Stat’s What It’s All About

Ordinal Regression as a Model for Signal Detection

R

statistics

code

ordinal regression

signal detection theory

I was basically done with this blog post when I came across Matti Vuorre’s post on the same exact topic. Matti goes into all the details, and really the present post can be seen as a brief account of all the cool things the probit-approach-to-SDT can do. I’m…

ggplot: Easy as pie (charts)

R

ggplot2

_{This post by no means endorses the use of pie charts. But, if you must, here’s how…}

ggplot: plot only some of the data

R

ggplot2

Often (especially when working with large and/or rich datasets) our (gg)plots can feel cluttered with information. But they don’t have to be!

Everything You Always Wanted to Know About ANOVA*

(*But Were Afraid to Ask)

statistics

R

regression

ANOVA

interactions

linear models

code

glm

Analysis of variance (ANOVA) is a statistical procedure, developed by R. A. Fisher, used to analyze the relationship between a continuous outcome (dependent variable) and…

ggplot: the placing and order of aesthetics matters

R

ggplot2

A group of people were asked to what degree they agree or disagree with a statement at two time points.

Testing The Equality of Regression Coefficients

statistics

R

regression

emmeans

You have two predictors in your model. One seems to have a stronger coefficient than the other. But is it significant?

(What to do) When Predictors Co-Vary

statistics

R

emmeans

code

glm

multicollinearity

assumptions

Co-varying predictors can be a messy business. They make estimates unstable, reducing our statistical power and making interpretation more difficult. In…

Estimating and testing GLMs with `emmeans`

A follow up to McCabe et al., 2020.

R

statistics

code

glm

emmeans

_{This post was written in collaboration with Almog Simchon (@almogsi) and Shachar Hochman (@HochmanShachar). Go follow them.}

The Mysterious Case of the Ghost Interaction

R

statistics

ANOVA

code

linear mixed models

_{This spooky post was written in collaboration with Yoav Kessler (@yoav_kessler) and Naama Yavor (@namivor).}.

Accounting for Within- AND Between-Subject Effects

statistics

mixed-design

code

linear models

This is a comment on Solomon Kurz’s (@SolomonKurz) recent post¹ where he discusses how group-level…

(Bootstrapping) Follow-Up Contrasts for Within-Subject ANOVAs (part 2)

R

statistics

ANOVA

bootstrap

code

mixed-design

permutation

A while back I wrote a post demonstrating how to bootstrap follow-up contrasts for repeated-measure ANOVAs for cases where you data violates some / any assumptions. Here is a demo of how to conduct the same bootstrap analysis, more simply (no need to make…

Signal Detection Theory vs. Logistic Regression

R

statistics

code

logistic regression

signal detection theory

I recently came across a paper that explained the equality between the parameters of signal detection theory…

(Bootstrapping) Follow-Up Contrasts for Within-Subject ANOVAs

R

statistics

ANOVA

bootstrap

code

mixed-design

permutation

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…

Quidditch: Is It All About the Snitch?

R

statistics

code

harry potter

simulation

Much has been said about how the game of Quidditch is ruined by the scoring system - specifically how it makes no sense that the snitch is worth 150 points and that catching…

Examining Inter-Rater Reliability in a Reality Baking Show

R

statistics

code

reliability

test theory

Game of Chefs is an Israeli reality cooking competition show, where chefs compete for the…

Common Practice (misuse of) Moderation in Regression

statistics

assumptions

interactions

linear models

moderation

regression

… or how I stopped trusting standardized coefficients.

What ARE the Assumptions of Linear Regression?

statistics

assumptions

linear models

regression

When you assume,
you make an ass out of you and me.

Testing the Bottleneck Account for Post-Error Slowing Beyond the Post-Error Response

publications

Post-error slowing (PES)

In our recent paper, Testing the Bottleneck Account for Post-Error Slowing Beyond the Post-Error Response, soon to be published in Biological Psychology¹, we examined whether post-error slowing (PES) can be explained as resulting from depleted cognitive resources caused by…

cheatR: an R package for catching cheaters

R

academia

code

plagiarism

cheatR is a mini package to help you find cheaters by comparing hand-ins. It was developed by Almog Simchon and me in response to students overheard bragging about how an assignment in an first-year undergrad course was “super easy” because “we all just copied from…