An experiment on information presentation and investor mutual fund selection

References

• Abdi, H. (2010). Holm’s sequential Bonferroni procedure. Encyclopedia of Research Design, 1(8), 1–22.
   Google Scholar
• Aguinis, H., Villamor, I., & Ramani, R. S. (2021). Mturk research: Review and recommendations. Journal of Management, 47(4), 823–837. https://doi.org/10.1177/0149206320969787
   Web of Science ®Google Scholar
• Aickin, M., & Gensler, H. (1996). Adjusting for multiple testing when reporting research results: The Bonferroni vs Holm methods. American Journal of Public Health, 86(5), 726–728. https://doi.org/10.2105/AJPH.86.5.726
   PubMed Web of Science ®Google Scholar
• Al Rahahleh, N., & Bhatti, M. I. (2022). Empirical comparison of Shariah-compliant vs conventional mutual fund performance. International Journal of Emerging Markets. https://doi.org/10.1108/IJOEM-05-2020-0565
   Web of Science ®Google Scholar
• Al Rahahleh, N., Bhatti, M. I., & Misman, M. N. (2019). Developments in risk management in Islamic finance: A review. Journal of Risk and Financial Management, 12(1), 37. https://doi.org/10.3390/jrfm12010037
   Web of Science ®Google Scholar
• Anic, V., & Wallmeier, M. (2020). Perceived attractiveness of structured financial products: The role of presentation format and reference instruments. Journal of Behavioral Finance, 21(1), 78–102. https://doi.org/10.1080/15427560.2019.1629441
   Web of Science ®Google Scholar
• Armstrong, R. A. (2014). When to use the Bonferroni correction. Ophthalmic and Physiological Optics, 34(5), 502–508. https://doi.org/10.1111/opo.12131
   PubMed Web of Science ®Google Scholar
• Aronow, P. M., Baron, J., & Pinson, L. (2019). A note on dropping experimental subjects who fail a manipulation check. Political Analysis, 27(4), 572–589. https://doi.org/10.1017/pan.2019.5
   Web of Science ®Google Scholar
• Ashraf, D., Khawaja, M., & Bhatti, M. I. (2022). Raising capital amid economic policy uncertainty: An empirical investigation. Financial Innovation, 8(1), 1–32. https://doi.org/10.1186/s40854-022-00379-w
   Web of Science ®Google Scholar
• Bender, R., & Lange, S. (2001). Adjusting for multiple testing—when and how? Journal of Clinical Epidemiology, 54(4), 343–349. https://doi.org/10.1016/S0895-4356(00)00314-0
   PubMed Web of Science ®Google Scholar
• Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B (Methodological), 57(1), 289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x
   Web of Science ®Google Scholar
• Beshears, J. L., Choi, J. J., Laibson, D. I., & Madrian, B. C. (2011). How does simplified disclosure affect individuals’ mutual fund choices? SSRN Journal University of Chicago Press, Chicago, 13, 75.
   Google Scholar
• Blakesley, R. E., Mazumdar, S., Dew, M. A., Houck, P. R., Tang, G., Reynolds, C. F., & Butters, M. A. (2009). Comparisons of methods for multiple hypothesis testing in neuropsychological research. Neuropsychology, 23(2), 255–264. https://doi.org/10.1037/a0012850
   PubMed Web of Science ®Google Scholar
• Boateng, G. O., Neilands, T. B., Frongillo, E. A., Melgar-Quiñonez, H. R., & Young, S. L. (2018). Best practices for developing and validating scales for health, social, and behavioral research: A primer. Frontiers in Public Health, 6. https://doi.org/10.3389/fpubh.2018.00149
   PubMed Web of Science ®Google Scholar
• Cardoso, R. L., Leite, R. O., Aquino, A. C. B. D., & García-Gallego, A. (2016). A graph is worth a thousand words: How overconfidence and graphical disclosure of numerical information influence financial analysts accuracy on decision making. PLoS One, 11(8), e0160443. https://doi.org/10.1371/journal.pone.0160443
   PubMed Web of Science ®Google Scholar
• Carmer, S. G., & Swanson, M. R. (1973). An evaluation of ten pairwise multiple comparison procedures by Monte Carlo methods. Journal of the American Statistical Association, 68(341), 66–74. https://doi.org/10.1080/01621459.1973.10481335
   Web of Science ®Google Scholar
• Castaiieda, M. B., Levin, J. R., & Dunham, R. B. (1993). Using planned comparisons in management research: A case for the Bonferroni procedure. Journal of Management, 19(3), 707–724. https://doi.org/10.1177/014920639301900311
   Web of Science ®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. https://doi.org/10.3758/s13428-019-01273-7
   PubMed Web of Science ®Google Scholar
• Choi, J. J., Laibson, D., & Madrian, B. C. (2010). Why does the law of one price fail? An experiment on index mutual funds. The Review of Financial Studies, 23(4), 1405–1432. https://doi.org/10.1093/rfs/hhp097
   PubMed Web of Science ®Google Scholar
• DeSanctis, G. (1984). Computer graphics as decision aids: Directions for research. Decision Sciences, 15(4), 463–487. https://doi.org/10.1111/j.1540-5915.1984.tb01236.x
   Web of Science ®Google Scholar
• Dew, J., & Xiao, J. (2011). The financial management behavior scale: Development and validation. Financial Counseling and Planning, 22(1), 43.
