Skip to main content Accessibility help
×
Hostname: page-component-586b7cd67f-g8jcs Total loading time: 0 Render date: 2024-11-25T21:16:33.609Z Has data issue: false hasContentIssue false

6 - Building the Study

from Part I - From Idea to Reality: The Basics of Research

Published online by Cambridge University Press:  25 May 2023

Austin Lee Nichols
Affiliation:
Central European University, Vienna
John Edlund
Affiliation:
Rochester Institute of Technology, New York
Get access

Summary

This chapter discusses the key elements involved when building a study. Planning empirical studies presupposes a decision about whether the major goal of the study is confirmatory (i.e., tests of hypotheses) or exploratory in nature (i.e., development of hypotheses or estimation of effects). Focusing on confirmatory studies, we discuss problems involved in obtaining an appropriate sample, controlling internal and external validity when designing the study, and selecting statistical hypotheses that mirror the substantive hypotheses of interest. Building a study additionally involves decisions about the to-be-employed statistical test strategy, the sample size required by this strategy to render the study informative, and the most efficient way to achieve this so that study costs are minimized without compromising the validity of inferences. Finally, we point to the many advantages of study preregistration before data collection begins.

Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2023

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aberson, C. L. (2019). Applied Power Analysis for the Behavioral Sciences, 2nd ed. Routledge, Taylor & Francis Group.Google Scholar
Anscombe, F. J. (1954). Fixed-sample-size analysis of sequential observations. Biometrics, 10, 89100. https://doi.org/10.2307/3001665CrossRefGoogle Scholar
Bakan, D. (1966). The test of significance in psychological research. Psychological Bulletin, 66(6), 423437. https://doi.org/10.1037/h0020412Google Scholar
Bakker, M., van Dijk, A., & Wicherts, J. M. (2012). The rules of the game called psychological science. Perspectives on Psychological Science, 7(6), 543554. https://doi.org/10.1177/1745691612459060CrossRefGoogle ScholarPubMed
Bakker, M., Veldkamp, C. L. S., van Assen, M. A. L. M., et al. (2020). Ensuring the quality and specificity of preregistrations. PLOS Biology, 18(12), e3000937. https://doi.org/10.1371/journal.pbio.3000937Google Scholar
Barnard, G. A. (1946). Sequential tests in industrial statistics. Supplement to the Journal of the Royal Statistical Society, 8(1), 126. https://doi.org/10.2307/2983610CrossRefGoogle Scholar
Bredenkamp, J. (1972). Der Signifikanztest in der psychologischen Forschung [The Test of Significance in Psychological Research]. Akademische Verlagsgesellschaft.Google Scholar
Bredenkamp, J. (1980). Theorie und Planung psychologischer Experimente [Theory and Planning of Psychological Experiments]. Steinkopff.CrossRefGoogle Scholar
Brysbaert, M. (2019). How many participants do we have to include in properly powered experiments? A tutorial of power analysis with reference tables. Journal of Cognition, 2(16), 138. https://doi.org/10.5334/joc.72Google Scholar
Brysbaert, M. & Stevens, M. (2018). Power analysis and effect size in mixed effects models: A tutorial. Journal of Cognition, 1(1), 9. https://doi.org/10.5334/joc.10Google Scholar
Campbell, D. T. (1957). Factors relevant to the validity of experiments in social settings. Psychological Bulletin, 54(4), 297312. https://doi.org/10.1037/h0040950CrossRefGoogle Scholar
