Overview of the Course

During this intensive workshop we will cover a number of introductions to topics which are core to statistical analysis in applied research. This will include introduction to R as a tool to analyse data, visualize it and to use it for a very very basic analysis of the relationships in your data. We will further revise some of the most commonly used statistical tests and provide you with a guidance how to set up and interpret them in R. We will introduce you to simple linear model and mixed effect modelling approaches, including logistic setting. Lastly, we will provide a very gentle introduction to Bayesian statistics and modelling using R.

If time allows, we will try to go through all the steps presented in the book, yet if we may need to skip a section, please try to complete all practices in your own time. We promise it wont take really long!

Programme

Week 1: Tuesday, 18th June, 12.30-5pm - Intro to R, Simple Data Analysis, Data organisation, Getting used to R console and scripts

Week 1: Thursday, 20th June, 12.30-5pm - Tests in R, Visualisations, Simple Linear Models, Interactions and Non-Linear Effects.

Week 2: Tuesday, 25th June, 12.30-5pm - Mixed Methods, Model Selection, Logistic Models, Assumptions testing and Visualisations

Week2 : Thursday, 27th June, 12.30-5pm - Intro to Bayesian estimation, Tests and Simple Linear Model in Bayesian Setting, Bayes LMER

Week 2: Friday , 28th of June, 2.00-5pm (Updated Schedule)

  • Drop-in sessions 12.30-2.25pm - Send us an email if planning to attend
  • Quick Intro to RMarkdown 2.30-3pm
  • Workshop by Stephanie Allan (University of Glasgow) ‘Scraping and Visualising Twitter Data using R’ 3.10-4.30pm
  • Closing Remarks

Drinks Reception (same Friday) 5-7pm (Concourse, 7 George Square)

Learning Outcomes

By the end of the workshop you will learn how to:

  • Use R and how knowledge of one statistical software language can help you transitioning in learning other software
  • Explore and visuliase the data effectively
  • Conduct basic analysis of the relationships in your data
  • Be able to conduct a complete analysis of longitudinal data/repeated measures
  • Understand the main differences between frequentist and Bayesian approaches
  • Appreciate the importance of measuring uncertainty
  • Gain the fundamental knowledge about model building and model selection
  • Hopefully, feel confident to learn more about R and statistics in your own time :)

Preparation

One of the requirements for the course is to have R installed and ready. It is also advisable to go through these introductory tutorials to get yourself going with R, do not worry if it seems very confusing at the start, with more practice you ll find it easier and friendlier as well.

Data sets

Each section will have a selection of datasets that we will use either for illustration or for practical exercises that you can attempt at your own time.

Expectations

To ensure that this course is actually useful, you are expected to attempt the exercises presented in the materials either during the series or in your own time. You may also find that some of the materiel will be more useful for you than others, yet do not skip the rest. All of these make up a very good overview of fundamentals which will be relevant not just for your PhD but for your further research career. Even if you think you already know introductory parts, still do come by as you may hear about new things or can share with us things you may know but which were not covered.

Please also use the time allocated for further queries and discussion of problems you feel you need an extra assistance with. You all may be in very different stages of your research/data collections and analysis. It does not mean however that you will not be able to ask about other issues during this session as long as they are related to data analysis, statistics or using R and other software for your research. We will have drop in sessions and also can use our breaks for more informal chats.

Lets stay in touch

Anastasia Ushakova Twitter:apavluhina

Emma Waterston Twitter:EmmaWaterston

List of extra resources

You can find a list of resources that can help you to take an in depth look at materials we covered in your own time here.

Authorship

The content of these materials was partially adapted from work by colleagues at Edinburgh and UCL as a part of statistics curriculum provision at both UG and PG levels, where material was adapted from other resources, these were cited in text. If you have any comments/feedback just get in touch and let us know, we will also share a feedback form by the end of the course :)

Last, but not least

The book was created using brilliant book down, if you want to learn how to build one, have a look here: its quite easy and fun!