduction
- 1 The Romance of Research Methods
- Part I Before You Dive In
- Part II Basic Design Considerations to Know, No Matter What Your Research Is About
- Part III Deep Dives on Methods and Tools for Testing Your Question of Interest
- 10 Survey Research
- 11 Conducting Surveys and Experiments on the Internet
- 12 Methods for Studying Everyday Experience in Its Natural Context
- 13 Mobile Sensing Methods
- 14 Language Research in Social Personality Psychology
- 15 Collecting Digital Footprints in the Wild
- 16 Behavioral Observation and Coding
- 17 Automaticity and Implicit Measures
- 18 Social Neuroendocrinology
- 19 Multivariate Neuroimaging in Social and Personality Psychology
- Part IV Understanding What Your Data Are Telling You About Psychological Processes
- Index
- References
11 - Conducting Surveys and Experiments on the Internet
from Part III - Deep Dives on Methods and Tools for Testing Your Question of Interest
Published online by Cambridge University Press: 12 December 2024
Book contents
- Handbook of Research Methods in Social and Personality Psychology
- Cambridge Handbooks in Psychology
- Handbook of Research Methods in Social and Personality Psychology
- Copyright page
- Contents
- Figures
- Tables
- Contributors
- Introduction
- 1 The Romance of Research Methods
- Part I Before You Dive In
- Part II Basic Design Considerations to Know, No Matter What Your Research Is About
- Part III Deep Dives on Methods and Tools for Testing Your Question of Interest
- 10 Survey Research
- 11 Conducting Surveys and Experiments on the Internet
- 12 Methods for Studying Everyday Experience in Its Natural Context
- 13 Mobile Sensing Methods
- 14 Language Research in Social Personality Psychology
- 15 Collecting Digital Footprints in the Wild
- 16 Behavioral Observation and Coding
- 17 Automaticity and Implicit Measures
- 18 Social Neuroendocrinology
- 19 Multivariate Neuroimaging in Social and Personality Psychology
- Part IV Understanding What Your Data Are Telling You About Psychological Processes
- Index
- References
Summary
Social and personality psychologists have conducted surveys and experiments online for nearly twenty-five years. Researchers have used the Internet to ask questions about a wide range of topics, including racial bias, personality development, and attitude change. The frequency of conducting internet research has increased over time and understanding how to conduct online research has become a critical skill for psychologists. This chapter provides a general introduction to conducting survey and experimental research online. We outline how researchers can host and program internet studies, as well as their options for recruiting participant samples. We also cover important issues that researchers should consider about data quality, representativeness, generalizability, and upholding ethical standards. Throughout the chapter we discuss practices and guidelines that we view as optimal at the current time, and direct readers to additional literature that can further inform their thinking.
Keywords
- Type
- Chapter
- Information
- Publisher: Cambridge University PressPrint publication year: 2024
References
Anderson, C. A., Allen, J. J., Plante, C., Quigley-McBride, A., Lovett, A., and Rokkum, J. N. (2019). The MTurkification of social and personality psychology. Personality and Social Psychology Bulletin, 45(6), 842–850.CrossRefGoogle ScholarPubMed
Bem, D. J. (2011). Feeling the future: Experimental evidence for anomalous retroactive influences on cognition and affect. Journal of Personality and Social Psychology, 100(3), 407–425.CrossRefGoogle ScholarPubMed
Bialek, M., and Pennycook, G. (2018). The cognitive reflection test is robust to multiple exposures. Behavior Research Methods, 50(5), 1953–1959.CrossRefGoogle ScholarPubMed
Birnbaum, M. H. (2004). Human research and data collection via the Internet. Annual Review of Psychology, 55, 803–832.CrossRefGoogle ScholarPubMed
Bosnjak, M., Fiebach, C. J., Mellor, D., Mueller, S., O’Connor, D. B., Oswald, F. L., and Sokol- Chang, R. I. (2022). A template for preregistration of quantitative research in psychology: Report of the joint psychological societies preregistration task force. American Psychologist, 77, 602–615.CrossRefGoogle ScholarPubMed