Campbell, J. I. D. & Thompson, V. A. (2012). MorePower 6.0 for ANOVA with relational confidence intervals and Bayesian analysis. Behavior Research Methods, 44(4), 12551265. https://doi.org/10.3758/s13428-012-0186-0Google Scholar
Chambers, C. D. & Tzavella, L. (2020). The past, present, and future of registered reports [Preprint]. MetaArXiv. https://doi.org/10.31222/osf.io/43298CrossRefGoogle Scholar
Champely, S. (2020). pwr: Basic functions for power analysis [Manual]. Available at: https://CRAN.R-project.org/package=pwr.Google Scholar
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences, 2nd ed. Erlbaum.Google Scholar
Cooper, E. H. & Pantle, A. J. (1967). The total-time hypothesis in verbal learning. Psychological Bulletin, 68(4), 221234. https://doi.org/10.1037/h0025052CrossRefGoogle ScholarPubMed
Craik, F. I. M. & Lockhart, R. S. (1972). Levels of processing: A framework for memory research. Journal of Verbal Learning and Verbal Behavior, 11(6), 671684. https://doi.org/10.1016/S0022-5371(72)80001-XGoogle Scholar
Cumming, G. (2014). The new statistics: Why and how. Psychological Science, 25, 729. https://doi.org/10.1177/0956797613504966Google Scholar
Edwards, W., Lindman, H., & Savage, L. J. (1963). Bayesian statistical inference for psychological research. Psychological Review, 70(3), 193242. https://doi.org/10.1037/h0044139CrossRefGoogle Scholar
Erdfelder, E. (1984). Zur Bedeutung und Kontrolle des beta-Fehlers bei der inferenzstatistischen Prüfung log-linearer Modelle [On importance and control of beta errors in statistical tests of log-linear models]. Zeitschrift für Sozialpsychologie, 15, 1832.Google Scholar
Erdfelder, E. (1994). Erzeugung und Verwendung empirischer Daten [Generation and Use of Empirical Data]. In Herrmann, T. & Tack, W. (eds.), Methodologische Grundlagen der Psychologie (Vol. 1, pp. 47–97). Hogrefe.Google Scholar
Erdfelder, E. & Bredenkamp, J. (1994). Hypothesenprüfung [Hypothesis Testing]. In Herrmann, T. & Tack, W. (eds.), Methodologische Grundlagen der Psychologie (Vol. 1, pp. 604–648). Hogrefe.Google Scholar
Erdfelder, E., Faul, F., & Buchner, A. (1996). GPOWER: A general power analysis program. Behavior Research Methods, Instruments, & Computers, 28(1), 111. https://doi.org/10.3758/BF03203630CrossRefGoogle Scholar
Falk, A. & Heckman, J. J. (2009). Lab experiments are a major source of knowledge in the social sciences. Science, 326(5952), 535538. https://doi.org/10.1126/science.1168244CrossRefGoogle Scholar
Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175191. https://doi.org/10.3758/BF03193146CrossRefGoogle ScholarPubMed
Foster, E. D. & Deardorff, A. (2017). Open Science Framework (OSF). Journal of the Medical Library Association, 105(2), 203206. https://doi.org/10.5195/JMLA.2017.88CrossRefGoogle Scholar
Fu, Q., Hoijtink, H., & Moerbeek, M. (2021). Sample-size determination for the Bayesian t test and Welch’s test using the approximate adjusted fractional Bayes factor. Behavior Research Methods, 53(1), 139152. https://doi.org/10.3758/s13428-020-01408-1CrossRefGoogle ScholarPubMed
Gelman, A. & Carlin, J. (2014). Beyond power calculations: Assessing type S (sign) and type M (magnitude) errors. Perspectives on Psychological Science, 9(6), 641651. https://doi.org/10.1177/1745691614551642Google Scholar
Gigerenzer, G. (1993). The superego, the ego, and the id in statistical reasoning. In Keren, G. & Lewis, C. (eds.), A Handbook for Data Analysis in the Behavioral Sciences (pp. 311339). Erlbaum.Google Scholar
Gigerenzer, G. (2004). Mindless statistics. The Journal of Socio-Economics, 33(5), 587606. https://doi.org/10.1016/j.socec.2004.09.033CrossRefGoogle Scholar
