Useful R Intro for new beginners here

I recommend *R For Data Science*

I also really like *Statistical Rethinking* by Richard McElreath. Here is an amazing guide to the book with code written using ggplot2 and brms

A graduate student in our department, Moein Razavi, came up with a cool R package for running hierarchical linear regression models after taking my PSYC 671 course. Check it out here

A nice example of multivariate regression in R here

3/18/20 – Since we can’t hold classes due to COVID-19 I’ll be recording my lectures and uploading them to YouTube.

Interactions w/Continuous Predictors 2

Interactions w/Continuous Predictors 1

Categorical predictors:

As of 2019 I have added several JASP and R examples to my methods/stats courses. Below is a power point with some slides that instructors and students might find useful:

Some Data Analysis Examples in JASP and R

Data files and R code for the above lecture can be found on the OSF: here

**PSYC 607 (Inferential Statistics and Experimental Design)**

For Fall 2018 I redid the lecture on Descriptive Statistics to include hands on practice using R’s ggplot2 package to plot different types of data. Students brought their laptops and did the R exercises during the lecture. Many students had no prior programming experience. The materials for this lecture are available on the osf by clicking here

**Bayesian Statistics**

I have taught Inferential Statistics and Regression at the graduate level since 2012. These are the two courses graduate students in the Psychology department at Texas A&M University are required to take their first year.

Recently I spent a large amount of time learning about Bayesian inference and Bayesian data analysis. This approach makes much more sense to me, and I gave a short course to graduate students and faculty at Texas A&M in May 2017. The course was based on John Kruschke’s (excellent) book *Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan.* The book’s website is here.

I also recommend JASP. It can basically replace SPSS to perform traditional analyses and it also easily performs Bayesian analyses from the General Linear Model, yielding Bayes Factors that can replace *p* values.

The course covers Bayesian Inference and includes hands on lectures that cover both the model comparison approach that utilizes Bayes Factors, and the parameter estimation approach that focuses on 95% highest density (or credible) intervals. I favor reporting Bayes Factors instead of *p* values and 95% HDIs for parameters instead of confidence intervals.

I have provided the power points for the course lectures to facilitate the field’s switch to Bayesian approaches. I highly recommend purchasing Kruschke’s book as well. The JASP tutorial is based on a tutorial E.J. Wagenmakers and others have written and made available on the Open Science Framework here.

Day 1 Lectures

Introduction: Credibility, Models, and, Parameters

Markov Chain Monte Carlo (MCMC)

Null Hypothesis Significance Testing (NHST)

Day 2 Tutorials

Doing Bayesian Data Analysis with JASP

Data files for the above lecture can be found on the OSF here

Doing Bayesian Data Analysis with R, JAGS

*Mac users had trouble getting the JAGS code to work until they installed the latest version of iquartz (or something like that).