   Google Scholar
• Eberhard, K. (2021). The effects of visualization on judgment and decision-making: A systematic literature review. Management Review Quarterly, 73(1), 167–214. https://doi.org/10.1007/s11301-021-00235-8
   Google Scholar
• Eichstaedt, K. E., Kovatch, K., Maroof, D. A., Greenwald, B. D., & Gurley, J. M. (2013). A less conservative method to adjust for familywise error rate in neuropsychological research: The Holm’s sequential Bonferroni procedure. NeuroRehabilitation, 32, 693–696. https://doi.org/10.3233/NRE-130893
   PubMed Web of Science ®Google Scholar
• Ellis, P. D. (2010). The essential guide to effect sizes: Statistical power, meta-analysis, and the interpretation of research results. Cambridge University Press.
   Google Scholar
• Félix, V., & Menezes, A. (2018). Comparisons of ten corrections methods for t-test in multiple comparisons via Monte Carlo study. The Electronic Journal of Applied Statistical Analysis, 11(1), 74–91.
   Web of Science ®Google Scholar
• Guo, X., Liang, C., Umar, M., & Mirza, N. (2022). The impact of fossil fuel divestments and energy transitions on mutual funds performance. Technological Forecasting and Social Change, 176, 121429. https://doi.org/10.1016/j.techfore.2021.121429
   Web of Science ®Google Scholar
• Harcourt-Cooke, C., Els, G., & van Rensburg, E. (2022). Using comics to improve financial behaviour. Journal of Behavioral and Experimental Finance, 33, 100614. https://doi.org/10.1016/j.jbef.2021.100614
   Web of Science ®Google Scholar
• Harpe, S. E. (2015). How to analyze Likert and other rating scale data. Currents in Pharmacy Teaching and Learning, 7(6), 836–850. https://doi.org/10.1016/j.cptl.2015.08.001
   Web of Science ®Google Scholar
• Harvey, N., & Bolger, F. (1996). Graphs versus tables: Effects of data presentation format on judgemental forecasting. International Journal of Forecasting, 12(1), 119–137. https://doi.org/10.1016/0169-2070(95)00634-6
   Web of Science ®Google Scholar
• High, R. (2007, September 17). Introduction to statistical power calculations for linear models with SAS 9.1. Retrieved June, 13, 2011.
   Google Scholar
• Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scand J Stat, 6(2), 65–70.
   Web of Science ®Google Scholar
• Huang, J., Wei, K. D., & Yan, H. (2022). Investor learning and mutual fund flows. Financial Management, 51(3), 739–765. https://doi.org/10.1111/fima.12378
   Web of Science ®Google Scholar
• Iannotta, G., & Navone, M. (2012). The cross-section of mutual fund fee dispersion. Journal of Banking & Finance, 36(3), 846–856. https://doi.org/10.1016/j.jbankfin.2011.09.013
   Web of Science ®Google Scholar
• Isler, O., Rojas, A., & Dulleck, U. (2022). Easy to shove, difficult to show: Effect of educative and default nudges on financial self-management. Journal of Behavioral and Experimental Finance, 34, 100639. https://doi.org/10.1016/j.jbef.2022.100639
   Web of Science ®Google Scholar
• Kang, H. (2021). Sample size determination and power analysis using the G*Power software. Journal of Educational Evaluation for Health Professions, 18, 17. https://doi.org/10.3352/jeehp.2021.18.17
   PubMed Web of Science ®Google Scholar
• Kennedy-Shaffer, L. (2019). Before p < 0.05 to beyond p < 0.05: Using history to contextualize p-values and significance testing. The American Statistician, 73(sup. 1), 82–90. https://doi.org/10.1080/00031305.2018.1537891
   PubMedGoogle Scholar
• Kim, J. H., Ahmed, K., & Ji, P. I. (2018). Significance testing in accounting research: A critical evaluation based on evidence: SIGNIFICANCE TESTING. Abacus, 54(4), 524–546. https://doi.org/10.1111/abac.12141
   Web of Science ®Google Scholar
• Lee, S., & Lee, D. K. (2018). What is the proper way to apply the multiple comparison test? Korean Journal of Anesthesiology, 71(5), 353–360. https://doi.org/10.4097/kja.d.18.00242
