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  • Johnny van Doorn
7 Easy Steps to Open Science: An Annotated Reading List
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The Open Science movement is rapidly changing the scientific landscape. Because exact definitions are often lacking and reforms are constantly evolving, accessible guides to open science are needed. This paper provides an introduction to open science and related reforms in the form of an annotated reading list of seven peer-reviewed articles, following the format of Etz et al. (2018). Written for researchers and students - particularly in psychological science - it highlights and introduces seven topics: understanding open science; open access; open data, materials, and code; reproducible analyses; preregistration and registered reports; replication research; and teaching open science. For each topic, we provide a detailed summary of one particularly informative and actionable article and suggest several further resources. Supporting a broader understanding of open science issues, this overview should enable researchers to engage with, improve, and implement current open, transparent, reproducible, replicable, and cumulative scientific practices.

Applied Science
Life Science
Physical Science
Social Science
Material Type:
Alexander Etz
Amy Orben
Hannah Moshontz
Jesse Niebaum
Johnny van Doorn
Matthew Makel
Michael Schulte-Mecklenbeck
Sam Parsons
Sophia Crüwell
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Bayesian inference for psychology. Part II: Example applications with JASP
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Bayesian hypothesis testing presents an attractive alternative to p value hypothesis testing. Part I of this series outlined several advantages of Bayesian hypothesis testing, including the ability to quantify evidence and the ability to monitor and update this evidence as data come in, without the need to know the intention with which the data were collected. Despite these and other practical advantages, Bayesian hypothesis tests are still reported relatively rarely. An important impediment to the widespread adoption of Bayesian tests is arguably the lack of user-friendly software for the run-of-the-mill statistical problems that confront psychologists for the analysis of almost every experiment: the t-test, ANOVA, correlation, regression, and contingency tables. In Part II of this series we introduce JASP (, an open-source, cross-platform, user-friendly graphical software package that allows users to carry out Bayesian hypothesis tests for standard statistical problems. JASP is based in part on the Bayesian analyses implemented in Morey and Rouder’s BayesFactor package for R. Armed with JASP, the practical advantages of Bayesian hypothesis testing are only a mouse click away.

Material Type:
Psychonomic Bulletin & Review
Akash Raj
Alexander Etz
Alexander Ly
Alexandra Sarafoglou
Bruno Boutin
Damian Dropmann
Don van den Bergh
Dora Matzke
Eric-Jan Wagenmakers
Erik-Jan van Kesteren
Frans Meerhoff
Helen Steingroever
Jeffrey N. Rouder
Johnny van Doorn
Jonathon Love
Josine Verhagen
Koen Derks
Maarten Marsman
Martin Šmíra
Patrick Knight
Quentin F. Gronau
Ravi Selker
Richard D. Morey
Sacha Epskamp
Tahira Jamil
Tim de Jong
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