Neuroscience, addiction, development, genetics, and statistics
In our previous post in this series, we (David and Tayler) shared how we navigated the process of securing postdocs in the same city. Here, we’ll talk about how we pulled it off a second time, landing two tenure-track assistant professor positions at the same university (we’re moving to the Medical College of Wisconsin!). In particular, this post will be focused on things we did specific to our…
I enjoy making data visualizations. Here are some examples of my work. All were done in R.
A running list of some of my favorite online statistics resources including blog posts, visualizations, interactive simulations, and text books. Blog posts cheat sheets Common statistical tests are linear models: A really clear explanation of how many common statistical tests can be conceptualized as types of linear regressions. An Introduction to Hierarchical Modeling: Continue reading Favorite…
In an interaction analysis, the probability of a false-positive result increases as the correlation between our covariate and predictor increases, and as the effect of our covariate x predictor interaction increases. The extremely simple solution is to include all covariate-by-predictor interactions in your model! Continue reading Interaction analyses Appropriately adjusting for control variables…
These projects will help us understand how covid-19 is reshaping the world, and will help health providers address this new reality. Continue reading Researchers want you to tell them how the pandemic has affected your mental health
Clustered data - such as multiple observations per individual, animal, or cell - are quite common in neuroscience research. Here I walk through an introduction to one approach to adjusting your analyses for clustering - block permutations and bootstrapping - that is widely applicable and makes very few assumptions. Continue reading A quick intro to block permutations and bootstraps for analyzing…
Here is my analysis of 'what correlates with how well someone climbs?'. Continue reading Correlates of rock climbing ability
This is a short post on how to quickly get started using Turbo with your ggplots. Continue reading Using the new Turbo palette from Google in R
If my sample is just barely large enough to detect my main effect, the smallest interaction that I can reasonably expect is a knockout effect. Continue reading Interaction analyses How large a sample do I need? (part 3)
For an interaction effect size bXM, the difference between the simple-slopes of the top and bottom quartiles Q will be approximately: bXM * (ln(Q) +1) Continue reading Interaction analyses Interpreting effect sizes (part 2)