publication . Article . Other literature type . 2017

A systematic review of Bayesian articles in psychology: The last 25 years.

van de Schoot, Rens; Winter, Sonja D.; Ryan, Oisín; Zondervan-Zwijnenburg, Mariëlle; Depaoli, Sarah;
Open Access
  • Published: 01 Jun 2017 Journal: Psychological Methods, volume 22, pages 217-239 (issn: 1082-989X, eissn: 1939-1463, Copyright policy)
  • Publisher: American Psychological Association (APA)
  • Country: Netherlands
Abstract
Although the statistical tools most often used by researchers in the field of psychology over the last 25 years are based on frequentist statistics, it is often claimed that the alternative Bayesian approach to statistics is gaining in popularity. In the current article, we investigated this claim by performing the very first systematic review of Bayesian psychological articles published between 1990 and 2015 (n = 1,579). We aim to provide a thorough presentation of the role Bayesian statistics plays in psychology. This historical assessment allows us to identify trends and see how Bayesian methods have been integrated into psychological research in the context ...
Subjects
free text keywords: Bayesian probability, Frequentist inference, Prior probability, Bayes' theorem, Econometrics, Bayesian statistics, Statistics, PsycINFO, Statistical hypothesis testing, Psychology, Frequentist probability, prior, posterior, MCMC-methods, Bayes's theorem, systematic review, Taverne
66 references, page 1 of 5

Ahn, W. Y., Vasilev, G., Lee, S. H., Busemeyer, J. R., Kruschke, J. K., Bechara, A., & Vassileva, J. (2014). Decision-making in stimulant and opiate addicts in protracted abstinence: Evidence from computational modeling with pure users. Frontiers in Psychology, 5, 849.

Akaike, H. (1981). Likelihood of a model and information criteria. [OpenAIRE]

Journal of Econometrics, 16, 3-14. http://dx.doi.org/10.1016/0304- 4076(81)90071-3 Albert, J. H. (1992). Bayesian estimation of normal ogive item response curves using Gibbs sampling. Journal of Educational Statistics, 17, 251-269. http://dx.doi.org/10.3102/10769986017003251 Albert, M. K. (2000). The generic viewpoint assumption and Bayesian inference. Perception, 29, 601- 608. http://dx.doi.org/10.1068/p3050 Allen, J. J. B., & Iacono, W. G. (1997). A comparison of methods for the analysis of event-related potentials in deception detection. Psychophysiology, 34, 234 -240. http://dx.doi.org/10.1111/j.1469-8986.1997 .tb02137.x Almond, R. G., DiBello, L. V., Moulder, B., & Zapata-Rivera, J. D. (2007).

Modeling diagnostic assessments with Bayesian networks. Journal of Educational Measurement, 44, 341-359. http://dx.doi.org/10.1111/j .1745-3984.2007.00043.x Andrews, M., Vigliocco, G., & Vinson, D. (2009). Integrating experiential and distributional data to learn semantic representations. Psychological Review, 116, 463- 498. http://dx.doi.org/10.1037/a0016261 Annis, J., Lenes, J. G., Westfall, H. A., Criss, A. H., & Malmberg, K. J.

(2015). The list-length effect does not discriminate between models of recognition memory. Journal of Memory and Language, 85, 27- 41.

http://dx.doi.org/10.1016/j.jml.2015.06.001 Arminger, G., & Muthén, B. O. (1998). A Bayesian approach to nonlinear latent variable models using the Gibbs sampler and the metropolishastings algorithm. Psychometrika, 63, 271-300. http://dx.doi.org/10 .1007/BF02294856 Asendorpf, J. B., Conner, M., De Fruyt, F., De Houwer, J., Denissen, J. J., Fiedler, K., . . . Wicherts, J. M. (2013). Recommendations for increasing replicability in psychology. European Journal of Personality, 27, 108 - 119. http://dx.doi.org/10.1002/per.1919 Ashby, D. (2006). Bayesian statistics in medicine: A 25 year review.

Frontiers in Psychology, 5, 984.

