Privacy/Publication/Research

Das Privacy Paradox aus psychologischer Perspektive

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Woman reading Book

Ende 2019 erschien der Beitrag “Das Privacy Paradox aus psychologischer Perspektive” im Handbuch Datenrecht und Digitalisierung. In dem Beitrag analysiere ich das Privacy Paradox und erkläre, warum Menschen online viele Informationen teilen, obwohl sie sich sehr um ihre Privatsphäre sorgen.

Freundlicherweise hat mir der Erich Schmidt Verlag erlaubt, 12 Monate nach Erscheinen einen Preprint des Beitrages zu veröffentlichen.

Das ist nun geschehen: Der Preprint ist ab sofort auf socarxiv kostenfrei zum Download verfügbar!

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General/open science

Open science and qualitative research: Yes, we can do this!

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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!

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Methods

What is statistical power? An illustration using simulated data

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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.



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open science

A list of publicly available open datasets

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Photo with sign "open"
Photo by Artem Bali from Pexels

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).

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