PYM0S2-Data Collection & Analysis 2

Module Provider: Psychology
Number of credits: 10 [5 ECTS credits]
Level:7
Terms in which taught: Spring term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites: PYM0S1 Data Collection and Analysis 1
Modules excluded:
Current from: 2020/1

Email: k.murayama@reading.ac.uk

Type of module:

Summary module description:
The module will extend students' theoretical and practical knowledge of data analysis, and of general statistical concepts such as general linear models.

Aims:
The module will extend students' theoretical and practical knowledge of data analysis, and of general statistical concepts such as general linear models.

Assessable learning outcomes:

By the end of the module, students should be able to:

• show knowledge of the purpose of each statistical technique covered, its assumptions and limitations


• show understanding of the foundations of two strategies that underlie all the statistical techniques - (1) general linear modelling (2) reducing multiple variables to a smaller number of dimensions or components


• choose appropriate techniques from those taught to test hypothese s about provided psychological data


• use R to implement the techniques, and interpret the results

Additional outcomes:
The content of this module will be drawn upon in many parts of the programme, in practical assignments (PYM0EP) and in theoretical or evaluative aspects of other modules.

Outline content:

General linear models. Analysis of variance. Analysis of covariance. Principal component analysis.

Brief description of teaching and learning methods:

1. Directed reading of books and articles, done in advance of associated seminars.


2. Lectures on principles of statistical techniques, their assumptions, purpose and limitations, followed by seminar discussions drawing on the lectures and/or preparatory reading.


3. Self-paced statistical computing practical classes with demonstrator support.

Summative assessment- Examinations:

Summative assessment- Coursework and in-class tests:

Assessment will be by a data analysis assignment of a provided set of data to test specific hypotheses, choosing from statistical methods covered in the module and using R. The written report should justify the methods used, present the results of the analysis, interpret them and comment on the validity of the analyses.

Formative assessment methods:

Penalties for late submission:
Penalties for late submission on this module are in accordance with the University policy. Please refer to page 5 of the Postgraduate Guide to Assessment for further information: http://www.reading.ac.uk/internal/exams/student/exa-guidePG.aspx

Assessment requirements for a pass:

50%

Reassessment arrangements:

If a student fails the assignment, an alternative, equivalent assignment can be submitted.Ìý The assignment and date of submission will be by arrangement with the Module Convenor and/or Programme Director. Students should note however that, given the University regulations on failing credits, it may not be in their interests to resubmit the coursework.

Additional Costs (specified where applicable):
1) Required text books:
2) Specialist equipment or materials:
3) Specialist clothing, footwear or headgear:
4) Printing and binding:
5) Computers and devices with a particular specification:
6) Travel, accommodation and subsistence:

Last updated: 24 September 2020