MRes Psychology / Course details

Year of entry: 2020

Course unit details:
Advanced Statistics Workshop

Unit code BIOL63112
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 2
Offered by School of Biological Sciences
Available as a free choice unit? No

Overview

Course unit overview

The module will consist of six workshops on: advanced analysis techniques in R (four workshops), MATLAB, and Bayesian statistics.

• The workshops on R will further develop students’ data wrangling, data visualisation, and statistical modelling skills in the R programming environment. They will also provide the opportunity to use a range of advanced R packages including lme4 for (generalised) linear mixed models.

• The workshop on MATLAB will introduce the MATLAB programming environment. It will cover programming and simulation-based statistics. The teaching will be complemented by real-time demos and after-class exercises.

• The workshop on Bayesian approaches to statistics will cover the application of Bayesian models. 

Aims

Aims

To develop students’ abilities in data wrangling, programming (incl. data simulation) and analysis in R, mixed models, MATLAB, and Bayesian approaches to statistics.

To provide students working in small groups with the experience of advanced decision-making in the application of psychological statistics.

To provide students working in small groups with the experience of using software appropriate for each method (e.g., R, MATLAB) and with the interpretation of output files.

 

Learning outcomes

Learning outcomes

Having attended the course unit, students will be able to:

• demonstrate an understanding of the logic underlying the use of advanced linear models and (generalised) linear mixed models in R, programming in R and MATLAB, Bayesian approaches, and the range of circumstances appropriate for their use,

• conduct analysis involving regression, hierarchical linear models, generalised linear mixed models, taking appropriate decisions and approaches and applying specialist software.

Teaching and learning methods

Teaching and learning methods

In Semester 2 each workshop will consist of a four-hour session combining taught material with practical exercises based on psychological data sets and software. 

 

Assessment methods

Assessment methods

Continuous assessment. Two assignments. Each topic will be formally assessed by a written assignment, worth 50% of the marks for this course unit.

 

Linear Mixed Modelling in R: the form of assessment varies from year to year. Each student will be required, for example, to carry out analysis based on provided data and write a short report of the analysis using R Markdown (1000 words equivalent).

 

 

Hackathon: the assessment will require each student to carry out data wrangling, data visualization, and statistical modelling on a large dataset of their choosing (e.g., downloaded from one of the ‘big data’ repositories such as Kaggle, Gapminder, or Google Dataset Search) or a large open dataset from a Psychological research area.  The data wrangling, visualization, and modelling will be written up using R Markdown. (1000 words equivalent).

 

Feedback methods

No information available.

Recommended reading

  • Field, A. (2009 or 2005). Discovering statistics using SPSS. London: Sage.

Either the second or third edition is good and contains a useful CD-ROM of examples.

Study hours

Scheduled activity hours
Lectures 24
Independent study hours
Independent study 126

Teaching staff

Staff member Role
Andrew Stewart Unit coordinator

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