Book contents
- The Cambridge Handbook of Consumer Psychology
- Cambridge Handbooks in Psychology
- The Cambridge Handbook of Consumer Psychology
- Copyright page
- Contents
- Figures
- Tables
- Contributors
- Introduction
- 1 Consumer Psychology of Individuals
- 2 Consumer Psychology of Groups and Society
- 3 Methods for Understanding Consumer Psychology
- Index
- References
3 - Methods for Understanding Consumer Psychology
Published online by Cambridge University Press: 30 March 2023
Book contents
- The Cambridge Handbook of Consumer Psychology
- Cambridge Handbooks in Psychology
- The Cambridge Handbook of Consumer Psychology
- Copyright page
- Contents
- Figures
- Tables
- Contributors
- Introduction
- 1 Consumer Psychology of Individuals
- 2 Consumer Psychology of Groups and Society
- 3 Methods for Understanding Consumer Psychology
- Index
- References
Summary
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- Chapter
- Information
- The Cambridge Handbook of Consumer Psychology , pp. 529 - 640Publisher: Cambridge University PressPrint publication year: 2023
References
References
Arens, Z. G., & Hamilton, R. W. (2016). Why focusing on the similarity of substitutes leaves a lot to be desired. Journal of Consumer Research, 43(3), 448–459.Google Scholar
Berinsky, A. J., Huber, G. A., & Lenz, G. S. (2012). Evaluating online labor markets for experimental research: Amazon. com’s Mechanical Turk. Political Analysis, 20(3), 351–368.CrossRefGoogle Scholar
Bolton, L. E., Reed, A., Volpp, K. G., & Armstrong, K. (2008). How does drug and supplement marketing affect a healthy lifestyle? Journal of Consumer Research, 34(5), 713–726.Google Scholar
Braun, M., & Schwartz, E. M. (2022). The A/B Test Deception: Divergent Delivery, Response Heterogeneity, and Erroneous Inferences in Online Advertising Field Experiments. SMU Cox School of Business Research Paper (21-10).Google Scholar
Brodeur, A., Lé, M., Sangnier, M., & Zylberberg, Y. (2016). Star wars: The empirics strike back. American Economic Journal: Applied Economics, 8(1), 1–32.Google Scholar
Chater, N., & Loewenstein, G. (2022). The i-Frame and the s-Frame: How Focusing on the Individual-Level Solutions Has Led Behavioral Public Policy Astray. Behavioral and Brain Sciences, in press. www.cambridge.org/core/services/aop-cambridge-core/content/view/A799C9C57F388A712BE5A8D34D5229A1/S0140525X22002023a.pdf/iframe_and_the_sframe_how_focusing_on_individuallevel_solutions_has_led_behavioral_public_policy_astray.pdfGoogle Scholar
Cialdini, R. B. (2009). We have to break up. Perspectives on Psychological Science, 4(1), 5–6.Google Scholar
DellaVigna, S., & Linos, E. (2022). RCTs to scale: Comprehensive evidence from two nudge units. Econometrica, 90(1), 81–116.Google Scholar
Eckles, D., Gordon, B. R., & Johnson, G. A. (2018). Field studies of psychologically targeted ads face threats to internal validity. Proceedings of the National Academy of Sciences, 115(23), E5254–E5255.CrossRefGoogle ScholarPubMed
Ferber, R. (1977). Research by convenience. Journal of Consumer Research, 4(1), 57–58.Google Scholar
Fernbach, P. M., Kan, C., & LynchJr., J. G. (2015). Squeezed: Coping with constraint through efficiency and prioritization. Journal of Consumer Research, 41(5), 1204–1227.Google Scholar
Garcia-Rada, X., Steffel, M., Williams, E. F., & Norton, M. I. (2022). Consumers value effort over ease when caring for close others. Journal of Consumer Research, 48(6), 970–990.Google Scholar
Gneezy, A. (2017). Field experimentation in marketing research. Journal of Marketing Research, 54(1), 140–143.Google Scholar
Harrison, G. W., & List, J. A. (2004). Field experiments. Journal of Economic Literature, 42(4), 1009–1055.Google Scholar
Jung, M. H., Perfecto, H., & Nelson, L. D. (2016). Anchoring in payment: Evaluating a judgmental heuristic in field experimental settings. Journal of Marketing Research, 53(3), 354–368.Google Scholar
Lai, C. K., Marini, M., Lehr, S. A., et al. (2014). Reducing implicit racial preferences: I. A comparative investigation of 17 interventions. Journal of Experimental Psychology: General, 143(4), 1765.Google Scholar
Lai, C. K., Skinner, A. L., Cooley, E., et al. (2016). Reducing implicit racial preferences: II. Intervention effectiveness across time. Journal of Experimental Psychology: General, 145(8), 1001.CrossRefGoogle ScholarPubMed
Levitt, S. D., & List, J. A. (2007). On the generalizability of lab behaviour to the field. Canadian Journal of Economics/Revue canadienne d’économique, 40(2), 347–370.Google Scholar
Levitt, S. D., & List, J. A. (2009). Field experiments in economics: The past, the present, and the future. European Economic Review, 53(1), 1–18.Google Scholar
List, J. A. (2011). The market for charitable giving. Journal of Economic Perspectives, 25(2), 157–180.Google Scholar
Lynch Jr., J. G. (1982). On the external validity of experiments in consumer research. Journal of Consumer Research, 9(3), 225–239.Google Scholar
Lynch, J. G. (1999). Theory and external validity. Journal of the Academy of Marketing Science, 27(3), 367–376.CrossRefGoogle Scholar
Mick, D. G. (2003). Appreciation, advice, and some aspirations for consumer research. Journal of Consumer Research, 29(4), 455–462.Google Scholar
Miguel, E., Camerer, C., Casey, K., et al. (2014). Promoting transparency in social science research. Science, 343(6166), 30–31.Google Scholar
Milkman, K. L., Gromet, D., Ho, H., et al. (2021). Megastudies improve the impact of applied behavioural science. Nature, 600(7889), 478–483.Google Scholar
