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 of different statistical frameworks (e.g., hypothesis testing, cognitive models, IRT, SEM, etc.). We also describe take-home messages and provide "big-picture" recommendations to the field as Bayesian statistics becomes more popular. Our review indicated that Bayesian statistics is used in a variety of contexts across subfields of psychology and related disciplines. There are many different reasons why one might choose to use Bayes (e.g., the use of priors, estimating otherwise intractable models, modeling uncertainty, etc.). We found in this review that the use of Bayes has increased and broadened in the sense that this methodology can be used in a flexible manner to tackle many different forms of questions. We hope this presentation opens the door for a larger discussion regarding the current state of Bayesian statistics, as well as future trends.

 

Translational abstract

Over 250 years ago, Bayes (or Price, or Laplace) introduced a method to take prior knowledge into account in data analysis. Although these ideas and Bayes’s theorem have been longstanding within the fields of mathematics and statistics, these tools have not been at the forefront of modern-day applied psychological research. It was frequentist statistics (i.e., p values and null hypothesis testing; developed by Fisher, Neyman, and Pearson long after Bayes’s theorem), which has dominated the field of Psychology throughout the 21st century. However, it is often claimed by ‘Bayesians’ 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). Our findings showed that there was some merit in this thought. In fact, the use of Bayesian methods in applied Psychological work has steadily increased since the nineties and is currently taking flight. It was clear in this review that Bayesian statistics is used in a variety of contexts across subfields of Psychology and related disciplines. This is an exciting time, where we can watch the field of applied statistics change more than ever before. The way in which researchers think about and answer substantive inquiries is slowly taking on a new philosophical meaning that now incorporates previous knowledge and opinions into the estimation process. We hope this presentation opens the door for a larger discussion regarding the current state of Bayesian statistics, as well as future trends.

Van de Schoot, R., Winter, S., Ryan, O., Zondervan-Zwijnenburg, M., & Depaoli, S. (in press). A Systematic Review of Bayesian Papers in Psychology: The Last 25 Years. Psychological Methods, 22(2), 217-239. http://dx.doi.org/10.1037/met000010

Former team member

After working with Rens on various research projects related to Bayesian Estimation and latent growth modeling I developed an interest in researching both of these further.

 
PhD Student

In her PhD project, Mariëlle focuses on including prior knowledge in statistical analyses (informative Bayesian research) and confronting prior knowledge with new data.

 
Sarah Depaoli
Assistant Professor at the University of California, Merced
Sarah’s research interests are largely focused on issues surrounding Bayesian estimation of latent variable models. She has a particular interest in estimation issues arising from nonlinear growth patterns over time. She is also interested in improving accuracy of uncovering unobserved (latent) groups of individuals. She is currently working with several students that are involved in research spanning a wide range of methodological topics .
Visit website