I have taught two graduate courses in Research Methods/Statistics since Fall 2012, one on ANOVA and one on Regression.  However, the 2020-21 year will be the last year I will be teaching these courses for the foreseeable future.  I have written about 20 or so R scripts for these courses using packages such as tidyverse and brms, along with examples in JASP and SPSS.  If you are an instructor who will be teaching courses like these in the future, and you would like my course materials then email me at and I will share my materials.

If you are in industry and would like me to give a short course on statistics to your company or team, or to help with statistical consulting please email me at  I can help with most statistical modeling questions, and I am interested in applied problems.

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

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

Multilevel modeling lecture that gives a brief overview, as well as examples of multilevel models in R, including one example where the response is at the trial level (participants make repeated choices on each trial).

Logistic Regression using R and JASP

Mediation example using R

Criticisms of Null Hypothesis Significance Testing

Programming Javascript Experiments in Qualtrics

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

Bayes’ Rule

Markov Chain Monte Carlo (MCMC)

Null Hypothesis Significance Testing (NHST)

Bayesian Approaches To (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).

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