Milkman, K. L., Patel, M. S., Gandhi, L., et al. (2021). A megastudy of text-based nudges encouraging patients to get vaccinated at an upcoming doctor’s appointment. Proceedings of the National Academy of Sciences, 118(20) e2101165118.CrossRefGoogle ScholarPubMed
Nelson, L., Simester, D., & Sudhir, K. (2020). Introduction to the special issue on marketing science and field experiments. Marketing Science, 39(6), 1033–1038.Google Scholar
Nelson, L. D., Simmons, J., & Simonsohn, U. (2018). Psychology’s renaissance. Annual Review of Psychology, 69, 511–534.Google Scholar
Oostrom, T. (2021). Funding of clinical trials and reported drug efficacy. Unpublished Manuscript.Google Scholar
Packard, G., & Berger, J. (2021). How concrete language shapes customer satisfaction. Journal of Consumer Research, 47(5), 787–806.Google Scholar
Paolacci, G., Chandler, J., & Ipeirotis, P. G. (2010). Running experiments on Amazon Mechanical Turk. Judgment and Decision Making, 5(5), 411–419.Google Scholar
Schäfer, T., & Schwarz, M. A. (2019). The meaningfulness of effect sizes in psychological research: Differences between sub-disciplines and the impact of potential biases. Frontiers in Psychology, 10, 813.Google Scholar
Scott, C. A. (1977). Modifying socially-conscious behavior: The foot-in-the-door technique. Journal of Consumer Research, 4(3), 156–164.Google Scholar
Simester, D. (2017). Field experiments in marketing. In Handbook of Economic Field Experiments, Vol. 1 (pp. 465–497). North-Holland.Google Scholar
Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011). False-positive psychology: Undisclosed flexibility in data collection and analysis allow presenting anything as significant. Psychological Science, 22, 1359–1366.Google Scholar
Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2021). Pre‐registration is a game changer. But, like random assignment, it is neither necessary nor sufficient for credible science. Journal of Consumer Psychology, 31(1), 177–180.CrossRefGoogle Scholar
Simonsohn, U., Nelson, L. D., & Simmons, J. P. (2014). p-curve and effect size: Correcting for publication bias using only significant results. Perspectives on Psychological Science, 9(6), 666–681.Google Scholar
Simonsohn, U., Simmons, J. P., & Nelson, L. D. (2020). Specification curve analysis. Nature Human Behaviour, 4(11), 1208–1214.CrossRefGoogle ScholarPubMed
Sudhir, K. (2016). The exploration-exploitation tradeoff and efficiency in knowledge production. Marketing Science, 35(1), 1–9.Google Scholar
Walters, D. J., & Hershfield, H. E. (2020). Consumers make different inferences and choices when product uncertainty is attributed to forgetting rather than ignorance. Journal of Consumer Research, 47(1), 56–78.Google Scholar
Webb, P. H. (1979). Consumer initial processing in a difficult media environment. Journal of Consumer Research, 6(3), 225–236.Google Scholar
References
Aguinis, H., Villamor, I., & Ramani, R. S. (2021). MTurk research: Review and recommendations. Journal of Management, 47(4), 823–837.Google Scholar
Arechar, A. A., & Rand, D. G. (2021). Turking in the time of COVID. Behavior Research Methods, 53(6), 2591–2595.Google Scholar
Arndt, A. D., Ford, J. B., Babin, B. J., & Luong, V. (2022). Collecting samples from online services: How to use screeners to improve data quality. International Journal of Research in Marketing, 39(1), 117–133.Google Scholar
Buhrmester, M. D., Talaifar, S., & Gosling, S. D. (2018). An evaluation of Amazon’s Mechanical Turk, its rapid rise, and its effective use. Current Directions in Psychological Science, 13(2), 149–154.Google Scholar
Chandler, J., Mueller, P., & Paolacci, G. (2014). Nonnaïveté among Amazon Mechanical Turk workers: Consequences and solutions for behavioral researchers. Behavior Research Methods, 46(1), 112–130.Google Scholar
Chandler, J., Paolacci, G., Peer, E., Mueller, P., & Ratliff, K. A. (2015). Using nonnaive participants can reduce effect sizes. Psychological Science, 26(7), 1131–1139.Google Scholar
Chandler, J., Rosenzweig, C., Moss, A. J., Robinson, J., & Litman, L. (2019). Online panels in social science research: Expanding sampling methods beyond Mechanical Turk. Behavior Research Methods, 51(5), 2022–2038.Google Scholar
Chandler, J., & Shapiro, D. (2016). Conducting clinical research using crowdsourced convenience samples. Annual Review of Clinical Psychology, 12.Google Scholar
Charalambides, N. (2021, August 24). We recently went viral on TikTok - here’s what we learned. Prolific Blog, www.blog.prolific.co/we-recently-went-viral-on-tiktok-heres-what-we-learnedGoogle Scholar
Chmielewski, M., & Kucker, S. C. (2020). An MTurk crisis? Shifts in data quality and the impact on study results. Social Psychological and Personality Science, 11(4), 464–473.Google Scholar
Couper, M. (2008). Designing Effective Web Surveys, Vol. 75. Cambridge University Press.Google Scholar
Dennis, S. A., Goodson, B. M., & Pearson, C. A. (2020). Online worker fraud and evolving threats to the integrity of MTurk data: A discussion of virtual private servers and the limitations of IP-based screening procedures. Behavioral Research in Accounting, 32(1), 119–134.Google Scholar
Dholakia, U. (2015, July 20). My Experience as an Amazon Mechanical Turk (MTurk) Worker. www.linkedin.com/pulse/my-experience-amazon-mechanical-turkmturk-worker-utpal-dholakiaGoogle Scholar
Dreyfuss, E. (2018, August 17). A bot panic hits Amazon’s Mechanical Turk. Wired, www.wired.com/story/amazon-mechanical-turk-bot-panicGoogle Scholar
