Economics at your fingertips  

Bayesian Reanalyses from Summary Statistics: A Guide for Academic Consumers

Alexander Ly, Akash Raj, Alexander Etz, Maarten Marsman, Quentin Frederik Gronau and Eric-Jan Wagenmakers
Additional contact information
Akash Raj: University of Amsterdam
Eric-Jan Wagenmakers: University of Amsterdam

No 7dzmk, OSF Preprints from Center for Open Science

Abstract: Across the social sciences, researchers have overwhelmingly used the classical statistical paradigm to draw conclusions from data, often focusing heavily on a single number: p. Recent years, however, have witnessed a surge of interest in an alternative statistical paradigm: Bayesian inference, in which probabilities are attached to parameters and models. We feel it is informative to provide statistical conclusions that go beyond a single number, and --regardless of one's statistical preference-- it can be prudent to report the results from both the classical and the Bayesian paradigm. In order to promote a more inclusive and insightful approach to statistical inference we show how the open-source software program JASP ( provides a set of comprehensive Bayesian reanalyses from just a few commonly-reported summary statistics such as t and N. These Bayesian reanalyses allow researchers --and also editors, reviewers, readers, and reporters-- to quantify evidence on a continuous scale, assess the robustness of that evidence to changes in the prior distribution, and gauge which posterior parameter ranges are more credible than others. The procedure is illustrated using the seminal Festinger and Carlsmith (1959) study on cognitive dissonance.

Date: 2017-06-02
References: View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

DOI: 10.31219/

Access Statistics for this paper

More papers in OSF Preprints from Center for Open Science
Bibliographic data for series maintained by OSF ().

Page updated 2020-01-27
Handle: RePEc:osf:osfxxx:7dzmk