Bulbulia, J. A., Xygalatas, D., Schjoedt, U., Fondevila, S., Sibley, C. G., & Konvalinka, I. (2013). Images from a jointly-arousing collective ritual reveal affective polarization. Frontiers in Psychology, 4, 960. [OpenAIRE]

Cavanagh, J. F., Wiecki, T. V., Kochar, A., & Frank, M. J. (2014). Eye tracking and pupillometry are indicators of dissociable latent decision processes. Journal of Experimental Psychology: General, 143, 1476 - 1488. http://dx.doi.org/10.1037/a0035813 Chiorri, C., Day, T., & Malmberg, L. E. (2014). An approximate measurement invariance approach to within-couple relationship quality. Frontiers in Psychology, 5, 983.

Choi, J. Y., Koh, D., & Lee, J. (2008). Ex-ante simulation of mobile TV market based on consumers' preference data. Technological Forecasting and Social Change, 75, 1043-1053. http://dx.doi.org/10.1016/j.techfore .2007.10.001 Cieciuch, J., Davidov, E., Schmidt, P., Algesheimer, R., & Schwartz, S. H.

(2014). Comparing results of an exact vs. an approximate (Bayesian) measurement invariance test: A cross-country illustration with a scale to measure 19 human values. Frontiers in Psychology, 5, 982.

Cipora, K., & Nuerk, H. C. (2013). Is the SNARC effect related to the level of mathematics? No systematic relationship observed despite more power, more repetitions, and more direct assessment of arithmetic skill.

Freisthler, B., & Weiss, R. E. (2008). Using Bayesian space-time models to understand the substance use environment and risk for being referred to child protective services. Substance Use & Misuse, 43, 239 -251. [OpenAIRE]

http://dx.doi.org/10.1080/10826080701690649 Gajewski, B. J., Coffland, V., Boyle, D. K., Bott, M., Price, L. R., Leopold, J., & Dunton, N. (2012). Assessing content validity through correlation and relevance tools a Bayesian randomized equivalence experiment.

Methodology, 8, 81-96. http://dx.doi.org/10.1027/1614-2241/a000040 Gangemi, A., Mancini, F., & van den Hout, M. (2012). Behavior as information: “If I avoid, then there must be a danger.” Journal of Behavior Therapy and Experimental Psychiatry, 43, 1032-1038. http:// dx.doi.org/10.1016/j.jbtep.2012.04.005 Gao, F., & Chen, L. (2005). Bayesian or non-Bayesian: A comparison study of item parameter estimation in the three-parameter logistic model.

66 references, page 1 of 5
Abstract
Although the statistical tools most often used by researchers in the field of psychology over the last 25 years are based on frequentist statistics, it is often claimed that the alternative Bayesian approach to statistics is gaining in popularity. In the current article, we investigated this claim by performing the very first systematic review of Bayesian psychological articles published between 1990 and 2015 (n = 1,579). We aim to provide a thorough presentation of the role Bayesian statistics plays in psychology. This historical assessment allows us to identify trends and see how Bayesian methods have been integrated into psychological research in the context ...
Subjects
free text keywords: Bayesian probability, Frequentist inference, Prior probability, Bayes' theorem, Econometrics, Bayesian statistics, Statistics, PsycINFO, Statistical hypothesis testing, Psychology, Frequentist probability, prior, posterior, MCMC-methods, Bayes's theorem, systematic review, Taverne
66 references, page 1 of 5

Ahn, W. Y., Vasilev, G., Lee, S. H., Busemeyer, J. R., Kruschke, J. K., Bechara, A., & Vassileva, J. (2014). Decision-making in stimulant and opiate addicts in protracted abstinence: Evidence from computational modeling with pure users. Frontiers in Psychology, 5, 849.

Akaike, H. (1981). Likelihood of a model and information criteria. [OpenAIRE]

Journal of Econometrics, 16, 3-14. http://dx.doi.org/10.1016/0304- 4076(81)90071-3 Albert, J. H. (1992). Bayesian estimation of normal ogive item response curves using Gibbs sampling. Journal of Educational Statistics, 17, 251-269. http://dx.doi.org/10.3102/10769986017003251 Albert, M. K. (2000). The generic viewpoint assumption and Bayesian inference. Perception, 29, 601- 608. http://dx.doi.org/10.1068/p3050 Allen, J. J. B., & Iacono, W. G. (1997). A comparison of methods for the analysis of event-related potentials in deception detection. Psychophysiology, 34, 234 -240. http://dx.doi.org/10.1111/j.1469-8986.1997 .tb02137.x Almond, R. G., DiBello, L. V., Moulder, B., & Zapata-Rivera, J. D. (2007).

