PYM0S1-Data Collection & Analysis 1

Module Provider: Psychology
Number of credits: 10 [5 ECTS credits]
Level:7
Terms in which taught: Autumn term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites: PYM0S2 Data Collection and Analysis 2
Modules excluded:
Current from: 2020/1

Email: k.murayama@reading.ac.uk

Type of module:

Summary module description:

The module will provide a basic understanding of strategies of data analysis and their applications to psychological research. It will ensure that students can apply and interpret core descriptive and inferential statistics, and will enable them to use suitable computer packages ( R) for these purposes.

Aims:

The module will provide a basic understanding of strategies of data analysis and their applications to psychological research. It will ensure that students can apply and interpret core descriptive and inferential statistics, and will enable them to use suitable computer packages ( R) for these purposes.

Assessable learning outcomes:

By the end of the module, students should be able to: show knowledge of the purpose, conceptual basis, assumptions and limitations of core statistical methods and describe, interpret and comment on results of R analyses that use these methods.

Additional outcomes:

The student will gain practical experience of using the package R to manage and present data, and to implement statistical methods covered in the module.ÌýThe content of this module will be drawn upon in many parts of the programme, including module PYM0S2 (the next level of statistical training), in practical assignments (e.g., PYM0EP) and in theoretical or evaluative aspects of other modules.

Outline content:

Principles and practice of core statistical analysis, including descriptive statistics, data transformation, correlation, regression, t-test, hypothesis testing, effect size, confidence intervals and statistical power.Ìý

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.

Summative assessment- Examinations:

Summative assessment- Coursework and in-class tests:

Assessment will be by an open-book test in the week following the completion of the taught part of the course. The test will include short-answer/free format questions on (a) basic understanding of statistical concepts; (b) critical evaluation about the choice and/or the interpretation of statistical results; and (c) interpretation of the R outputs. The focus will be on the understanding the concepts, rather than the computation of statistics.

Formative assessment methods:

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

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