Brock, R. L., Barry, R. A., Lawrence, E., Dey, J., and Rolffs, J. (2012). Internet administration of paper-and-pencil questionnaires used in couple research: Assessing psychometric equivalence. Assessment, 19(2), 226–242.CrossRefGoogle ScholarPubMed
Buhrmester, M. D., Kwang, T., and Gosling, S. D. (2011). Amazon’s Mechanical Turk: A new source of inexpensive, yet high-quality, data? Perspectives on Psychological Science, 6, 3–5.CrossRefGoogle Scholar
Chandler, J. J., and Paolacci, G. (2017). Lie for a dime: When most prescreening responses are honest but most study participants are impostors. Social Psychological and Personality Science, 8(5), 500–508.CrossRefGoogle Scholar
Chandler, J., Paolacci, G., Peer, E., Mueller, P., and Ratliff, K. A. (2015). Using nonnaive participants can reduce effect sizes. Psychological Science, 26(7), 1131–1139.CrossRefGoogle ScholarPubMed
Chandler, J., Rosenzweig, C., Moss, A. J., Robinson, J., and Litman, L. (2019). Online panels in social science research: Expanding sampling methods beyond Mechanical Turk. Behavior Research Methods, 51(5), 2022–2038.CrossRefGoogle ScholarPubMed
Clifford, S., Jewell, R. M., and Waggoner, P. D. (2015). Are samples drawn from Mechanical Turk valid for research on political ideology? Research & Politics, 2(4), 2053168015622072, https://doi.org/10.1177%2F2053168015622072.CrossRefGoogle Scholar
Coppock, A. (2019). Generalizing from survey experiments conducted on Mechanical Turk: A replication approach. Political Science Research and Methods, 7(3), 613–628.CrossRefGoogle Scholar
Coppock, A., Leeper, T. J., and Mullinix, K. J. (2018). Generalizability of heterogeneous treatment effect estimates across samples. Proceedings of the National Academy of Sciences, 115(49), 12441–12446.CrossRefGoogle ScholarPubMed
Crump, M. J., McDonnell, J. V., and Gureckis, T. M. (2013). Evaluating Amazon’s Mechanical Turk as a tool for experimental behavioral research. PLOS ONE, 8(3), e57410, https://doi.org/10.1371/journal.pone.0057410.CrossRefGoogle Scholar
Curran, P. G. (2016). Methods for the detection of carelessly invalid responses in survey data. Journal of Experimental Social Psychology, 66, 4–19.CrossRefGoogle Scholar
Fraley, R. C. (2004). How to Conduct Behavioral Research over the Internet: A Beginner’s Guide to HTML and CGI/Perl. Guilford Press.Google Scholar
Gosling, S. D., Vazire, S., Srivastava, S., and John, O. P. (2004). Should we trust web-based studies? A comparative analysis of six preconceptions about internet questionnaires. American Psychologist, 59(2), 93–104.CrossRefGoogle ScholarPubMed
Hauser, D. J., and Schwarz, N. (2015). It’s a trap! Instructional manipulation checks prompt systematic thinking on “tricky” tasks. Sage Open, 5(2), 2158244015584617, https://doi.org/10.1177%2F2158244015584617.CrossRefGoogle Scholar
Hauser, D. J., and Schwarz, N. (2016). Attentive Turkers: MTurk participants perform better on online attention checks than do subject pool participants. Behavior Research Methods, 48(1), 400–407.CrossRefGoogle ScholarPubMed
Hawkins, R. X. (2015). Conducting real-time multiplayer experiments on the web. Behavior Research Methods, 47(4), 966–976.CrossRefGoogle ScholarPubMed
Henrich, J., Heine, S. J., and Norenzayan, A. (2010). The weirdest people in the world? Behavioral and Brain Sciences, 33(2–3), 61–83.CrossRefGoogle ScholarPubMed
Ilagan, M. J., and Falk, C. F. (in press). Supervised classes, unsupervised mixing proportions: Detection of bots in a Likert-type questionnaire. Educational and Psychological Measurement, https://doi.org/10.1177/00131644221104220.CrossRefGoogle Scholar
Kees, J., Berry, C., Burton, S., and Sheehan, K. (2017). An analysis of data quality: Professional panels, student subject pools, and Amazon’s Mechanical Turk. Journal of Advertising, 46(1), 141–155.CrossRefGoogle Scholar
Krantz, J. H., and Dalal, R. (2000). Validity of Web-based psychological research. In Birnbaum, M. H. (ed.) Psychological Experiments on the Internet. Academic Press.Google Scholar
Kraut, R., Olson, J., Banaji, M., Bruckman, A., Cohen, J., and Couper, M. (2004). Psychological research online: Report of Board of Scientific Affairs’ Advisory Group on the Conduct of Research on the Internet. American Psychologist, 59(2), 105–117.CrossRefGoogle Scholar
