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: 2021/2

Module Convenor: Prof Kou Murayama
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. Lectures on statistical principles and analysis, with some self-directed activities (not counted for the final mark) to consolidate learning.


2. Directed reading of books and articles on statistical issues not covered by the lectures.


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

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.ÌýÌý

Formative assessment methods:

Students will receive ongoing feedback during the practical sessions,Ìý and are invited to discuss their assignment outcome in a one-to-one meeting.Ìý

Penalties for late submission:

The below information applies to students on taught programmes except those on Postgraduate Flexible programmes. Penalties for late submission, and the associated procedures, which apply to Postgraduate Flexible programmes are specified in the policy 􀀓Penalties for late submission for Postgraduate Flexible programmes􀀔, which can be found here: