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. Lastly, we will introduce you to simple linear model and mixed effect modelling approaches.

If time allows, we will try to go through all the steps presented in the material 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!

Topics we will cover

  • Introduction to R
  • Common tools to describe and visualise data (i.e.psych, gpplot)
  • Studying relationships: an overview of statistical tests,diagnostics and assumptions testing
  • Intuition behind simple linear models, correlation analysis
  • Mixed effect models

Learning Outcomes

By the end of the workshop you 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

Data sets

All the data that you will need for this course can be accessed here: Data It is a zip folder, so make sure you unzip it somewhere where you can easily find it so we can work with it later.


Please note this is a rough schedule, we will take breaks as we go and will take some time for practice as well.


9-9.30 Introductions and setting up

Part 1 ( Milan Valasek)


  • Overview of R
  • Basics of descriptive analysis and data types
  • Descriptive statistics in R and plots

Lunch Break

Part 2 (Anastasia Ushakova)


  • Statistical tests in R
  • Correlation Analysis and Simple Linear model
  • Regression Diagnostics
  • Interactions and Model Comparisons

16.00-17.00 Practice, questions



Setting up, questions drop in (come and see us if you have any questions about what we were doing so far)

Part 3 (Anastasia Ushakova)


  • Linear model continued
  • Extensions to repeated measures
  • Mixed effect models
  • High Dimensional Analysis:resources for R

12.00-13.00 Practice and final questions



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.

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.

Lets stay in touch

Dr Anastasia Ushakova Twitter:apavluhina

Dr Milan Valasek


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.