Skip to main content

Workshop Series

The Statistical Consulting Studio 
holds several workshops each semester.  The topics are chosen to benefit a broad range of researchers with various backgrounds.  While these classes are free for all faculty and students in the Emma Eccles Jones College of Education and Human Services, space is limited.  Please register early to ensure a seat.

All sessions 1-3 pm in LILLY 003

Reserve Your Seat







February 17

GEE: Generalized Estimating Equations

Classic linear regression relies on observations being independent or uncorrelated.  Data collected on observations nested within a hierarchical population structure (e.g. students within classes) and longitudinal studies (repeated measurements on each subject) should be analyzed via a method that takes into account the lack of independence between observations. Violating or ignoring this assumption can lead to spurious results such as an inflated Type I error rate. Directly including a correlation structure as part of the model essentially corrects the under-estimated standard errors, resulting in more appropriate and replicable results. This workshop will provide an overview of GEE as a method for handling longitudinal, nested, and non-independent data, and discuss difference with multi-level (mixed-effects) statistical models.

Friday March



March 10

Random Forests: Classification & Regression Trees

It is common in statistical modeling to have far more predictor variables available than will be included in a final model (especially when including combinations of variables as interaction effects). Often, predictor variables are correlated with one another and have differing patterns of missingness. In order to arrive at the most parsimonious statistical model that accounts for as much of the variance in the outcome as possible, a systematic approach to variable selection is needed, which also allows for interactions, correlations, missingness, and unbalancedness across groups. This workshop will demonstrate the use of Random Forests as a method to arrive at the strongest and most parsimonious set of predictor variables. Although a limitation of Random Forests is interpretability of effects, methods for using the results of Random Forests as a guide in other modeling approaches will be presented.





April 7

Ridge & LASSO: Regularized Regression

Ridge and LASSO regression are alternative approaches in situations where a large number of predictor variables are available to choose from, and where a high degree of correlation among those predictor variables is possible. These methods guide selection of the most important predictor variables (even from among hundreds) without having to ‘fish’ for significance. The resulting models are parsimonious, with easy to interpret estimates, that also avoid problems with collinearity, biased estimates, and overfitting.

A new system to electronically collect and enter data is now available at USU.
Although similar to Qualtrics, it offers many more advanced features.

Learn more about REDCap it's features and getting started.

Each session will include

  • overview of main features
  • sample demonstration
  • questions and answers

All sessions held 1-2pm in EDUC 454

Researve Your Seat (space is limited)


January 27


February 16


March 22


April 25

If you would like to receive periodic emails announcing new sessions and links to register, click “Subscribe” HERE to submit your email address to our mailing list. 

Materials for past workshops may be accessed below.  Clicking on a date below to be directed to a webpage with class notes and files.






R, For Absolute Beginners

(starting from scratch)

Oct 2015


Effect Size & Power Analysis:
Using the FREE ‘G*Power’ Software

Nov 2015


Data Visualization:

Easily Exploring Your Data in R using ggplot2

(assumes some familiarity with R)


Feb 2016


Powerful Publishable Plots

(no software needed)

Apr 2016