Green, P., & MacLeod, C. J. (2016). SIMR: An R package for power analysis of generalized linear mixed models by simulation. Methods in Ecology and Evolution, 7(4), 493498. https://doi.org/10.1111/2041-210X.12504CrossRefGoogle Scholar
Greve, W., Bröder, A., & Erdfelder, E. (2013). Result-blind peer reviews and editorial decisions: A missing pillar of scientific culture. European Psychologist, 18(4), 286294. https://doi.org/10.1027/1016-9040/a000144CrossRefGoogle Scholar
Guven, C. & Lee, W.-S. (2015). Height, aging and cognitive abilities across Europe. Economics & Human Biology, 16, 1629. https://doi.org/10.1016/j.ehb.2013.12.005CrossRefGoogle ScholarPubMed
Hays, W. L. (1963). Statistics. Holt, Rinehart and Winston.Google Scholar
Heck, D. W. & Erdfelder, E. (2019). Maximizing the expected information gain of cognitive modeling via design optimization. Computational Brain & Behavior, 2(3–4), 202209. https://doi.org/10.1007/s42113-019-00035-0Google Scholar
Highhouse, S. & Gillespie, J. Z. (2009). Do samples really matter that much? In C. E. Lance & R. J. Vandenberg (eds.), Statistical and Methodological Myths and Urban Legends: Doctrine, Verity and Fable in the Organizational and Social Sciences (pp. 247265). Routledge, Taylor & Francis Group.Google Scholar
Jager, J., Putnick, D. L., & Bornstein, M. H. (2017). More than just convenient: The scientific merits of homogeneous convenience samples. Monographs of the Society for Research in Child Development, 82(2), 1330. https://doi.org/10.1111/mono.12296Google Scholar
John, L. K., Loewenstein, G., & Prelec, D. (2012). Measuring the prevalence of questionable research practices with incentives for truth telling. Psychological Science, 23(5), 524532. https://doi.org/10.1177/0956797611430953Google Scholar
Kerr, N. L. (1998). HARKing: Hypothesizing after the results are known. Personality and Social Psychology Review, 2(3), 196217. https://doi.org/10.1207/s15327957pspr0203_4Google Scholar
Kruschke, J. K. (2013). Bayesian estimation supersedes the t test. Journal of Experimental Psychology: General, 142(2), 573603. https://doi.org/10.1037/a0029146Google Scholar
Kumle, L., , M. L.-H., & Draschkow, D. (2021). Estimating power in (generalized) linear mixed models: An open introduction and tutorial in R. Behavior Research Methods, 53, 25282543. https://doi.org/10.3758/s13428-021-01546-0Google Scholar
Lakatos, I. (1978). The Methodology of Scientific Research Programmes. Cambridge University Press.CrossRefGoogle Scholar
Lakens, D. (2021). The practical alternative to the p value is the correctly used p value. Perspectives on Psychological Science, 16(3), 639648. https://doi.org/10.1177/1745691620958012CrossRefGoogle Scholar
Lakens, D. (2022). Sample size justification. Collabra: Psychology, 8(1), 33267. https://doi.org/10.1525/collabra.33267Google Scholar
Lakens, D. & Caldwell, A. R. (2021). Simulation-based power analysis for factorial analysis of variance designs. Advances in Methods and Practices in Psychological Science, 4(1), 251524592095150. https://doi.org/10.1177/2515245920951503Google Scholar
Lakens, D., Adolfi, F. G., Albers, C. J., et al. (2018). Justify your alpha. Nature Human Behaviour, 2(3), 168171. https://doi.org/10.1038/s41562-018-0311-xGoogle Scholar
Lakens, D., Pahlke, F., & Wassmer, G. (2021). Group sequential designs: A tutorial [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/x4azmCrossRefGoogle Scholar
Landers, R. N. & Behrend, T. S. (2015). An inconvenient truth: Arbitrary distinctions between organizational, mechanical turk, and other convenience samples. Industrial and Organizational Psychology, 8(2), 142164. https://doi.org/10.1017/iop.2015.13Google Scholar
