I’m currently at the 70th Annual Conference of the International Communication Assocation, where this year’s theme is Open Communication.
One ongoing discussion is how open science works for research coming from a more qualitative perspective.
Because of COVID-19 the conference is all digital, which is why I’m tweeting even more than usual (sorry ’bout that). In what follows, you can find the transcript of a tweet that was particular long, so that you can read it more easily. Here we go!
Vor einem Jahr hatte ich die Freude, eine Woche lang meine Forschung bei “Real Scientist” vorzustellen. Bei Real Scientist geht es darum, die eigene Forschung einer breiten Öffentlichkeit auf Twitter niederschwellig näher zu bringen. Aktuell hat der Twitter-Kanal über 8.000 Abonnenten, die rege mitlesen und kommentieren.
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Countless definitions of privacy exist. Arguably, the most prominent one was offered by Alan Westin. It goes as follows:
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I like WordPress and I love Rmarkdown. So recently I’ve thought: With Rmarkdown I can easily produce HTMLs of analyses, research reports, or tutorials. My blog runs on WordPress. So instead of manually copying everything over to WordPress, wouldn’t it be nice if I could automatically upload my Rmarkdown scripts to WordPress … ?
In Germany we have the following saying: Everything has already been said – but not yet by everyone. For what it's worth, in what follows I take my own turn and try to explain the concept of statistical power using simple words, simulations of data, and some gifs.
Note that this post is written primarily for students in order to provide some guidelines for empirical theses or reports. I provide the R-code necessary for the analyses, and you can also download everything from my github.
It is becoming increasingly clear that we as researchers need to analyze large-scale, publicly available open datasets. The replication crisis in psychology vividly illustrated that most studies are strongly underpowered and that results are not replicable (e.g., Munafo et al., 2017). Besides, and maybe less well-known, small samples also increase the probability of finding extreme results, which leads to a literature full of effect sizes that are artificially inflated (e.g., Button et al., 2013).
I’ve recently written a thread on Twitter that, at least by my standards, got pretty viral. In the thread I discuss the topic of effect sizes. Specifically, I look at how many hours you need to spend on Facebook so that it actually affects your well-being. Some people said they’d like to cite it, which is why I’m now also posting it here.
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Recently, there has been a lot of talk about the effects of smartphones on mental health. For example, one was able to read the following:
Continue reading →Rates of teen depression and suicide have skyrocketed since 2011. It’s not an exaggeration to describe iGen as being on the brink of the worst mental-health crisis in decades. Much of this deterioration can be traced to their phones.
Jean Twenge
