Introduction to Programming in SAS
Octavia Wong
Jan. 25, Feb. 1, Feb. 8, & Feb. 15; 11:30am – 1:30pm
Location: Zoom
Cost: $40
Click here to register.
This short course is an introduction to the Statistical Analysis System (SAS) syntax commands and procedures. We will be covering:
- Reading, transforming, merging, and saving data files in some common formats
- Selecting cases, and modifying and computing variables
- Performing some basic statistical procedures and tests, including descriptive statistics, correlations, contingency tables, Chi-square tests, t-tests, ANOVA, regression, general linear models, and multilevel models
- Creating various figures, including bar charts, box plots, histograms, normal QQ plots, scatterplots, density curves, and line graphs
- Saving output results and work in some common formats
- Creating simple macros (if time allows)
Note: This course is not intended as an introduction or review of basic statistics. Rather, it focuses on the implementation of these statistics in SAS. As such, this course is designed for participants with some introductory level statistical knowledge but no previous experience in using SAS.
A Hands-On Introduction to Systematic Reviews and Meta-Analysis
Nataly Beribisky
Feb. 10 & Feb. 17; 9:30am – 11:30am
Location: Zoom
Cost: $20
Click here to register.
This course will introduce participants to the nature and types of systematic reviews that can be used to address specific research questions, as well as to meta-analysis, the analytical procedure used to summarize the effects extracted from systematic reviews. By the end of the workshop participants will understand the role of systematic reviews and meta-analyses, the steps of conducting a systematic review, and the methods used to organize and combine the results of studies from systematic reviews. All aspects of the course will include hands-on experience using open-source software.
Introduction to R Programming
Ronda Lo
March 2, 9, 16, 23; 1:30pm – 4:30pm
Location: BSB 061 (Note: This workshop is in-person and will require proof of vaccination.)
Cost: $60
Click here to register.
This workshop will introduce the basics of how to program in R, including data management techniques, simple analyses (e.g., t-tests, correlations, ANOVA) and visualization of data. By the end of the workshop, participants will be more familiar with how to import data files, use R to clean their own data (e.g., recode, rename, delete, add data), conduct simple analyses, and plot their data. All code, materials, and examples will be provided for you to keep after the workshop. This workshop is suitable for all beginners, including undergraduates looking to conduct their own analyses for honours thesis projects and independent research projects, as well as graduate students and faculty looking to get a better understanding and familiarity with R to use in their research.
Adopting Open Science Practices in Psychology
Chantelle Ivanski
Mar. 2; 9:30am – 11:30am
Location: Zoom
Cost: $10
Click here to register.
In this workshop, we will talk about the history of the replication crisis and how we can help to solve these issues moving forward through open science practices. This will include issues around questionable research practices, with a specific focus on p-hacking and HARKing. We will discuss the major tenants of open science (namely preregistration, open materials, and data sharing) with tips on how to best engage in these practices in your own work. We will also write a sample preregistration together.
An Introduction to 'The New Statistics'
Joshua Quinlan
March 11; 2:30pm – 5:30pm
Location: Zoom
Cost: $15
Click here to register.
Null hypothesis statistical testing (NHST) has long been the dominant approach to data analysis in psychology, despite well-documented and long-acknowledged problems with the perspective. Recently, there has been a movement to focus more on analytic techniques that are not NHST-reliant, such as effect sizes and confidence intervals, dubbed ‘the New Statistics.’ This workshop will provide an in-depth introduction to these concepts, along with related concepts in research methods designed to address the limitations of NHST. Recommended for undergraduate students and early graduate students with an interest in statistics and open science.
