Statistical Training Program

Mixed Methods 5 - The Quantitative in Mixed Method - 'de-confusing' core concepts
15 May

Mixed Methods 5 - The Quantitative in Mixed Method - 'de-confusing' core concepts

Mixed methods may have a tarnished reputation in some fields, especially where the quantitative analysis is poorly conducted or reported. This workshop seeks to redress this problem!
We cover some basic concepts that shape quantitative analysis and contrast them with corresponding concepts in qualitative analysis. This aims to help resolve confusion about key concepts which often arise due to misunderstandings across methods and paradigm.
Quantitative Validation of Survey Constructs
12 May

Quantitative Validation of Survey Constructs

This is one part of a two-part series, aiming to guide you on how to validate surveys. This first part focuses on basic quantitative techniques for validating survey constructs.
Mixed Methods 4 - Assembling Mixed Methods involving surveys and interviews Q&A Session
05 May

Mixed Methods 4 - Assembling Mixed Methods involving surveys and interviews Q&A Session

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 component,s and how results are combined. Other workshops detail data collection particular qual and quant methods. This workshop presents several Mixed Methods designs suitable for mixing survey and interview data.
Qualitative validation of surveys
03 May

Qualitative validation of surveys

This session will guide participants on how to implement qualitative research techniques in the initial phases of survey validation, before quantitative survey validation techniques are applied. The qualitative techniques covered in this session are based on the seven steps outlined in the AMEE guide (Artino et al., 2014).
Taming ggplot in R
02 May

Taming ggplot in R

The workshop builds on the previous one (Excellent Graphics in R), which showed how ggplot supports Tufte's principles of excellence, for visualising data. 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.
Excellent Graphics in R
28 Apr

Excellent Graphics in R

We weave in discussion of the principles of excellent graphics, whilst introducing the grammar of graphics underlying the ggplot2 package in R. We put five basic principles into action for visualising data. This starts with the quintessential plots for showing statistical distributions: histograms, box plots, density plots and others. Then we consider plots of relationships: the humble scatterplot, with many options for refinement.