Eyal, P., David, R., Andrew, G., Zak, E., & Ekaterina, D. (2022). Data quality of platforms and panels for online behavioral research. Behavior Research Methods, 54, 1643–1662.Google Scholar
Freitas, A. L., Gollwitzer, P., & Trope, Y. (2004). The influence of abstract and concrete mindsets on anticipating and guiding others’ self-regulatory efforts. Journal of Experimental Social Psychology, 40(6), 739–752.Google Scholar
Garcia, T. (2021, June 15). Consumers find shortages and higher prices as COVID-impacted supply chains shift for recovery. MarketWatch. www.marketwatch.com/story/consumers-find-shortages-and-higher-prices-as-covid-impacted-supply-chains-shift-for-recovery-11623425796Google Scholar
Goodman, J. K., Cryder, C. E., Cheema, A. A. (2013). Data collection in a flat world: Strengths and weaknesses of Mechanical Turk samples. Journal of Behavioral Decision Making, 26 (July), 213–224.Google Scholar
Goodman, J. K., & Paolacci, G. (2017). Crowdsourcing consumer research. Journal of Consumer Research, 44(1), 196–210.Google Scholar
Gosling, S. D., & Mason, W. (2015). Internet research in psychology. Annual Review of Psychology, 66, 877–902.Google Scholar
Hauser, D., Moss, A. J., Rosenzweig, C., Jaffe, S. N., Robinson, J., & Litman, L. (2021). Evaluating CloudResearch’s Approved Group as a solution for problematic data quality on MTurk. https://psyarxiv.com/48yxj/Google Scholar
Hauser, D. J., Paolacci, G., & Chandler, J. (2019). Common concerns with MTurk as a participant pool: Evidence and solutions. In Kardes, F. R., Herr, P. M., & Schwarz, N. (Eds.). Handbook of Research Methods in Consumer Psychology (pp. 319–337). Routledge.Google Scholar
Hauser, D. J., & 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.Google Scholar
Hernandez, J. M. da, C., Wright, S. A., & Rodrigues, F. F. (2015). Attributes versus benefits: The role of construal levels and appeal type on the persuasiveness of marketing messages. Journal of Advertising, 44(3), 243–253.Google Scholar
Jacowitz, K. E., & Kahneman, D. (1995). Measures of anchoring in estimation tasks. Personality and Social Psychology Bulletin, 21(11), 1161–1166.Google Scholar
Jacquemet, N., James, A. G., Luchini, S., Murphy, J. J., & Shogren, J. F. (2021). Do truth-telling oaths improve honesty in crowd-working? PLoS ONE, 16(1), e0244958.Google Scholar
Janiszewski, C., & van Osselaer, S. M. J. (2021). The benefits of candidly reporting consumer research. Journal of Consumer Psychology, 31(4), 633–646.Google Scholar
Kees, J., Berry, C., Burton, S., & 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.Google Scholar
Kennedy, R., Clifford, S., Burleigh, T., Waggoner, P. D., Jewell, R., & Winter, N. J. (2020). The shape of and solutions to the MTurk quality crisis. Political Science Research and Methods, 8(4), 614–629.Google Scholar
Kochhar, R. (2020, June 11). Unemployment rose higher in three months of COVID-19 than it did in two years of the Great Recession. Pew Research Center. www.pewresearch.org/fact-tank/2020/06/11/unemployment-rose-higher-in-three-months-of-covid-19-than-it-did-in-two-years-of-the-great-recessionGoogle Scholar
Litman, L., Moss, A., Rosenzweig, C., & Robinson, J. (2021, January 28). Reply to MTurk, Prolific or panels? Choosing the right audience for online research. Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3775075Google Scholar
Litman, L., Robinson, J., & Rosenzweig, C. (2015). The relationship between motivation, monetary compensation, and data quality among US- and India-based workers on Mechanical Turk. Behavioral Research Methods, 47, 519–528.Google Scholar
Loepp, E., & Kelly, J. T. (2020). Distinction without a difference? An assessment of MTurk Worker types. Research & Politics, 7(1), p. 1–8.Google Scholar
Lourenco, S. F., & Tasimi, A. (2020). No participant left behind: Conducting science during COVID-19. Trends in Cognitive Sciences, 24(8), 583–584.Google Scholar
Lu, L., Neale, N., Line, N. D., & Bonn, M. (2021). Improving data quality using Amazon Mechanical Turk through platform setup. Cornell Hospitality Quarterly, 19389655211025475.Google Scholar
Martin, D., Hanrahan, B. V., O’Neill, J., & Gupta, N. (2014). Being a Turker. In Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work and Social Computing, Association for Computing Machinery, 224–235.Google Scholar
Mason, W., & Suri, S. (2012). Conducting behavioral research on Amazon’s Mechanical Turk. Behavior Research Methods, 44, 1–23.Google Scholar
Moss, A., & Litman, L. (2018). After the bot scare: Understanding what’s been happening with data collection on MTurk and how to stop it. Retrieved February 4, 2019. www.cloudresearch.com/resources/blog/after-the-bot-scare-understanding-whats-been-happening-with-data-collection-on-mturk-and-how-to-stop-itGoogle Scholar
Moss, A. J., Rosenzweig, C., Jaffe, S. N., Gautam, R., Robinson, J., & Litman, L. (2021). Bots or inattentive humans? Identifying sources of low-quality data in online platforms. https://doi.org/10.31234/osf.io/wr8dsGoogle Scholar
Moss, A. J., Rosenzweig, C., Robinson, J., & Litman, L. (2020). Demographic stability on Mechanical Turk despite COVID-19. Trends in Cognitive Sciences, 24(9), 678–680.Google Scholar
Mummolo, J., & Peterson, E. (2019). Demand effects in survey experiments: An empirical assessment. American Political Science Review, 113, 517–529.Google Scholar
Mutikani, L. (2021, September 8). U.S. job openings hit record high as employers struggle to find workers. Reuters, www.reuters.com/business/us-job-openings-rise-record-109-million-july-2021-09-08Google Scholar
