Accelerating Psychological Science with Large-Scale Collaborations

Science is the collaborative attempt to understand ourselves and the world around us better by gathering and evaluating evidence. Ironically enough, we are pretty bad at evaluating evidence. Luckily, others rejoice in pointing out our flaws. It is this reciprocal corrective process which is at the core of science, and the reason why it works so well. Working collaboratively helps us catch and correct each other’s mistakes.
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Fabian Dablander

Fabian Dablander just finished his Masters in Cognitive Science at the University of Tübingen. He is interested in innovative ways of data collection, Bayesian statistics, open science, and effective altruism. You can find him on Twitter @fdabl.

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“Bullied Into Bad Science”: An Interview with Corina Logan

The last two years have seen a lot of talk about the issues of science and scientific publishing – and how the incentives prevalent in science (publish or perish, preferably with high-impact stories with lots of news coverage) are actually bad for science. Corina Logan, a zoologist and part of a group of postdocs from the University of Cambridge is eager to push for a change in the publishing culture. They argue that the current way of publishing is hindering the progress of science. A recent column by Brian Martinson in Nature summarises the problem nicely: “[The fact that researchers need publications encourages] all manner of corner-cutting, sloppiness in research, and other degradations in the quality of publications, not to mention an obvious motive for plagiarism. A quest for high-profile papers leads researchers to favour a spectacular result, even if it is specious. Authors cite themselves to boost the impact of publications, and cite colleagues to curry favour.” Continue reading

Katharina Brecht

Katharina Brecht

After finishing her PhD at the University of Cambridge, Katharina is currently a Postdoc in the Institute of Neurobiology at the University of Tübingen. Her research interests revolve around the mechanisms of social and causal cognition in animals.

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A conceptual introduction to mathematical modeling of cognition

Psychological researchers try to understand how the mind works. That is, they describe observable phenomena, try to induce explanatory theories, and use those theories to deduce predictions. The explanatory value of a theory is then assessed by comparing theoretical predictions to new observations. Continue reading

Frederik Aust

Frederik Aust

Frederik Aust is pursuing a PhD in cognitive psychology at the University of Cologne. He is interested in mathematical models of memory and cognition, open science, and R programming.

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How to stop being busy and become productive

With the rise of social media, potential distractions have risen to unseen levels; they dominate our daily lives. Do you check Facebook, Twitter, Snapchat, Instagram, or Email on a constant basis? Do you have an embarrassing relationship with your alarm clock’s snooze button? Do you pass on social invites, telling other people that you are too busy? As a generation, we have lost the ability to focus sharply on the task at hand; instead, we work on a multitude of things simultaneously, lamenting that we do not achieve what we seek to achieve. Continue reading

Fabian Dablander

Fabian Dablander just finished his Masters in Cognitive Science at the University of Tübingen. He is interested in innovative ways of data collection, Bayesian statistics, open science, and effective altruism. You can find him on Twitter @fdabl.

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Are You Registering That? An Interview with Prof. Chris Chambers

There is no panacea for bad science, but if there were, it would certainly resemble Registered Reports. Registered Reports are a novel publishing format in which authors submit only the introduction, methods, and planned analyses without actually having collected the data. Thus, peer-review only focuses on the soundness of the research proposal and is not contingent on the “significance” of the results (Chambers, 2013). In one strike, this simple idea combats publication bias, researchers’ degrees of freedom, makes apparent the distinction between exploratory and confirmatory research, and calms the researcher’s mind. There are a number of journals offering Registered Reports, and this is arguable the most important step journals can take to push psychological science forward (see also King et al., 2016). For a detailed treatment of Registered Reports, see here, here, here, and Chambers (2015). Continue reading

Fabian Dablander

Fabian Dablander just finished his Masters in Cognitive Science at the University of Tübingen. He is interested in innovative ways of data collection, Bayesian statistics, open science, and effective altruism. You can find him on Twitter @fdabl.

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Introducing jamovi: Free and Open Statistical Software Combining Ease of Use with the Power of R

For too long, Psychology has had to put up with costly, bulky, and inflexible statistics software. Today, we’d like to introduce you to a breath of fresh air: jamovi, free statistics software available for all platforms that is intuitive and user-friendly, and developed with so much pace that its capabilities will potentially soon outrun SPSS. Continue reading

Peter Edelsbrunner

Peter Edelsbrunner

Peter is currently doctoral student at the section for learning and instruction research of ETH Zurich in Switzerland. He graduated from Psychology at the University of Graz in Austria. Peter is interested in conceptual knowledge development and the application of flexible mixture models to developmental research. Since 2011 he has been active in the EFPSA European Summer School and related activities.