Modeling diagnostic assessments with Bayesian networks. Journal of Educational Measurement, 44, 341-359. http://dx.doi.org/10.1111/j .1745-3984.2007.00043.x Andrews, M., Vigliocco, G., & Vinson, D. (2009). Integrating experiential and distributional data to learn semantic representations. Psychological Review, 116, 463- 498. http://dx.doi.org/10.1037/a0016261 Annis, J., Lenes, J. G., Westfall, H. A., Criss, A. H., & Malmberg, K. J.

(2015). The list-length effect does not discriminate between models of recognition memory. Journal of Memory and Language, 85, 27- 41.

http://dx.doi.org/10.1016/j.jml.2015.06.001 Arminger, G., & Muthén, B. O. (1998). A Bayesian approach to nonlinear latent variable models using the Gibbs sampler and the metropolishastings algorithm. Psychometrika, 63, 271-300. http://dx.doi.org/10 .1007/BF02294856 Asendorpf, J. B., Conner, M., De Fruyt, F., De Houwer, J., Denissen, J. J., Fiedler, K., . . . Wicherts, J. M. (2013). Recommendations for increasing replicability in psychology. European Journal of Personality, 27, 108 - 119. http://dx.doi.org/10.1002/per.1919 Ashby, D. (2006). Bayesian statistics in medicine: A 25 year review.

Frontiers in Psychology, 5, 984.

Bulbulia, J. A., Xygalatas, D., Schjoedt, U., Fondevila, S., Sibley, C. G., & Konvalinka, I. (2013). Images from a jointly-arousing collective ritual reveal affective polarization. Frontiers in Psychology, 4, 960. [OpenAIRE]

Cavanagh, J. F., Wiecki, T. V., Kochar, A., & Frank, M. J. (2014). Eye tracking and pupillometry are indicators of dissociable latent decision processes. Journal of Experimental Psychology: General, 143, 1476 - 1488. http://dx.doi.org/10.1037/a0035813 Chiorri, C., Day, T., & Malmberg, L. E. (2014). An approximate measurement invariance approach to within-couple relationship quality. Frontiers in Psychology, 5, 983.

Choi, J. Y., Koh, D., & Lee, J. (2008). Ex-ante simulation of mobile TV market based on consumers' preference data. Technological Forecasting and Social Change, 75, 1043-1053. http://dx.doi.org/10.1016/j.techfore .2007.10.001 Cieciuch, J., Davidov, E., Schmidt, P., Algesheimer, R., & Schwartz, S. H.

(2014). Comparing results of an exact vs. an approximate (Bayesian) measurement invariance test: A cross-country illustration with a scale to measure 19 human values. Frontiers in Psychology, 5, 982.

Cipora, K., & Nuerk, H. C. (2013). Is the SNARC effect related to the level of mathematics? No systematic relationship observed despite more power, more repetitions, and more direct assessment of arithmetic skill.

Freisthler, B., & Weiss, R. E. (2008). Using Bayesian space-time models to understand the substance use environment and risk for being referred to child protective services. Substance Use & Misuse, 43, 239 -251. [OpenAIRE]

http://dx.doi.org/10.1080/10826080701690649 Gajewski, B. J., Coffland, V., Boyle, D. K., Bott, M., Price, L. R., Leopold, J., & Dunton, N. (2012). Assessing content validity through correlation and relevance tools a Bayesian randomized equivalence experiment.

Methodology, 8, 81-96. http://dx.doi.org/10.1027/1614-2241/a000040 Gangemi, A., Mancini, F., & van den Hout, M. (2012). Behavior as information: “If I avoid, then there must be a danger.” Journal of Behavior Therapy and Experimental Psychiatry, 43, 1032-1038. http:// dx.doi.org/10.1016/j.jbtep.2012.04.005 Gao, F., & Chen, L. (2005). Bayesian or non-Bayesian: A comparison study of item parameter estimation in the three-parameter logistic model.

66 references, page 1 of 5
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publication . Article . Other literature type . 2017

A systematic review of Bayesian articles in psychology: The last 25 years.

van de Schoot, Rens; Winter, Sonja D.; Ryan, Oisín; Zondervan-Zwijnenburg, Mariëlle; Depaoli, Sarah;