Palan, S., & Schitter, C. (2018). Prolific.ac – A subject pool for online experiments. Journal of Behavioral and Experimental Finance, 17, 22–27.Google Scholar
Paolacci, G., & Chandler, J. (2014). Inside the Turk: Understanding Mechanical Turk as a participant pool. Current Directions in Psychological Science, 23, 184–188.Google Scholar
Peer, E., Brandimarte, L., Samat, S., & Acquisti, A. (2017). Beyond the Turk: Alternative platforms for crowdsourcing behavioral research. Journal of Experimental Social Psychology, 70, 153–163.Google Scholar
Peer, E., Vosgerau, J., & Acquisti, A. (2014). Reputation as a sufficient condition for data quality on Amazon Mechanical Turk. Behavior Research Methods, 46(4), 1023–1031.Google Scholar
Peyton, K., Huber, G. A., & Coppock, A. (2021). The generalizability of online experiments conducted during the COVID-19 pandemic. Journal of Experimental Political Science, 1–16.Google Scholar
Ramos, S. (2021, March 1). COVID-19’s impact felt by researchers. American Psychological Association, www.apa.org/science/leadership/students/covid-19-impact-researchersGoogle Scholar
Robinson, J., Rosenzweig, C., Moss, A. J., & Litman, L. (2019). Tapped out or barely tapped? Recommendations for how to harness the vast and largely unused potential of the Mechanical Turk participant pool. PLoS ONE, https://doi.org/10.1371/journal.pone.0226394CrossRefGoogle Scholar
Sandford, A. (2020, April 2). Coronavirus: Half of humanity on lockdown in 90 countries. Euronews. Archived from the original on May 19, 2020. Retrieved June 15, 2021. www.euronews.com/2020/04/02/coronavirus-in-europe-spain-s-death-toll-hits-10-000-after-record-950-new-deaths-in-24-hou#vuukle-comments-1065562Google Scholar
Shapiro, D. N., Chandler, J., & Mueller, P. (2013). Using Mechanical Turk to study clinical populations. Clinical Psychological Science, 1, 213–220.Google Scholar
Sheth, J. (2020). Impact of Covid-19 on consumer behavior: Will the old habits return or die? Journal of Business Research, 117, 280–283.Google Scholar
Simon, R. (2021, April 16). Covid-19’s toll on U.S. business? 200,000 Extra closures in pandemic’s first year. The Wall Street Journal, www.wsj.com/livecoverage/covid-2021-04-16/card/QKG4xgwU4HyCqMq6TiyIGoogle Scholar
Simmons, J., Nelson, L. D., & Simonsohn, U. (2021). Pre‐registration: Why and how. Journal of Consumer Psychology, 31(1), 151–162.Google Scholar
Simonsohn, U., Nelson, L. D., & Simmons, J. P. (2014). P-curve: A key to the file-drawer. Journal of Experimental Psychology: General, 143(2), 534–547.Google Scholar
Stokel-Walker, C. (2018, August 10). Bots on Amazon’s Mechanical Turk are ruining psychology studies. New Scientist, www.newscientist.com/article/2176436-bots-on-amazons-mechanical-turk-are-ruining-psychology-studiesGoogle Scholar
Su, I., & Ceci, S. (2021). “Zoom developmentalists”: Home-based videoconferencing developmental research during COVID-19. https://doi.org/10.31234/osf.io/nvdy6Google Scholar
Wessling, K. S., Huber, J., Netzer, O. (2017). MTurk character misrepresentation: Assessment and solutions. Journal of Consumer Research, 44, 211–230.Google Scholar
Wright, S., Manolis, C., Brown, D., et al. (2012). Construal-level mind-sets and the perceived validity of marketing claims. Marketing Letters, 23(1), 253–261.Google Scholar
Wright, S. A., & Goodman, J. K. (2019). Mechanical Turk in consumer research: Perceptions and usage in marketing academia. In Kardes, F. R., Herr, P. M., & Schwarz, N. (Eds.). Handbook of Research Methods in Consumer Psychology (pp. 338–357). Routledge.Google Scholar
Yue, C., Gilbert, B., Zhu, Q., Kleeman, H., & Rea, S. C. (2020). Crowdsourcing as a tool for research: Methodological, fair, and political considerations. Bulletin of Science, Technology & Society, 40(3–4), 40–53.Google Scholar
Zhou, H., & 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.Google Scholar
References
Airy, G. B. (1861). On the Algebraical and Numerical Theory of Errors of Observations and the Combination of Observations. Macmillan & Company.Google Scholar
Amrhein, V., Greenland, S., & McShane, B. (2019). Scientists rise up against statistical significance. Nature, 567(7748), 305–307.Google Scholar
Baguley, T. (2009). Standardized or simple effect size: What should be reported? British Journal of Psychology, 100(3), 603–617.Google Scholar
Baribault, B., Donkin, C., Little, D. R., et al. (2018). Meta-studies for robust tests of theory. Proceedings of the National Academy of Sciences, 115(11), 2607–2612.Google Scholar
Berkey, C. S., Hoaglin, D. C., Antczak-Bouckoms, A., Mosteller, F., & Colditz, G. A. (1998). Meta-analysis of multiple outcomes by regression with random effects. Statistics in Medicine, 17(22), 2537–2550.Google Scholar
Berlin, J. (1995). Benefits of heterogeneity in meta-analysis of data from epidemiologic studies. American Journal of Epidemiology, 142(383–387), 7625402.Google Scholar
BondJr., C. F., Wiitala, W. L., & Richard, F. D. (2003). Meta-analysis of raw mean differences. Psychological Methods, 8(4), 406.Google Scholar
Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009). Introduction to Meta-analysis. Wiley.Google Scholar
Brunner, J., & Schimmack, U. (2016). How replicable is psychology? a comparison of four methods of estimating replicability on the basis of test statistics in original studies. www.utstat.utoronto.ca/~brunner/zcurve2016/HowReplicable.pdfGoogle Scholar
