Author: neurotroph

  • Creating commandline tools using R and optparse

    Command line tools are powerful for automating your analysis or predictive models. optparse makes it easy to use command line arguments to make your script adaptable to changing configurations.

  • How to do stepwise regression in R?

    You don’t.

  • Podcasts for Data Science Start-Ups

    Podcasts for Data Science Start-Ups

    I am not a Podcasts person. Most episodes are too long, there is a lot of nonsense talk with inside jokes, and I usually find information quicker when googling on my own. As the past months have been quite busy, however, I was looking to fill the time where I couldn’t read or do other […]

  • From Psychologist to Data Scientist

    Social scientist or psychologist interested in data science? I compiled a list of skills and resources, I think are relevant in order to start in the field.

  • Leaving Academia: Goodbye, cruel world!

    Leaving Academia: Goodbye, cruel world!

    In September, my contract as a research assistant at the University of Bonn ended. I was lucky to have a 50% contract for three years and even more lucky that I had the option to extend the contract for another year. Nevertheless, I will leave academia as I’m close to finishing by PhD thesis and […]

  • Why “Prestige” is Better Than Your h-Index

    Why “Prestige” is Better Than Your h-Index

    Psychological science is one of the fields that is undergoing drastic changes in how we think about research, conduct studies and evaluate previous findings. Most notably, many studies from well-known researchers are under increased scrutiny. Recently, journalists and researchers have reviewed the Stanford Prison Experiment that is closely associated with the name of Philip Zimbardo. […]

  • Submission Criteria for Psychological Science

    Daniël told me about this the other day: Our recent pre-print on informative ‘null effects’ is now cited in the submission criteria for Psychological Science in a paragraph on drawing inferences from ‘theoretically significant’ results that may not be ‘statistically significant’. I feel very honoured that the editorial board at PS considers our manuscript as […]

  • New Preprint: Making “Null Effects” Informative

    New Preprint: Making “Null Effects” Informative

    In February and March this year, I stayed at the Eindhoven Technical University in the amazing group with Daniël Lakens, Anne Scheel and Peder Isager, who are actively researching questions of replicability in psychological science. Over the two months I have learned a lot, exchanged some great ideas with the three of them – and […]

  • Workshop “Einführung in die Datenanalyse mit R” (Post and Slides in German)

    Last weekend, I gave a 1.5 day workshop for students at my university on data analysis using R. In this post I briefly share my experience along with the workshop slides and an example project – both of which are in German. If you are looking for an English introduction into R, have a look […]

  • Update on the Replication Bayes Factor

    In December I already blogged about the ReplicationBF package, I made available on GitHub. It allows you to calculate Replication Bayes Factors for t- and F-tests. The preprint detailing the formulas for the latter was outdated and the method in the package was not optimal, so I recently updated both.