Quantitative Methods Workshop Series (QMWS)

The first annual Quantitative Methods Workshop Series took place in the Spring of 2021 and was a huge success. Given the interest, we are running a similar slate of workshops in the Fall and Winter terms. The Fall slate of workshops has now been announced and are available for registration. All Fall workshops will be held online via Zoom.

Stay tuned for the announcement of the Winter workshops!

Quantitative Methods Workshop Series: Fall 2021

Introduction to R (for Undergraduate/Graduate Students)
Naomi Martinez Gutierrez
Oct. 21, Oct. 28, Nov. 4, & Nov. 11; 1:00pm – 4:00pm
Location: Zoom
Cost: $60 (+ Eventbrite Registration Fee)
Click here to register.
R is an independent open source statistical software package that is of value for its wide-ranging pre-programmed statistical procedures and capacity for programming tailored statistical analyses. Also, R is invaluable for generating informative high-quality graphics.
This course is a step-by-step hands-on introduction to R. No familiarity with R is assumed, but participants will need a basic working knowledge of statistics. Participants will learn how to: 1) install R on their computers; 2) enter, import, and manipulate data; 3) use a variety of R packages including the tidyverse; and 4) carry out basic mathematical, statistical and graphical operations and procedures in R. Upon completion of this course, participants will be comfortable with, and able to do, basic statistical work in R. Additionally, they will be familiar with resources for follow-up help and learning about R.

Fundamentals of Regression Analysis (for Undergraduate/Graduate Students)
Linda Farmus
Dec. 3 & Dec. 10; 10:00am – 1:00pm
Location: Zoom
Cost: $15 (+ Eventbrite Registration Fee)
Click here to register.
"Regression" is a general term for methods that fit a statistical model to a given set of variables to predict the effect that changes in independent variables have on a dependent variable using linear assumptions.
The focus of this brief introductory course is to understand and interpret linear regression analysis output from simple regression, multiple regression (with and without interaction effects), and logistic regression models using R statistical software. Other topics covered include evaluating the accuracy of regression models, checking assumptions, and non-conventional cases of regression models, such as ANOVA and ANCOVA.

The Problems with (and solutions for) the P-Value: Critiques and Alternatives for Null-Hypothesis Statistical Testing (NHST)
Dr. Raymond Mar
Nov. 11 & Nov. 12; 2:00 pm – 3:00 pm
Location: Zoom
Cost: $10 (+ Eventbrite Registration Fee)
Click here to register.
For decades, statisticians have pointed out the problems with employing NHST for statistical inferences. These issues have become increasingly difficult to ignore, with the APA recommending a shift away from NHST in a report now over 20 years old (Wilkinson and APA task force on statistical inference). In this 2 day workshop, we will review the problems of NHST (day 1) and discuss various alternatives (day 2).

Visualizing Linear Models: An R Bag of Tricks
Dr. Michael Friendly
Oct. 27, Nov. 3, & Nov. 10; 1:00 pm – 4:00 pm
Location: Zoom
Cost: $45 (+ Eventbrite Registration Fee)
Click here to register.
OK, so you ran your ANOVA, multiple regression (MRA), or multivariate counterparts (MANOVA, MMRA), but now you need to visualize the results to both understand them and communicate. Who you gonna run to? – R of course.
This course covers data visualization methods designed to convert models and tables into insightful graphs. It starts with a review of graphical methods for univariate linear models---data plots, model (effect) plots and diagnostic plots. A brief introduction to multivariate linear models uses data ellipses (or ellipsoids) as visual summaries of 2D (or 3+ D) of multivariate relations. The Hypothesis-Error (HE) framework provides a set of tools for visualizing effects of predictors in multivariate linear models. I give some examples of HEplots for MANOVA and MMRA designs. Finally, if time permits, some model diagnostic plots for detecting multivariate outliers and lack of homogeneity of (co)variances will be described.
Participants should have a background in statistics including a course in linear models (ANOVA, multiple regression). In addition, they should have some familiarity with using R and R Studio, such as the SCS course, An Introduction to R and the Tidyverse or equivalent. A web page for the course will give access to lecture notes and resources: https://friendly.github.io/VisMLM-course/

How to Choose the Right Statistical Test (for Undergraduate Students)
Joshua Quinlan
Nov. 18; 1:00pm – 4:00pm
Location: Zoom
Cost: $15 (+ Eventbrite Registration Fee)
Click here to register.
You’ve got a great idea for a study, maybe you’ve even collected your data… now what? This workshop is designed to help newcomers in statistics choose the right statistical test for their research question and design. It should be helpful to students who are new in statistics and still struggle with knowing which tests and effect sizes are used and when. In particular, it may also be helpful to students conducting honours thesis studies or independent research projects. Some basic familiarity with statistics will be necessary but this workshop is intended for beginners in statistics.

 

Archive of Past Quantitative Methods Workshop Series