Bryan, C. J., Tipton, E., & Yeager, D. S. (2021). Behavioural science is unlikely to change the world without a heterogeneity revolution. Nature Human Behaviour, 5(8), 980–989.Google Scholar
Cheung, M. W.-L. (2015). Meta-Analysis: A Structural Equation Modeling Approach. Wiley.Google Scholar
Chung, Y., Rabe-Hesketh, S., & Choi, I.-H. (2013). Avoiding zero between-study variance estimates in random-effects meta-analysis. Statistics in Medicine, 32(23), 4071–4089.Google Scholar
Chung, Y., Rabe-Hesketh, S., Dorie, V., Gelman, A., & Liu, J. (2013). A non-degenerate estimator for hierarchical variance parameters via penalized likelihood estimation. Psychometrika, 78(4), 685–709.Google Scholar
Cooper, H., Hedges, L. V., & Valentine, J. C. (2019). The Handbook of Research Synthesis and Meta-analysis. Russell Sage Foundation.Google Scholar
DeKay, M. L., Rubinchik, N., Li, Z., & De Boeck, P. (2022). Accelerating Psychological Science With Metastudies: A Demonstration Using the Risky-Choice Framing Effect. Perspectives on Psychological Science, 17(6), 1704–1736. https://doi.org/10.1177/17456916221079611.Google Scholar
Dijksterhuis, A. (2004). Think different: The merits of unconscious thought in preference development and decision making. Journal of Personality and Social Psychology, 87(5), 586.Google Scholar
Gelman, A. (2015). The connection between varying treatment effects and the crisis of unreplicable research: A bayesian perspective. Journal of Management, 41(2), 632–643.Google Scholar
Gelman, A. (2019a). Comment on “post-hoc power using observed estimate of effect size is too noisy to be useful. Annals of Surgery, 270(2), e64.Google Scholar
Gelman, A. (2019b). Don’t calculate post-hoc power using observed estimate of effect size. Annals of Surgery, 269(1), e9–e10.Google Scholar
Glass, G. V. (1976). Primary, secondary, and meta-analysis of research. Educational Researcher, 5(10), 3–8.Google Scholar
Greenland, S. (1987). Quantitative methods in the review of epidemiologic literature. Epidemiologic Reviews, 9(1), 1–30.Google Scholar
Greenland, S. (1994). Invited commentary: A critical look at some popular meta-analytic methods. American Journal of Epidemiology, 140(3), 290–296.Google Scholar
Greenland, S. (2012). Nonsignificance plus high power does not imply support for the null over the alternative. Annals of Epidemiology, 22(5), 364–368.Google Scholar
Greenland, S. (2017). Invited commentary: The need for cognitive science in methodology. American Journal of Epidemiology, 186(6), 639–645.Google Scholar
Greenland, S., & O’Rourke, K. (2008). Meta-analysis. In Rothman, K. J., Greenland, S., & Lash, T. L. (Eds.). Modern Epidemiology, 3rd ed. Lippincott, Williams, and Wilkins.Google Scholar
Greenland, S., Schlesselman, J. J., & Criqui, M. H. (1986). The fallacy of employing standardized regression coefficients and correlations as measures of effect. American Journal of Epidemiology, 123(2), 203–208.Google Scholar
Hartung, J., & Knapp, G. (2005). On confidence intervals for the among-group variance in the one-way random effects model with unequal error variances. Journal of Statistical Planning and Inference, 127(1–2), 157–177.Google Scholar
Harville, D. A. (1977). Maximum likelihood approaches to variance component estimation and to related problems. Journal of the American Statistical Association, 72(358), 320–338.Google Scholar
Hedges, L. V., & Vevea, J. L. (2005). Selection method approaches. In Rothstein, H. R., Sutton, A. J., & Borenstein, M. (Eds.). Publication Bias in Meta-analysis: Prevention, Assessment and Adjustments (pp. 145–174). John Wiley & Sons.Google Scholar
Higgins, J. P., & Thompson, S. G. (2002). Quantifying heterogeneity in a meta-analysis. Statistics in Medicine, 21(11), 1539–1558.Google Scholar
Hoenig, J. M., & Heisey, D. M. (2001). The abuse of power: The pervasive fallacy of power calculations for data analysis. The American Statistician, 55(1), 19–24.Google Scholar
Huedo-Medina, T. B., Sánchez-Meca, J., Marín-Martínez, F., & Botella, J. (2006). Assessing heterogeneity in meta-analysis: Q statistic or I2 index? Psychological Methods, 11(2), 193.Google Scholar
Ioannidis, J. P., Patsopoulos, N. A., & Evangelou, E. (2007). Uncertainty in heterogeneity estimates in meta-analyses. BMJ, 335, 914.Google Scholar
Iyengar, S. S., & Lepper, M. R. (2000). When choice is demotivating: Can one desire too much of a good thing? Journal of Personality and Social Psychology, 79(6), 996–1006.Google Scholar
Kalaian, H. A., & Raudenbush, S. W. (1996). A multivariate mixed linear model for meta-analysis. Psychological Methods, 1(3), 227–235.Google Scholar
L’Abbé, K., Detsky, A. S., & O’Rourke, K. (1987). Meta-analysis in clinical research. Annals of Internal Medicine, 107(2), 224–233.Google Scholar
Lane, D. M., & Dunlap, W. P. (1978). Estimating effect size: Bias resulting from the significance criterion in editorial decisions. British Journal of Mathematical and Statistical Psychology, 31(2), 107–112.Google Scholar
Light, R. J., & Pillemer, D. B. (1984). Summing Up: The Science of Reviewing Research. Harvard University Press.Google Scholar
Linden, A. H., & Hönekopp, J. (2021). Heterogeneity of research results: A new perspective from which to assess and promote progress in psychological science. Perspectives on Psychological Science, 16(2), 358–376.Google Scholar
McShane, B. B., & Böckenholt, U. (2014). You cannot step into the same river twice: When power analyses are optimistic. Perspectives on Psychological Science, 9(6), 612–625.Google Scholar