Leatherdale, S. T. (2019). Natural experiment methodology for research: A review of how different methods can support real-world research. International Journal of Social Research Methodology, 22(1), 1935. https://doi.org/10.1080/13645579.2018.1488449CrossRefGoogle Scholar
Lin, H., Werner, K. M., & Inzlicht, M. (2021). Promises and perils of experimentation: The mutual-internal-validity problem. Perspectives on Psychological Science, 16(4), 854863. https://doi.org/10.1177/1745691620974773CrossRefGoogle ScholarPubMed
Mayo, D. G. (2018). Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars. Cambridge University Press.Google Scholar
Meiser, T. (2011). Much pain, little gain? Paradigm-specific models and methods in experimental psychology. Perspectives on Psychological Science, 6(2), 183191. https://doi.org/10.1177/1745691611400241Google Scholar
Miller, J. & Ulrich, R. (2020). A simple, general, and efficient method for sequential hypothesis testing: The independent segments procedure. Psychological Methods, 26(4), 486497. https://doi.org/10.1037/met0000350CrossRefGoogle ScholarPubMed
Morey, R. D., Rouder, J. N., Verhagen, J., & Wagenmakers, E.-J. (2014). Why hypothesis tests are essential for psychological science: A comment on Cumming (2014). Psychological Science, 25, 12891290. https://doi.org/10.1177/0956797614525969Google Scholar
Nosek, B. A., Ebersole, C. R., DeHaven, A. C., & Mellor, D. T. (2018). The preregistration revolution. Proceedings of the National Academy of Sciences, 115(11), 26002606. https://doi.org/10.1073/pnas.1708274114CrossRefGoogle ScholarPubMed
Pashler, H. & Wagenmakers, E.-J. (2012). Editors’ introduction to the special section on replicability in psychological science: A crisis of confidence? Perspectives on Psychological Science, 7(6), 528530. https://doi.org/10.1177/1745691612465253Google Scholar
Perugini, M., Gallucci, M., & Costantini, G. (2014). Safeguard power as a protection against imprecise power estimates. Perspectives on Psychological Science, 9, 319332. https://doi.org/10.1177/1745691614528519CrossRefGoogle Scholar
Perugini, M., Gallucci, M., & Costantini, G. (2018). A practical primer to power analysis for simple experimental designs. International Review of Social Psychology, 31(1). https://doi.org/10.5334/irsp.181Google Scholar
Popper, K. R. (1968). The Logic of Scientific Discovery, 3rd ed. Hutchinson.Google Scholar
Reiber, F., Schnuerch, M., & Ulrich, R. (2020). Improving the efficiency of surveys with randomized response models: A sequential approach based on curtailed sampling. Psychological Methods, 27(2), 198211. https://doi.org/10.1037/met0000353Google Scholar
Reichenbach, H. (1938). Experience and Prediction: An Analysis of the Foundations and the Structure of Knowledge. University of Chicago Press. https://doi.org/10.1037/11656-000Google Scholar
Roberts, S. & Pashler, H. (2000). How persuasive is a good fit? A comment on theory testing. Psychological Review, 107(2), 358367. https://doi.org/10.1037/0033-295X.107.2.358CrossRefGoogle Scholar
Rouder, J. N., Morey, R. D., & Wagenmakers, E.-J. (2016). The interplay between subjectivity, statistical practice, and psychological science. Collabra, 2, 112. https://doi.org/10.1525/collabra.28Google Scholar
Rouder, J. N., Schnuerch, M., Haaf, J. M., & Morey, R. D. (2022). Principles of model specification in ANOVA designs. Computational Brain & Behavior. https://doi.org/10.1007/s42113-022-00132-7Google Scholar
Rouder, J. N., Speckman, P. L., Sun, D., Morey, R. D., & Iverson, G. (2009). Bayesian t tests for accepting and rejecting the null hypothesis. Psychonomic Bulletin & Review, 16, 225237. https://doi.org/10.3758/PBR.16.2.225Google Scholar
