Skip to main navigationSkip to main content
The University of Southampton

PSYC6055 Statistical Programming in R

Module Overview

The Statistical Programming in R Module is focused on extending existing skills in analyzing data from quantitative research. The focus of this course will not be on extensively expanding the mathematical knowledge of the techniques employed but will be on acquiring practical skills such as scripting, flexible matrix manipulation and advanced visualization. All these skills are particularly useful when confronted with especially large datasets, and when confronted with a multitude of repetitive statistical procedures needing implementation. Analyses will be implemented using the interactive programming environment known as R. R is a free, open source implementation of the S language for statistical analysis.

Aims and Objectives

Learning Outcomes

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

  • Understanding and implementing Linear Mixed Models (LMM)
Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

  • Implementation of statistical procedures within the R environment
Transferable and Generic Skills

Having successfully completed this module you will be able to:

  • Data manipulation - acquiring skills in flexible matrix manipulation
  • Scripting - programming an analysis in such a way that the script can be used with minimal effort for similar datasets and analyses and for especially large datasets
  • Data visualisation - learning how to create high-quality figures, especially associated with more complex analyses (e.g. three dimensional scatter plots, Trellis displays, etc.).


This module is divided into two main components. The first component is Introduction into the R language and implementing Basic Statistical Methods. The second component is implementing the General Linear Model and Mixed Linear Models (MLM) in R. A specific focus will be on scripting, data manipulation and advanced data visualisation. No prior knowledge concerning MLM or computer programming is required. An Introduction on the basic ideas of MLM will be conveyed as well as implementation of this technique for specific research questions. Whereas this technique is used quite frequently in certain scientific areas (e.g. Medicin), it has only recently started being used in areas such as for instance Psychology, and as such has not yet found its way to the standard undergraduate curriculum. However, this technique has considerable advantages compared to for instance ANOVA’s in a multitude of situations.

Learning and Teaching

Teaching and learning methods

Teaching Activities Type: 12 weeks of 2 hours workshops Hours: 24 Group Size: 25 maximum

Independent Study100
Follow-up work24
Total study time150

Resources & Reading list

Software requirements. R is open-source, free software which is available for download for the Windows, Mac OS X and Linux platform.

Alain Zuur, Elena Ieno, & Erik Meesters (2009). A Beginner's Guide To R. 



MethodPercentage contribution
Assessment 50%
Assessment 50%


MethodPercentage contribution
Assessment 100%

Repeat Information

Repeat type: Internal & External

Share this module Share this on Facebook Share this on Twitter Share this on Weibo
Privacy Settings