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  • Alexander Etz
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.

Subject:
Applied Science
Life Science
Physical Science
Social Science
Material Type:
Reading
Author:
Alexander Etz
Amy Orben
Hannah Moshontz
Jesse Niebaum
Johnny van Doorn
Matthew Makel
Michael Schulte-Mecklenbeck
Sam Parsons
Sophia Crüwell
Date Added:
08/12/2019
A Bayesian Perspective on the Reproducibility Project: Psychology
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CC BY
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We revisit the results of the recent Reproducibility Project: Psychology by the Open Science Collaboration. We compute Bayes factors—a quantity that can be used to express comparative evidence for an hypothesis but also for the null hypothesis—for a large subset (N = 72) of the original papers and their corresponding replication attempts. In our computation, we take into account the likely scenario that publication bias had distorted the originally published results. Overall, 75% of studies gave qualitatively similar results in terms of the amount of evidence provided. However, the evidence was often weak (i.e., Bayes factor < 10). The majority of the studies (64%) did not provide strong evidence for either the null or the alternative hypothesis in either the original or the replication, and no replication attempts provided strong evidence in favor of the null. In all cases where the original paper provided strong evidence but the replication did not (15%), the sample size in the replication was smaller than the original. Where the replication provided strong evidence but the original did not (10%), the replication sample size was larger. We conclude that the apparent failure of the Reproducibility Project to replicate many target effects can be adequately explained by overestimation of effect sizes (or overestimation of evidence against the null hypothesis) due to small sample sizes and publication bias in the psychological literature. We further conclude that traditional sample sizes are insufficient and that a more widespread adoption of Bayesian methods is desirable.

Subject:
Psychology
Material Type:
Reading
Provider:
PLOS ONE
Author:
Alexander Etz
Joachim Vandekerckhove
Date Added:
08/07/2020
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 (http://www.jasp-stats.org), 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.

Subject:
Psychology
Material Type:
Reading
Provider:
Psychonomic Bulletin & Review
Author:
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
Date Added:
08/07/2020
Robust Modeling in Cognitive Science
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CC BY-NC-ND
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In an attempt to increase the reliability of empirical findings, psychological scientists have recently proposed a number of changes in the practice of experimental psychology. Most current reform efforts have focused on the analysis of data and the reporting of findings for empirical studies. However, a large contingent of psychologists build models that explain psychological processes and test psychological theories using formal psychological models. Some, but not all, recommendations borne out of the broader reform movement bear upon the practice of behavioral or cognitive modeling. In this article, we consider which aspects of the current reform movement are relevant to psychological modelers, and we propose a number of techniques and practices aimed at making psychological modeling more transparent, trusted, and robust.

Subject:
Social Science
Material Type:
Primary Source
Author:
Alexander Etz
Amy H. Criss
Berna Devezer
Christopher Donkin
Corey N. White
Dora Matzke
Fabio P. Leite
Jeffrey N. Rouder
Jennifer S. Trueblood
Joachim Vandekerckhove
Michael D. Lee
Date Added:
11/13/2020