Module Catalogue 2022/23

HSC8036 : Health Statistics

  • Offered for Year: 2022/23
  • Module Leader(s): Ms Vicky Ryan
  • Lecturer: Professor Dawn Teare, Dr Jérémie Nsengimana, Dr Michael Grayling, Professor James Wason
  • Other Staff: Dr Theophile Bigirumurame, Dr Thomas Chadwick
  • Owning School: FMS Graduate School
  • Teaching Location: Newcastle City Campus
Semesters
Semester 1 Credit Value: 20
ECTS Credits: 10.0
Pre Requisites
Code Title
HSC8001Fundamentals of Research
Pre Requisite Comment

Prior to, or at the same time as this module, the student should have attended the Fundamentals of Research module or have equivalent skills.

Co Requisites
Co Requisite Comment

N/A

Aims

To explain the relevance of statistical ideas and techniques to health sciences.
To introduce the requirements, advantages and disadvantages of the techniques covered in the module, and hence identify an appropriate statistical technique for a situation.
Enable students to interpret the results of statistical analyses reported in the literature and carry out and interpret simple statistical analyses using appropriate software.

Outline Of Syllabus

This is an introduction to statistical concepts, and their use and relevance in health sciences. The emphasis will be on when to use particular techniques, and how to interpret the results. Students will learn how to apply many of the techniques, and computer practical sessions will reinforce concepts and give practice in carrying out and interpreting statistical analyses. Topics covered are graphical and numerical data summary; the Normal, and Chi-squared distributions; combining probabilities; confidence intervals and hypothesis tests for comparing means and proportions; transformations and non-parametric tests; simple correlation and linear regression; confounding and effect modification; ANOVA and multiple comparisons; survival analysis; simple linear and logistic regression; sample size calculations; MINITAB and EPI INFO commands to perform analyses.

Learning Outcomes

Intended Knowledge Outcomes

By the end of the module, the student will be able to:
•       Understand the relevance of statistical ideas and techniques to health sciences.
•       Know the assumptions necessary, and advantages and disadvantages of the techniques covered in the syllabus.

Intended Skill Outcomes

By the end of the module, the student will be able to:
•       Identify an appropriate statistical technique for a situation.
•       Interpret the results of simple statistical analyses reported in the literature.
•       Carry out simple statistical analyses using appropriate statistical software and interpret the results.

Teaching Methods

Teaching Activities
Category Activity Number Length Student Hours Comment
Scheduled Learning And Teaching ActivitiesLecture231:0023:00Present in person: Lectures including interactive activities
Guided Independent StudyAssessment preparation and completion24:008:00Formative practice activities
Structured Guided LearningLecture materials121:0012:00Non-synchronous online
Structured Guided LearningAcademic skills activities231:0023:00Technical exercises and guided reading
Scheduled Learning And Teaching ActivitiesSmall group teaching31:003:00Present in person: Paper presentations
Scheduled Learning And Teaching ActivitiesSmall group teaching13:003:00Present in Person: Project introduction and preparation in small groups
Guided Independent StudyProject work301:0030:00N/A
Scheduled Learning And Teaching ActivitiesWorkshops101:0010:00Present in Person : Computer practicals - interactive activities around teaching
Scheduled Learning And Teaching ActivitiesDrop-in/surgery181:0018:00Present in person: Scheduled office hours until the project hand in date
Guided Independent StudyStudent-led group activity21:002:00Group paper presentation preparation
Guided Independent StudyIndependent study681:0068:00N/A
Total200:00
Teaching Rationale And Relationship

The lecture materials and linked computer-based practical sessions and group work develop knowledge and module-specific skills. Module and key skills are further developed through the technical exercises and the project which is used as the assessment.

Reading Lists

Assessment Methods

The format of resits will be determined by the Board of Examiners

Exams
Description Length Semester When Set Percentage Comment
Digital Examination601A40PIP Inspera invigilated MCQs
Other Assessment
Description Semester When Set Percentage Comment
Report1M602500 word report on an analysis of a large dataset
Assessment Rationale And Relationship

The MCQs will test students' knowledge and understanding of statistical methods and the ability to interpret the results of analyses. The project will test the students' data analysis and presentation skills in addition to knowledge and understanding.

There are formative activities in the form of exercises (with outline answers) available for each session. There will be practice MCQs throughout the course and a mock MCQ exam.

Timetable

Past Exam Papers

General Notes

N/A

Disclaimer: The information contained within the Module Catalogue relates to the 2022/23 academic year. In accordance with University Terms and Conditions, the University makes all reasonable efforts to deliver the modules as described. Modules may be amended on an annual basis to take account of changing staff expertise, developments in the discipline, the requirements of external bodies and partners, and student feedback. Module information for the 2023/24 entry will be published here in early-April 2023. Queries about information in the Module Catalogue should in the first instance be addressed to your School Office.