## 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:
Module version for: 2017/8

Module Convenor: Dr Kou Murayama

Summary module description:
«p»«p»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 (Microsoft Excel and SPSS) for these purposes.«/p»«/p»

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 (e.g. Microsoft Excel and SPSS) 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 Excel/SPSS analyses that use these methods.

The student will gain practical experience of using the packages Excel and SPSS to manage and present data, and to implement statistical methods covered in the module. These practical skills will not be assessed directly in this module, but may be in the co-requisite module PYM0S2. The 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 statitics, data transformation, correlation, regression, t-test, hypothesis testing, effect size, confidence intervals and statistical power.

Brief description of teaching and learning methods:

1. Lectures on statistical principles and analysis, with some quizzes (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.

Contact hours:
 Autumn Spring Summer Seminars 10 Practicals classes and workshops 10 Guided independent study 80 Total hours by term 100.00 Total hours for module 100.00

Summative Assessment Methods:
 Method Percentage Class test administered by School 100

Other information on summative assessment:

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 SPSS/Excel outputs. The focus will be on the understanding the concepts, rather than the computation of statistics.

Formative assessment methods:

Students are offered a meeting to discuss their exam outcome.

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

Length of examination:

Requirements for a pass:
50%

Reassessment arrangements:
A failed test may be taken again in the Spring term, by arrangement with the Course Director.