   PubMed Web of Science ®Google Scholar
• Lenth, R. V. (2001). Some practical guidelines for effective sample size determination. The American Statistician, 55(3), 187–193. https://doi.org/10.1198/000313001317098149
   Web of Science ®Google Scholar
• Manderscheid, L. V. (1965). Significance levels. 0.05, 0.01, Or? Journal of Farm Economics, 47(5), 1381–1385. https://doi.org/10.2307/1236396
   Google Scholar
• Mansor, F., Bhatti, M. I., & Ariff, M. (2015). New evidence on the impact of fees on mutual fund performance of two types of funds. Journal of International Financial Markets, Institutions and Money, 35, 102–115. https://doi.org/10.1016/j.intfin.2014.12.009
   Web of Science ®Google Scholar
• Mauck, N., & Salzsieder, L. (2017). Diversification bias and the law of one price: An experiment on index mutual funds. Journal of Behavioral Finance, 18(1), 45–53. https://doi.org/10.1080/15427560.2017.1276067
   Web of Science ®Google Scholar
• Ma, Y., Xiao, K., Zeng, Y., & Koijen, R. (2022). Mutual fund liquidity transformation and reverse flight to liquidity. The Review of Financial Studies, 35(10), 4674–4711. https://doi.org/10.1093/rfs/hhac007
   Web of Science ®Google Scholar
• Nakagawa, S. (2004). A farewell to Bonferroni: The problems of low statistical power and publication bias. Behavioral Ecology, 15(6), 1044–1045. https://doi.org/10.1093/beheco/arh107
   Web of Science ®Google Scholar
• Newall, P. W. S., & Love, B. C. (2015). Nudging investors big and small toward better decisions. Decision, 2(4), 319–326. https://doi.org/10.1037/dec0000036
   Google Scholar
• Newall, P. W. S., & Parker, K. N. (2019). Improved mutual fund investment choice architecture. Journal of Behavioral Finance, 20(1), 96–106. https://doi.org/10.1080/15427560.2018.1464455
   Web of Science ®Google Scholar
• Norman, G. (2010). Likert scales, levels of measurement and the “laws” of statistics. Advances in Health Sciences Education, 15(5), 625–632. https://doi.org/10.1007/s10459-010-9222-y
   PubMed Web of Science ®Google Scholar
• O’brien, R., & Castelloe, J., 2004. Sample-size analysis in study planning: Concepts and issues, with examples using. Proc Power and Proc. GLMPOWER. Proc. SUGI 29 Conference. Montreal, Canada.
   Google Scholar
• Olejnik, S., Li, J., Supattathum, S., & Huberty, C. J. (1997). Multiple testing and statistical power with modified Bonferroni procedures. Journal of Educational and Behavioral Statistics, 22(4), 389–406. https://doi.org/10.3102/10769986022004389
   Web of Science ®Google Scholar
• Palan, S., & Schitter, C. (2018). Prolific.Ac—a subject pool for online experiments. Journal of Behavioral and Experimental Finance, 17, 22–27. https://doi.org/10.1016/j.jbef.2017.12.004
   Web of Science ®Google Scholar
• Park, H. M., 2015. Hypothesis testing and statistical power of a test. https://scholarworks.iu.edu/dspace/handle/2022/19738.
   Google Scholar
• Rafter, J. A., Abell, M. L., & Braselton, J. P. (2002). Multiple comparison methods for means. SIAM Review, 44(2), 259–278. https://doi.org/10.1137/S0036144501357233
   Web of Science ®Google Scholar
• Rosdini, D., Sari, P. Y., Amrania, G. K. P., & Yulianingsih, P. (2020). Decision making biased: How visual illusion, mood, and information presentation plays a role. Journal of Behavioral and Experimental Finance, 27, 100347. https://doi.org/10.1016/j.jbef.2020.100347
   Web of Science ®Google Scholar
• Scholl, B., & Fontes, A. (2022). Mutual fund knowledge assessment for policy and decision problems. Financial Services Review, 30(1), 31–56.
   Google Scholar
• Tekin, E., & Roediger, H. L. (2017). The range of confidence scales does not affect the relationship between confidence and accuracy in recognition memory. Cognitive Research: Principles and Implications, 2(1), 49. https://doi.org/10.1186/s41235-017-0086-z
   PubMedGoogle Scholar