McShane, B. B., Böckenholt, U. (2017). Single paper meta-analysis: Benefits for study summary, theory-testing, and replicability. Journal of Consumer Research, 43(6), 1048–1063.Google Scholar
McShane, B. B., & Böckenholt, U. (2018a). Multilevel multivariate meta-analysis with application to choice overload. Psychometrika, 83(1), 255–271.Google Scholar
McShane, B. B., & Böckenholt, U. (2018b). Want to make behavioural research more replicable? Promote single paper meta-analysis. Significance, 15(6), 38–40.Google Scholar
McShane, B. B., & Böckenholt, U. (2019). Meta-analysis. In Kardes, F. R., Herr, P. M., & Schwarz, N. (Eds.). Handbook of Research Methods in Consumer Psychology. Routledge.Google Scholar
McShane, B. B., & Böckenholt, U. (2020). Enriching meta-analytic models of summary data: A thought experiment and case study. Advances in Methods and Practices in Psychological Science, 3(1), 81–93.Google Scholar
McShane, B. B., & Böckenholt, U. (2022a). Meta-analysis of studies with multiple contrasts and differences in measurement scales. Journal of Consumer Psychology, 32(1), 23–40.Google Scholar
McShane, B. B., & Böckenholt, U. (2022b). Multilevel multivariate meta-analysis made easy: An introduction to MLMVmeta. Behavior Research Methods, online ahead of print. doi: 10.3758/s13428-022-01892-7.Google Scholar
McShane, B. B., Böckenholt, U., & Hansen, K. T. (2016). Adjusting for publication bias in meta-analysis: An evaluation of selection methods and some cautionary notes. Perspectives on Psychological Science, 11(5), 730–749.Google Scholar
McShane, B. B., Böckenholt, U., & Hansen, K. T. (2020). Average power: A cautionary note. Advances in Methods and Practices in Psychological Science, 3(2), 185–199.Google Scholar
McShane, B. B., Böckenholt, U. & Hansen, K. T. (2022). Variation and covariation in large-scale replication projects: An evaluation of replicability. Journal of the American Statistical Association, 117(540), 1605–1621. doi: 10.1080/01621459.2022.2054816Google Scholar
McShane, B. B., & Gal, D. (2016). Blinding us to the obvious? The effect of statistical training on the evaluation of evidence. Management Science, 62(6), 1707–1718.Google Scholar
McShane, B. B., & Gal, D. (2017). Statistical significance and the dichotomization of evidence. Journal of the American Statistical Association, 112(519), 885–895.Google Scholar
McShane, B. B., Gal, D., Gelman, A., Robert, C., & Tackett, J. L. (2019). Abandon statistical significance. The American Statistician, 73(sup1), 235–245.Google Scholar
McShane, B. B., Tackett, J. L., Böckenholt, U., & Gelman, A. (2019). Large scale replication projects in contemporary psychological research. The American Statistician, 73(supp1), 99–105.Google Scholar
O’Rourke, K. (2002). Meta-analytical themes in the history of statistics: 1700 to 1938. Pakistan Journal of Statistics, 18(2), 285–299.Google Scholar
O’Rourke, K. (2007). An historical perspective on meta-analysis: Dealing quantitatively with varying study results. Journal of the Royal Society of Medicine, 100(12), 579–582.Google Scholar
Pearson, K. (1904). Report on certain enteric fever inoculation statistics. British Medical Journal, 3, 1243–1246.Google Scholar
Petitti, D. B. (1994). Of babies and bathwater. American Journal of Epidemiology, 140(9), 779–782.Google Scholar
Plackett, R. L. (1958). Studies in the history of probability and statistics: VII. The principle of the arithmetic mean. Biometrika, 45(1–2), 130–135.Google Scholar
Rothstein, H. R., Sutton, A. J., & Borenstein, M., eds. (2005). Publication Bias in Meta-analysis: Prevention, Assessment and Adjustments. John Wiley & Sons.Google Scholar
Rubin, D. B. (1992). Meta-analysis: Literature synthesis or effect-size surface estimation? Journal of Educational Statistics, 17(4), 363–374.Google Scholar
Schimmack, U., & Brunner, J. (2017). -curve: A method for the estimating replicability based on test statistics in original studies. https://replicationindex.files.wordpress.com/2017/11/adv-meth-practices-draft-v17-12-08.pdfGoogle Scholar
Schmidt, F. L., & Hunter, J. E. (2014). Methods of Meta-analysis: Correcting Error and Bias in Research Findings. Sage Publications.Google Scholar
Simmons, J. P., & Simonsohn, U. (2017). Power posing: P-curving the evidence. Psychological Science, 28(5), 687–693.Google Scholar
Simonsohn, U., Nelson, L. D., & Simmons, J. P. (2014). -curve and effect size: Correcting for publication bias using only significant results. Perspectives on Psychological Science, 9(6), 666–681.Google Scholar
Thompson, S. G. (1994). Systematic review: Why sources of heterogeneity in meta-analysis should be investigated. BMJ, 309(6965), 1351–1355.Google Scholar
Tukey, J. W. (1969). Analyzing data: Sanctification or detective work? American Psychologist, 24(2), 83–91.Google Scholar
Vevea, J. L., & Woods, C. M. (2005). Publication bias in research synthesis: Sensitivity analysis using a priori weight functions. Psychological Methods, 10(4), 428.Google Scholar
Viechtbauer, W. (2007). Confidence intervals for the amount of heterogeneity in meta-analysis. Statistics in Medicine, 26(1), 37–52.Google Scholar
Viechtbauer, W. (2021). for multilevel and multivariate models. www.metafor-project.org/doku.php/tips:i2_multilevel_multivariateGoogle Scholar
von Hippel, P. T. (2015). The heterogeneity statistic I2 can be biased in small meta-analyses. BMC Medical Research Methodology, 15(1), 35.Google Scholar
Wilkinson, L. (1999). Statistical methods in psychology journals: Guidelines and explanations. American Psychologist, 54(8), 594–604.Google Scholar
