Useful R Intro for new beginners here
I recommend R For Data Science
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
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:
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
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
Day 2 Tutorials
Data files for the above lecture can be found on the OSF here
*Mac users had trouble getting the JAGS code to work until they installed the latest version of iquartz (or something like that).