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Magical 7±2 Tips for Psychologists Participating in a Hackathon

A hackathon is an event, typically lasting for 24-48 hours, in which a group of people with diverse backgrounds come together to solve a problem by building a first working prototype of a solution (usually a web app, program or a utility).

There is something inherently likable, or dare I say, smart, about hackathons. They have a specific goal, your progress and results are measurable, getting a first working prototype is both achievable and realistic, and it will all be over in 24-48 hours. I have come to appreciate hackathons a lot over the last five months where I’ve participated in five, and won two of them with my teams. I would like to invite you to participate in one as well by giving you 7±2 tips to make your hackathon experience especially enjoyable. Continue reading

Taavi Kivisik

Data scientist and developer at Qlouder. While at the University of Tartu and University of Toronto, I was inspired to learn more about efficient learning and mnemonics. Midway through the studies I discovered my passion for research methodology and technical side of research, statistics and programming, also machine learning. I’m volunteering as a Lead Archivist for the Nordic Psychology Students’ Conference (NPSC). I'm former President of the Estonian Psychology Students’ Association and former Junior Editor at the Journal of European Psychology Students’ (JEPS). I sometimes tweet @tkivisik .

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Open online education: Research findings and methodological challenges

With a reliable internet connection comes access to the enormous World Wide Web. Being so large, we rely on tools like Google to search and filter all this information. Additional filters can be found in sites like Wikipedia, offering a library style access to curated knowledge, but it too is enormous. In more recent years, open online courses has rapidly become a highly popular method of gaining easy access to curated, high quality, as well as pre-packaged knowledge. A particularly popular variety is the Massive Open Online Course, or MOOC, which are found on platforms like Coursera and edX. The promise – global and free access to high quality education – has often been applauded. Some have heralded the age of the MOOC as the death of campus based teaching. Others are more critical, often citing the high drop-out rates as a sign of failure, or argue that MOOCs do not or cannot foster ‘real’ learning (e.g., Zemsky, 2014; Pope, 2014). Continue reading

Tim van der Zee

Skeptical scientist. I study how people learn from educational videos in open online courses, and how we can help them learn better. PhD student at Leiden University (the Netherlands), but currently a visiting scholar at MIT and UMass Lowell. You can follow me on Twitter: @Research_Tim and read my blog at www.timvanderzee.com

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Introduction to Data Analysis using R

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R is a statistical programming language whose popularity is quickly overtaking SPSS and other “traditional” point-and-click software packages (Muenchen, 2015). But why would anyone use a programming language, instead of point-and-click applications, for data analysis? An important reason is that data analysis rarely consists of simply running a statistical test. Instead, many small steps, such as cleaning and visualizing data, are usually repeated many times, and computers are much faster at doing repetitive tasks than humans are. Using a point-and-click interface for these “data cleaning” operations is laborious and unnecessarily slow: Continue reading

Matti Vuorre

Matti Vuorre

Matti Vuorre is a PhD Student at Columbia University in New York City. He studies cognitive psychology and neuroscience, and focuses on understanding the mechanisms underlying humans' metacognitive capacities.

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Not solely about that Bayes: Interview with Prof. Eric-Jan Wagenmakers

Last summer saw the publication of the most important work in psychology in decades: the Reproducibility Project (Open Science Collaboration, 2015; see here and here for context). It stirred up the community, resulting in many constructive discussions but also in verbally violent disagreement. What unites all parties, however, is the call for more transparency and openness in research.

Eric-Jan “EJ” Wagenmakers has argued for pre-registration of research (Wagenmakers et al., 2012; see also here) and direct replications (e.g., Boekel et al., 2015; Wagenmakers et al., 2015), for a clearer demarcation of exploratory and confirmatory research (de Groot, 1954/2013), and for a change in the way we analyze our data (Wagenmakers et al., 2011; Wagenmakers et al., in press). Continue reading

Fabian Dablander

Fabian Dablander just finished his Masters in Cognitive Science at the University of Tübingen. He is interested in innovative ways of data collection, Bayesian statistics, open science, and effective altruism. You can find him on Twitter @fdabl.

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