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A guide to working with country-year panel data and Bayesian multilevel models

By Andrew Heiss

Added Sun Dec 12, 2021

What is this?

Excerpt from guide: “Here’s a basic guide to dealing with country-year panel data using Bayesian multilevel modeling!”

  1. Link to guide here: https://www.andrewheiss.com/blog/2021/12/01/multilevel-models-panel-data-guide/

Five different “Cohen’s d” statistics for within-subject designs

By Cookie Scientist

What is this?

Excerpt from blog: What I’ll show here is that there are at least 5 different and non-equivalent ways that people might compute a d-like effect size (which they would invariably simply call “Cohen’s d”) for Jeff’s dataset, and the resulting effect sizes range from about 0.25 to 1.91. I’ll compare and contrast these procedures and ultimately choose one that I think is the least crazy, if you must compute a standardized effect size (more on that later).

  1. Link to blog here: http://jakewestfall.org/blog/index.php/2016/03/25/five-different-cohens-d-statistics-for-within-subject-designs/

Handling missing values in R

Added Sun June 6th, 2021

What is this?

Excerpt from blog: Handling missing values in R, one of the common tasks in data analysis is handling missing values. In R, missing values are often represented by the symbol NA (not available) or some other value that represents missing values (i.e. 99). Impossible values (e.g., dividing by zero) are represented by the symbol NaN (not a number)

  1. Link to blog here: https://www.r-bloggers.com/2021/04/handling-missing-values-in-r/

Running Multiple Linear Regression Models in for-Loop

By Joachim Schork

Added Sun June 6th, 2021

What is this?

Excerpt from site: In this article, I’ll show how to estimate multiple regression models in a for-loop in the R programming language.

  1. Link to article here: https://statisticsglobe.com/r-multiple-regressions-in-for-loop

(What to do) When Predictors Co-Vary

By Mattan S. Ben-Shachar

What is this?

Excerpt from blog: 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.

  1. Link to blog here: https://shouldbewriting.netlify.app/posts/2020-08-11-when-predictors-covary/