Health and Social Care (School of)
Spring & Summer
Postgraduate: Level 7
Monday 15 January 2024
Friday 22 March 2024
26 September 2023
Requisites for this module
The aim of this module is to introduce students who have no previous experience of quantitative methods to basic and intermediate statistical concepts and procedures. The emphasis is on practical applications, not mathematics although a small amount of elementary mathematics is inevitably required.
The module covers the following themes:
- Principles of data analysis, description and inference, and preparation of data for statistical testing
- Conducting uni- and bivariate analysis, tests of association and tests of difference, multivariate analysis and regression modelling
- Interpreting statistical output
By the end of the module you should be able to:
- Understand the logic of statistical description and inference.
- Know how to interpret basic statistics.
- Conduct elementary and intermediate quantitative data analysis using a statistical software package.
This module will equip students to understand, as well as develop their skills in carrying out a range of statistical tests.
By the end of the module the student will have:
- Knowledge and understanding of the application of univariate and basic multivariate quantitative techniques.
- The ability to prepare data to formulate suitable statistical models for problems.
- The ability to analyse data via suitable statistical models and correctly interpret the results.
- The ability to report the interpretation using appropriate reporting conventions.
- The ability to assess the validity of suitable statistical models, and the associated limitations of using univariate and basic multivariate quantitative techniques.
Understanding statistical findings is key for anyone wishing to pursue an academic or Professional career. All health and social care policies and treatment recommendations should be based on evidence but in order to understand this evidence base you need to be able to understand the results of any given study and be able to ascertain whether the interpretations made on the basis of those results are appropriate. In addition, those wishing to carry out service evaluations and research within a practice based context will need to be able to demonstrate good statistical ability in addition to being able to use appropriate statistical software. The aim of this module is to help students develop practical skills in running and interpreting statistical analysis. Students will begin with basic univariate statistics which will serve as a refresher for those already a little familiar with statistics from undergraduate studies. Then they move on to bivariate and finally multivariate analyses. The basic statistics are analysed using Excel but the majority of the module uses SPSS for the purposes of data analysis. Please note that if insufficient numbers of students (i.e. <10) opt to take this module we may have to cancel it at short notice. If this is the case we will of course notify anyone who has applied to take this course in a particular term as soon as possible and offer an opportunity to discuss alternative options.
There are opportunities for both self-directed online learning and individual face to face tutorials with the module lead. For your study on this module you will be drawing on printed material, a variety of web resources including video clips, and software. You will also engage in online discussion with fellow students. A student copy of SPSS can be obtained from the Softward Hub and you will need access to SPSS and Excel throughout the module.
Pallant, J. (2020) SPSS Survival Manual: A Step by Step Guide to Data Analysis using IBM SPSS
. 7th edition. Milton Keynes: Open University Press. Available at: https://app.kortext.com/Shibboleth.sso/Login?entityID=https://idp0.essex.ac.uk/shibboleth&target=https://app.kortext.com/borrow/615865
Greenhalgh, T. (1997) ‘How to Read a Paper: Statistics for the Non-Statistician. I: Different Types of Data Need Different Statistical Tests’, BMJ: British Medical Journal
, 315(7104), pp. 364–366. Available at: https://www.jstor.org/stable/25175393
Greenhalgh, T. (1997) ‘How to Read a Paper: Statistics for the Non-Statistician. II: “Significant” Relations and Their Pitfalls’, BMJ: British Medical Journal
, 315(7105), pp. 422–425. Available at: https://www.jstor.org/stable/25175446
University of Essex?:: The Albert Sloman Library
(no date). Available at: https://library.essex.ac.uk/home
SAGE - Student and Instructor Site for Discovering Statistics Using IBM SPSS Statistics, Fourth Edition
(no date). Available at: http://www.uk.sagepub.com/field4e/
The above list is indicative of the essential reading for the course.
The library makes provision for all reading list items, with digital provision where possible, and these resources are shared between students.
Further reading can be obtained from this module's reading list
Assessment items, weightings and deadlines
|Coursework / exam
Exam format definitions
- Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
- In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
- In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
- In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary,
for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.
Your department will provide further guidance before your exams.
Module supervisor and teaching staff
Prof Andrew Bateman, email: firstname.lastname@example.org.
Student Programme Administrator: Ashwini Bharambe
Dr Elaine Lehane
University College Cork
Available via Moodle
Of 9 hours, 8 (88.9%) hours available to students:
0 hours not recorded due to service coverage or fault;
1 hours not recorded due to opt-out by lecturer(s), module, or event type.
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