Yuan, K.-H., & Maxwell, S. E. (2005). On the post hoc power in testing mean differences. Journal of Educational and Behavioral Statistics, 30(2), 141–167.Google Scholar
References
Bartl, M., & Casper, C. (2021). Netnography applied: Five lessons learned from 16 years of field experience. In Kozinets, R. V., & Gambetti, R. (Eds.). Netnography Unlimited: Understanding Technoculture Using Qualitative Social Media Research (pp. 149–171). Routledge.Google Scholar
Chakraborty, U., & Biswal, S. K. (2022). Psychological empowerment of women entrepreneurs: A netnographic study on Twitter. Management Research Review, 45(6), 717–734.Google Scholar
Clement, J. (2021). Number of video gamers worldwide in 2021, by region. Statista.com, www.statista.com/statistics/293304/number-video-gamers/Google Scholar
Cooke, T. S. (2021). A Qualitative Metasynthesis of Male Client Experiences of Counseling and a Netnography of Men Seeking Support through an Online Discussion Forum for Victims of Infidelity. Doctoral dissertation, Oregon State University.Google Scholar
DuFault, B. L., & Schouten, J. W. (2020). Self-quantification and the datapreneurial consumer identity. Consumption Markets & Culture, 23(3), 290–316.Google Scholar
Gretzel, U. (2021). Dreaming about travel: A Pinterest netnography. In Wörndl, W., Koo, C., & Stienmetz, J. L. (Eds.). Information and Communication Technologies in Tourism 2021 (pp. 256–268). Springer.Google Scholar
Howard, L. (2016). An exploration of autonetnography as an eResearch methodology to examine learning and teaching scholarship in Networked Learning. Electronic Journal of e-Learning, 14(5), 322–335.Google Scholar
Iannotti, R. J. (1985). Naturalistic and structured assessments of prosocial behavior in preschool children: The influence of empathy and perspective taking. Developmental Psychology, 21(1), 46–55.Google Scholar
Indartoyo, I. M., Kim, D. W., Purwanto, B. M., Gunawan, A., Riantini, R. E., & Gea, D. (2020). Netnography analysis of consumer sentiment towards panic buying in the early period of the COVID-19 virus spread. In 2020 International Conference on Information Management and Technology (ICIMTech) (pp. 626–631). IEEE.Google Scholar
Karagöz, D., Işık, C., Dogru, T., & Zhang, L. (2021). Solo female travel risks, anxiety and travel intentions: Examining the moderating role of online psychological-social support. Current Issues in Tourism, 24(11), 1595–1612.Google Scholar
Kozinets, R. V. (2019), Consuming technocultures: An extended JCR curation. Journal of Consumer Research, 46(3), 620–627.Google Scholar
Kozinets, R. V. (2020), Netnography: The Essential Guide to Qualitative Social Media Research. Sage.Google Scholar
Kozinets, R. V., Ferreira, D. A., & Chimenti, P. (2021). How do platforms empower consumers? Insights from the affordances and constraints of Reclame Aqui. Journal of Consumer Research, 48(3), 428–455.Google Scholar
Kozinets, R. V., & Gambetti, R. (Eds.). (2021). Netnography Unlimited: Understanding Technoculture Using Qualitative Social Media Research. Routledge.Google Scholar
Kozinets, R. V., Gretzel, U., & Dinhopl, A. (2017). Self in art/self as art: Museum selfies as identity work. Frontiers in Psychology, 8(May), 1–12.Google Scholar
Kozinets, R. V., & Kedzior, R. (2009). I, avatar: Auto-netnographic research in virtual worlds. In Solomon, M., & Wood, N. (Eds.). Virtual Social Identity and Consumer Behavior, (pp. 3–19). M. E. Sharpe.Google Scholar
Latham, G. P., Fay, C. H., & Saari, L. M. (1979). The development of behavioral observation scales for appraising the performance of foremen. Personnel Psychology, 32(2), 299–311.Google Scholar
Lizzo, R., & Liechty, T. (2020). The Hogwarts Running Club and sense of community: A netnography of a virtual community. Leisure Sciences, DOI: 10.1080/01490400.2020.1755751Google Scholar
Lumma, A. L., Hackert, B., & Weger, U. (2020). Insights from the inside of empathy: Investigating the experiential dimension of empathy through introspection. Philosophical Psychology, 33(1), 64–85.Google Scholar
Markham, A. (2012). Fabrication as ethical practice: Qualitative inquiry in ambiguous internet contexts. Information, Communication & Society, 15(3), 334–353.Google Scholar
Markham, A. N. (2016). Ethnography in the digital internet era. In Denzin, N. K., & Lincoln, Y. S. (Eds.). The Sage Handbook of Qualitative Research, 5th ed. (pp. 650–668). Sage.Google Scholar
McMillan, D. W., & Chavis, D. M. (1986). Sense of community: A definition and theory. Journal of Community Psychology, 14(1), 6–23.Google Scholar
Miles, M. B., Huberman, A. M., & Saldaña, J. (2014). Qualitative Data Analysis: A Methods Sourcebook. Sage.Google Scholar
Miller, D. B. (1977). Roles of naturalistic observation in comparative psychology. American Psychologist, 32(3), 211–219.Google Scholar
Nasu, V. H. (2020). Remote learning under COVID-19 social distancing: Discussion, resources, implications for accounting faculty and students, and a netnography study. In Proceedings of the XX USP International Conference in Accounting, São Paulo, Brazil (pp. 29–31).Google Scholar
Odekerken-Schröder, G., Mele, C., Russo-Spena, T., Mahr, D., & Ruggiero, A. (2020). Mitigating loneliness with companion robots in the COVID-19 pandemic and beyond: An integrative framework and research agenda. Journal of Service Management, 31(6), 1149–1162.Google Scholar
Rosario, A. B., Russell, C. A., & Shanahan, D. E. (2022). Paradoxes of social support in virtual support communities: A mixed-method inquiry of the social dynamics in health and wellness Facebook groups. Journal of Interactive Marketing, 57(1), 54–89.Google Scholar
