Statistical Training Program

The Many Faces of Regression
23 May

The Many Faces of Regression

To help you digest the diverse literature on regression, this workshop gives you a tour of the many different ways that regression can be used (with copious examples).
Recruiting Participants for Interviews
21 May

Recruiting Participants for Interviews

This workshop offers practical guidance on devising a recruitment plan, by outlining several strategies to engage participants.
MM7 - Critical reading & writing of study design in MM - Self paced learning and Q&A session
20 May

MM7 - Critical reading & writing of study design in MM - Self paced learning and Q&A session

Critical reading and writing about mixed methods involves appraisal and justification. This session looks at the core theoretical concepts of mixed methodologies (MM) and unpacks common difficulties in explaining and justifying the choice of MM study design aspects in academic writing.

The presenter will send out the recording to watch prior to the session. Please come along to the Q&A session with your questions ready.
Shiny App Fundamentals in R
20 May

Shiny App Fundamentals in R

Learn how to share your code and results with end users: Shiny app fundamentals in R.
Finessing a Grammer of Graphics in R
16 May

Finessing a Grammer of Graphics in R

We examine straightforward though inelegant & limited solutions, to motivate your learning about the concepts that support more elegant & more useful options. We move towards more efficient and effective use of ggplot, whilst also better aligning with Tufte's principles of excellence in graphics. Importantly, we will introduce computational thinking to help you learn and test new ggplot functionality, whilst ensuring that graphs are reproducible.
MM6 - Assembling Mixed Methods involving surveys and interviews
13 May

MM6 - Assembling Mixed Methods involving surveys and interviews

By assembling we mean putting together different components of a mixed methods study, including how qual(itative) and quant(itative) components combine, how data collected is "mixed" across qual and quant components and how results are combined.