Sackett, P. R. & Larson, J. Jr. R. (1990). Research strategies and tactics in industrial and organizational psychology. In M. D. Dunnette & L. M. Hough (eds.), Handbook of Industrial and Organizational Psychology, Volume 1, 2nd ed. (pp. 419489). Consulting Psychologists Press.Google Scholar
Sagarin, B. J., Ambler, J. K., & Lee, E. M. (2014). An ethical approach to peeking at data. Perspectives on Psychological Science, 9(3), 293304. https://doi.org/10.1177/1745691614528214CrossRefGoogle ScholarPubMed
Scheel, A. M., Schijen, M. R. M. J., & Lakens, D. (2021). An excess of positive results: Comparing the standard psychology literature with registered reports. Advances in Methods and Practices in Psychological Science, 4(2), 251524592110074. https://doi.org/10.1177/25152459211007467Google Scholar
Schimmack, U. (2020). A meta-psychological perspective on the decade of replication failures in social psychology. Canadian Psychology/Psychologie Canadienne, 61(4), 364376. https://doi.org/10.1037/cap0000246CrossRefGoogle Scholar
Schnuerch, M. & Erdfelder, E. (2020). Controlling decision errors with minimal costs: The sequential probability ratio t test. Psychological Methods, 25(2), 206226. https://doi.org/10.1037/met0000234Google Scholar
Schnuerch, M., Erdfelder, E., & Heck, D. W. (2020). Sequential hypothesis tests for multinomial processing tree models. Journal of Mathematical Psychology, 95, 102326. https://doi.org/10.1016/j.jmp.2020.102326Google Scholar
Schönbrodt, F. D. & Stefan, A. M. (2019). BFDA: An R package for Bayes factor design analysis (version 0.5.0) [Manual]. Available at: https://github.com/nicebread/BFDA.Google Scholar
Schönbrodt, F. D., Wagenmakers, E.-J., Zehetleitner, M., & Perugini, M. (2017). Sequential hypothesis testing with Bayes factors: Efficiently testing mean differences. Psychological Methods, 22(2), 322339. https://doi.org/10.1037/met0000061CrossRefGoogle ScholarPubMed
Schram, A. (2005). Artificiality: The tension between internal and external validity in economic experiments. Journal of Economic Methodology, 12(2), 225237. https://doi.org/10.1080/13501780500086081Google 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), 13591366. https://doi.org/10.1177/0956797611417632Google Scholar
Ulrich, R., Miller, J., & Erdfelder, E. (2018). Effect size estimation from t-statistics in the presence of publication bias: A brief review of existing approaches with some extensions. Zeitschrift für Psychologie, 226, 5680. https://doi.org/10.1027/2151-2604/a000319Google Scholar
Vanpaemel, W. (2010). Prior sensitivity in theory testing: An apologia for the Bayes factor. Journal of Mathematical Psychology, 54(6), 491498. https://doi.org/10.1016/j.jmp.2010.07.003CrossRefGoogle Scholar
Vanpaemel, W. & Lee, M. D. (2012). Using priors to formalize theory: Optimal attention and the generalized context model. Psychonomic Bulletin & Review, 19(6), 10471056. https://doi.org/10.3758/s13423-012-0300-4Google Scholar
Wagenmakers, E.-J. (2007). A practical solution to the pervasive problems of p values. Psychonomic Bulletin & Review, 14, 779804. https://doi.org/10.3758/BF03194105Google Scholar
Wagenmakers, E.-J., Wetzels, R., Borsboom, D., van der Maas, H. L. J., & Kievit, R. A. (2012). An agenda for purely confirmatory research. Perspectives on Psychological Science, 7(6), 632638. https://doi.org/10.1177/1745691612463078Google Scholar
Wald, A. (1947). Sequential Analysis. Wiley.Google Scholar
Wetherill, G. B. (1975). Sequential Methods in Statistics, 2nd ed. Chapman and Hall.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×