Litman, L., and Robinson, J. (2021). Conducting Online Research on Amazon Mechanical Turk and Beyond. Sage Academic Publishing.CrossRefGoogle Scholar
Mason, W., and Suri, S. (2012). Conducting behavioral research on Amazon’s Mechanical Turk. Behavior Research Methods, 44(1), 1–23.CrossRefGoogle ScholarPubMed
Moss, A., and Litman, L. (2018). After the Bot Scare: Understanding What’s Been Happening with Data Collection on MTurk and How to Stop It, www.cloudresearch.com/resources/blog/after-the-bot-scare-understanding-whats-been-happening-with-data-collection-on-mturk-and-how-to-stop-it.Google Scholar
Necka, E. A., Cacioppo, S., Norman, G. J., and Cacioppo, J. T. (2016). Measuring the prevalence of problematic respondent behaviors among MTurk, campus, and community participants. PLOS ONE, 11(6), e0157732, https://doi.org/10.1371/journal.pone.0157732.CrossRefGoogle ScholarPubMed
Nosek, B. A., Banaji, M. R., and Greenwald, A. G. (2002). E‐research: Ethics, security, design, and control in psychological research on the Internet. Journal of Social Issues, 58(1), 161–176.CrossRefGoogle Scholar
Nosek, B. A., Sriram, N., and Umansky, E. (2012). Presenting survey items one at a time compared to all at once decreases missing data without sacrificing validity in research with internet volunteers. PLOS ONE, 7(5), e36771, https://doi.org/10.1371/journal.pone.0036771.CrossRefGoogle Scholar
Oppenheimer, D. M., Meyvis, T., and Davidenko, N. (2009). Instructional manipulation checks: Detecting satisficing to increase statistical power. Journal of Experimental Social Psychology, 45(4), 867–872.CrossRefGoogle Scholar
Palan, S., and Schitter, C. (2018). Prolific.ac: A subject pool for online experiments. Journal of Behavioral and Experimental Finance, 17, 22–27.CrossRefGoogle Scholar
Center, Pew Research (2021). Internet/Broadband Fact Sheet, www.pewresearch.org/internet/fact-sheet/internet-broadband/?menuItem=6ba9316e-006c-482d-be4b-69feb64c4be8.Google Scholar
Ping, H., Stoyanovich, J., and Howe, B. (2017). Datasynthesizer: Privacy-preserving synthetic datasets (June 2017). In Proceedings of the 29th International Conference on Scientific and Statistical Database Management, Association for Computing Machinery, New York.Google Scholar
Rhodes, M., Rizzo, M. T., Foster-Hanson, E., Moty, K., Leshin, R. A., Wang, M., … Ocampo, J. D. (2020). Advancing developmental science via unmoderated remote research with children. Journal of Cognition and Development, 21(4), 477–493.CrossRefGoogle ScholarPubMed
Sears, D. O. (1986). College sophomores in the laboratory: Influences of a narrow data base on social psychology’s view of human nature. Journal of Personality and Social Psychology, 51(3), 515–530.CrossRefGoogle Scholar
Simmons, J. P., Nelson, L. D., and Simonsohn, U. (2011). False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological Science, 22(11), 1359–1366.CrossRefGoogle ScholarPubMed
Skitka, L. J., and Sargis, E. G. (2006). The Internet as psychological laboratory. Annual Review of Psychology, 57, 529–555.CrossRefGoogle ScholarPubMed
Srivastava, S., John, O. P., Gosling, S. D., and Potter, J. (2003). Development of personality in early and middle adulthood: Set like plaster or persistent change? Journal of Personality and Social Psychology, 84, 1041–1053.CrossRefGoogle ScholarPubMed
Summerville, A., and Chartier, C. R. (2013). Pseudo-dyadic “interaction” on Amazon’s Mechanical Turk. Behavior Research Methods, 45(1), 116–124.CrossRefGoogle ScholarPubMed
Thai, S., and Page-Gould, E. (2018). ExperienceSampler: An open-source scaffold for building smartphone apps for experience sampling. Psychological Methods, 23(4), 729–739.CrossRefGoogle ScholarPubMed
Tissera, H., Mignault, M. C., and Human, L. J. (2023). “Zooming” in on positive and accurate metaperceptions in first impressions: Examining the links with social anxiety and liking in online video interactions. Journal of Personality and Social Psychology, 125(4), 852–873.CrossRefGoogle Scholar
Zhou, H., and Fishbach, A. (2016). The pitfall of experimenting on the web: How unattended selective attrition leads to surprising (yet false) research conclusions. Journal of Personality and Social Psychology, 111(4), 493–504.CrossRefGoogle ScholarPubMed