Advanced Statistics for Research
Postgraduate: Level 7
Sunday 15 January 2023
Friday 24 March 2023
13 April 2021
Requisites for this module
MSC C80612 Research Methods in 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)
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.
The module aims to provide a detailed overview of some advanced statistical tests used by postgraduate and postdoctoral researchers in psychology.
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.
No additional information available.
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
||Homework Assignment 1
||Homework Assignment 2
||Homework Assignment 3
||Homework Assignment 4
||Lab based open book test
Module supervisor and teaching staff
Dr Alasdair Clarke, email: email@example.com.
Dr Anna Hughes;
Dr Alexander Jones
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).
* Please note: due to differing publication schedules, items marked with an asterisk (*) base their information upon the previous academic year.
Disclaimer: The University makes every effort to ensure that this information on its Module Directory is accurate and up-to-date. Exceptionally it can
be necessary to make changes, for example to programmes, modules, facilities or fees. Examples of such reasons might include a change of law or regulatory requirements,
industrial action, lack of demand, departure of key personnel, change in government policy, or withdrawal/reduction of funding. Changes to modules may for example consist
of variations to the content and method of delivery or assessment of modules and other services, to discontinue modules and other services and to merge or combine modules.
The University will endeavour to keep such changes to a minimum, and will also keep students informed appropriately by updating our programme specifications and module directory.
The full Procedures, Rules and Regulations of the University governing how it operates are set out in the Charter, Statutes and Ordinances and in the University Regulations, Policy and Procedures.