Schuman, D. L., Schuman, D. L., Pope, N., & Johnson, A. (2021). Netnography in a military context: Ethical considerations. In Kozinets, R. V., & Gambetti, R. (Eds.) Netnography Unlimited: Understanding Technoculture Using Qualitative Social Media Research (pp. 83–99). Routledge.Google Scholar
Tankovska, H. (2021). Mobile social media worldwide - Statistics & Facts. Statista, www.statista.com/topics/2478/mobile-social-networks/Google Scholar
Uram, P., & Skalski, S. (2022). Still logged in? The link between Facebook addiction, FoMO, self-esteem, life satisfaction and loneliness in social media users. Psychological Reports, 125(1), 218–231.Google Scholar
Wells, W. D. (1993). Discovery-oriented consumer research. Journal of Consumer Research, 19(4), 489–504.Google Scholar
Wertz, F. J. (2021). Objectivity and eidetic generality in psychology: The value of explicating fundamental methods. Qualitative Psychology, 8(1), 125–140.Google Scholar
References
Alba, J. W. (2012). In defense of bumbling. Journal of Consumer Research, 38(6), 981–987.Google Scholar
Bem, D. J. (1987). Writing the empirical journal article. In Zanna, M., & Darley, J. (Eds.). The Compleat Academic: A Practical Guide for the Beginning Social Scientist (pp. 171–201). Random House.Google Scholar
Claesen, A., Gomes, S. L. B. T., Tuerlinckx, F., & Vanpaemel, W. (2019). Preregistration: Comparing dream to reality. Working paper: KU Leuven. PsyArXiv, https://psyarxiv.com/d8wexGoogle Scholar
Forstmeier, W., Wagenmakers, E.-J., & Parker, T. H. (2017). Detecting and avoiding likely false-positive findings – A practical guide. Biological Reviews, 92(4), 1941–1968.Google Scholar
Fuchs, C., Schreier, M., & van Osselaer, S. M. J. (2015). The handmade effect: What’s love got to do with it? Journal of Marketing, 79(2), 98–110.Google Scholar
Ikeda, A., Xu, H., Fuji, N., Zhu, S., & Yamada, Y. (2019). Questionable research practices following pre-registration. Japanese Psychological Review, 62(3), 281–295.Google Scholar
Janiszewski, C., & van Osselaer, S. M. J. (2021). The benefits of candidly reporting consumer research. Journal of Consumer Psychology, 31(4), 633–646.Google Scholar
Janiszewski, C., & van Osselaer, S. M. J. (2022). Abductive theory construction. Journal of Consumer Psychology, 32(1), 175–193.Google Scholar
Kerr, N. L. (1998). HARKing: Hypothesizing after the results are known. Personality and Social Psychology Review, 2(3), 196–217.Google Scholar
Klesse, A., Levav, J., & Goukens, C. (2015). The effect of preference expression modality on self-control. Journal of Consumer Research, 42(4), 535–550.Google Scholar
LynchJr., J. G., Alba, J. W., Krishna, A., Morwitz, V. G., & Gürhan-Canli, Z. (2012). Knowledge creation in consumer research: Multiple routes, multiple criteria. Journal of Consumer Psychology, 22(4), 473–485.Google Scholar
Meyvis, T., & van Osselaer, S. M. J. (2018). Increasing the power of your study by increasing effect size. Journal of Consumer Research, 44(5), 1157–1173.Google Scholar
Norton, M. I., Mochon, D., & Ariely, D. (2012). The IKEA effect: When labor leads to love. Journal of Consumer Psychology, 22(3), 453–460.Google Scholar
Nosek, B. A., & Lakens, D. (2014). A method to increase the credibility of published results. Social Psychology, 45(3), 137–141.Google Scholar
Pham, M. T. (2013). The seven sins of consumer psychology. Journal of Consumer Psychology, 23(4), 411–423.Google Scholar
Pham, M. T., & Tae Oh, T. (2021). Preregistration is neither sufficient nor necessary for good science. Journal of Consumer Psychology, 31(1), 163–176.Google Scholar
Rohrer, J. M. (2018, February 28). Run all the models! Dealing with data analytic flexibility. Association for Psychological Science Observer, www.psychologicalscience.org/observer/run-all-the-models-dealing-with-data-analytic-flexibilityGoogle Scholar
Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011). False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological Science, 22(11), 1359–1366.Google Scholar
Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2021). Pre-registration: Why and how. Journal of Consumer Psychology, 31(1), 151–162.Google Scholar
Simonsohn, U., Nelson, L. D., & Simmons, J. P. (2014). P-curve: A key to the file-drawer. Journal of Experimental Psychology: General, 143(2), 534–547.Google Scholar
Simonsohn, U., Simmons, J. P. & Nelson, L. D. (2020). Specification curve analysis. Nature Human Behavior, 4(11), 1208–1214.Google Scholar
Sweldens, S., van Osselaer, S. M. J., & Janiszewski, C. (2010). Evaluative conditioning procedures and the resilience of conditioned brand attitudes. Journal of Consumer Research, 37(3), 473–489.Google Scholar
Thaler, R. (1981). Some empirical evidence on dynamic inconsistency. Economics Letters, 8(3), 201–207.Google Scholar
Thompson, C. J., Mick, D. G., van Osselaer, S. M. J., & Huber, J. (in press). Commentaries on “The Case for Qualitative Research.” Journal of Consumer Psychology, https://doi.org/10.1002/jcpy.1299Google Scholar
van Osselaer, S. M. J., & Lim, S. (2019). Research productivity of faculty at 30 leading marketing departments. Marketing Letters, 30(3), 121–137.Google Scholar
Yamada, Y. (2018). How to crack pre-registration: Toward transparent and open science. Frontiers in Psychology, 9, 1831.Google Scholar
Yarkoni, T. (2019, November 22). The generalizability crisis. Working paper: University of Texas-Austin. PsyArXiv, https://psyarxiv.com/jqw35Google Scholar