_{Last updated 2020-10-30.}

This Github repo contains all lesson files for Practical Applications in R for Psychologists. The goal is to impart students with the basic tools to process data, describe data (w/ summary statistics and plots), and the foundations of building, evaluating and comparing statistical models in R, focusing on linear regression modeling (using both frequentist and Bayesian approaches).

These topics were taught in the graduate-level course Advanced Research Methods for Psychologists (Psych Dep., Ben-Gurion University of the Negev, Fall semester, 2020), laying the foundation for the topic-focused courses (Spring semester):

• Structural equation modelling (SEM)
• Machine Learning (ML).
• Hierarchical linear models (HLM).

Notes:

• This repo contains only materials relating to Practical Applications in R. Though statistics are naturally discussed in many lessons, the focus is generally on the application and not on the theory.
• Please note that some code does not work on purpose and without warning, to force students to learn to debug.

You will need:

1. A fresh installation of R (preferably version 4.0 or above).
2. RStudio IDE (optional, but recommended).
3. The following packages, listed by lesson:

You can install all the packages used by running:

# in alphabetical order:

pkgs <- c(
  "afex", "BayesFactor", "bayestestR", "DescTools", "dplyr",
  "effectsize", "emmeans", "finalfit", "ggeffects", "ggplot2",
  "ggResidpanel", "haven", "Hmisc", "insight", "interactions",
  "mediation", "mice", "parameters", "performance", "permuco",
  "ppcor", "psych", "psychTools", "pwr", "readxl", "summarytools",
  "tidyr", "tidyverse"
)

install.packages(pkgs, dependencies = TRUE)