Advanced Statistics for Research

The details
Colchester Campus
Postgraduate: Level 7
Sunday 15 January 2023
Friday 24 March 2023
13 April 2021


Requisites for this module



Key module for

MSC C80612 Research Methods in Psychology,
MPHDC80048 Psychology,
PHD C80048 Psychology,
MSCIC998 Psychology with Advanced Research Methods,
MSCICB98 Psychology with Advanced Research Methods (Including Placement Year),
MSCICB99 Psychology with Advanced Research Methods (Including Year Abroad)

Module description

This module provides a detailed overview of the most common statistical tests used by postgraduate and postdoctoral researchers in psychology.

The module will build upon Statistics I. The course will provide opportunities to specialize in advanced concepts and statistical techniques, including multi-level modelling and Bayesian analysis. The course will also provide experience of using R for data processing and visualisation.

Module aims

The module aims to provide a detailed overview of some advanced statistical tests used by postgraduate and postdoctoral researchers in psychology.

Module learning outcomes

By the end of the module, students should have a solid conceptual understanding of and have confidence in using a number of advanced statistical techniques, including:

1. Carry out reproducible analysis by using computer code
2. Data visualisation
3. Statistics as linear modelling (e.g.,: multi-level, glm, etc)
4. Power analysis and data simulation
5. Synthesize the above while carrying out data exploration

1), 2), 3) and 4) will be assessed via the homework assignments, while 5 will be assessed in the open-book test.

Students will develop an understanding when to use what advanced statistical technique and have an awareness of how to address common problems encountered when using these procedures.

Students will know how to interpret and report these techniques appropriately, and be aware of common errors in the interpretation of data.

Students will attain considerable experience of using R to analyse data. They will develop sufficient statistical computing skills to independently analyse data at an advanced level.

Module information

No additional information available.

Learning and teaching methods

2h lectures + 1h workshops / week (for 10 weeks)


  • McElreath, Richard. (©2016) Statistical rethinking: a Bayesian course with examples in R and Stan, Boca Raton: CRC Press/Taylor & Francis Group.

The above list is indicative of the essential reading for the course. The library makes provision for all reading list items, with digital provision where possible, and these resources are shared between students. Further reading can be obtained from this module's reading list.

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Weighting
Coursework   Homework Assignment 1    12.5% 
Coursework   Homework Assignment 2    12.5% 
Coursework   Homework Assignment 3    12.5% 
Coursework   Homework Assignment 4    50% 
Coursework   Lab based open book test    12.5% 

Overall assessment

Coursework Exam
100% 0%


Coursework Exam
100% 0%
Module supervisor and teaching staff
Dr Alasdair Clarke, email:
Dr Anna Hughes;



External examiner

Dr Alexander Jones
Middlesex University
Senior lecturer
Available via Moodle
Of 30 hours, 0 (0%) hours available to students:
30 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).


Further information

* Please note: due to differing publication schedules, items marked with an asterisk (*) base their information upon the previous academic year.

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