2016/17 Taught Postgraduate Module Catalogue

DSUR5104M Statistical Methods

10 creditsClass Size: 50

Module manager: Jing Kang
Email: j.kang@leeds.ac.uk

Taught: Semester 1 (Sep to Jan) View Timetable

Year running 2016/17

Pre-requisite qualifications

An undergraduate degree in science, social science, or a clinical subject such as medicine or dentistry.

Applicants with other qualifications who can demonstrate recent study success and relevant work experience may also be considered.

English requirements for candidates for whom English is not their first language:
- IELTS: 7.0 overall with not less than 6.5 in every skill.
- TOEFL: 580 (paper based) and 240 (computer based).
- 94 TOEFL iBT with minimum scores of 20 in listening, 23 in reading, 23 in speaking and 24 in writing.

This module is not approved as an Elective

Objectives

The objectives of this module are to:
- introduce statistical inference as the key tool for investigating diseases in a population;
- introduce a range of hypothesis tests used when studying health and disease in populations;
- develop understanding of when each test should be applied and the conditions required for each test to be valid, leading to choosing the most appropriate hypothesis test for a research question;
- develop understanding of how to soundly interpretation and present statistical hypothesis testing;
- enable the student to critically evaluate the use and interpretation of statistical tests in published literature;
- develop qualities that are appropriate to their future responsibilities to colleagues and society in general. As such, we aim to develop a professional attitude towards statistical inference.

Learning outcomes
Overall

By the end of this module participants should be able to:
- identify the appropriate statistical test to analyse data in a variety of situations;
- check the validity of the assumptions behind this test;
- perform this test using a statistical computer package;
- present their results appropriately;
- interpret the results of their analyses.

Transferable skills

By the end of this module the student should be able to:
- undertake on-line tasks, posting work and commenting on the work of others;
- reflect critically on their progress.

Skills outcomes
Each of the two assignments will reflect student's ability regarding online tasks (as students will perform the assessments from VLE and submit it electronically) and critical view on their progress (as each assessment requires students to state their critical reflection). Also, reflective log will also be another way to reflect student's progress and skills critically, but it will not be summatively assessed.

Syllabus

The module will be delivered by Jing Kang over 2 days, as a blend of online learning and computer practicals.

The module will cover the following subjects:
- Confidence intervals and t-tests;
- analysis of variance;
- chi-squared tests;
- McNemar's test;
- correlation;
- introducton to linear regression;
- Wilcoxon and Mann-Whitney tests;
- Kruskal-Wallis test and Spearman's correlation.

Teaching methods

Delivery type	Number	Length hours	Student hours
Lecture	4	1.00	4.00
Tutorial	4	1.50	6.00
Private study hours			90.00
Total Contact hours			10.00
Total hours (100hr per 10 credits)			100.00

Private study

Module participants will be expected to finish assignment problems and to read relevant material in the recommended reading list provided.

Opportunities for Formative Feedback

This will be done in a number of ways:
- Student attendance and contribution to practicals.
-Module blog which encourage students to ask questions.

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
Assignment	15	50.00
Assignment	15	50.00
Total percentage (Assessment Coursework)		100.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Reading list

There is no reading list for this module

Last updated: 31/03/2017

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