(What to do) When Predictors Co-Vary
Aug 11, 2020
Co-varying predictors can be a messy business. They make estimates unstable, reducing our statistical power and making interpretation more difficult. In this post I will demonstrate how ignoring the presence of co-variation between predictors when exploring our models can lead to odd results and how we might deal with this issue.

Estimating and testing GLMs with `emmeans`
Apr 13, 2020
This post was written in collaboration with Almog Simchon (@almogsi) and Shachar Hochman (@HochmanShachar). Go follow them.
The fictional simplicity of Generalized Linear Models
Who doesn’t love GLMs? The ingenious idea of taking a response level variable (e.
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The Mysterious Case of the Ghost Interaction
Oct 30, 2019
This spooky post was written in collaboration with Yoav Kessler (@yoav_kessler) and Naama Yavor (@namivor)..
Experimental psychology is moving away from repeated-measures-ANOVAs, and towards linear mixed models (LMM1). LMMs have many advantages over rmANOVA, including (but not limited to):
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Accounting for Within- AND Between-Subject Effects
Oct 21, 2019
This is a comment on Solomon Kurz’s (@SolomonKurz) recent post1 where he discusses how group-level data does not always reflect individual-level processes. I highly recommend reading his series of posts on the topic!
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(Bootstrapping) Follow-Up Contrasts for Within-Subject ANOVAs (part 2)
Aug 14, 2019
A while back I wrote a post demonstrating how to *bootstrap* follow-up contrasts for repeated-measure ANOVAs. Here is a demo of how to conduct the same bootstrap analysis, more simply.

Signal Detection Theory vs. Logistic Regression
Jul 29, 2019
I recently came across a paper that explained the equality between the parameters of signal detection theory (SDT) and the parameters of logistic regression in which the state (“absent”/“present”) is used to predict the response (“yes”/“no”, but also applicable in scale-rating designs) (DeCarlo, 1998).
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(Bootstrapping) Follow-Up Contrasts for Within-Subject ANOVAs
May 9, 2019
So you’ve run a repeated-measures ANOVA and found that your residuals are neither normally distributed, nor homogeneous, or that you are in violation of any other assumptions. Naturally you want to run some a-parametric analysis… but how?