Advanced Statistics MED5430

  • Academic Session: 2024-25
  • School: School of Health and Wellbeing
  • Credits: 20
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Semester 2
  • Available to Visiting Students: Yes
  • Taught Wholly by Distance Learning: Yes
  • Collaborative Online International Learning: No

Short Description

The use of statistical methods is integral to understanding and undertaking contemporary public health research. This course builds on the statistical concepts and methods introduced in the Master of Public Health introductory core course Introduction to Epidemiology and Statistics and covers 1. the application of more advanced, but commonly used, methods of data analyses and 2. further practical experience of applying these methods to analyse data using an appropriate statistical computing package. 

Timetable

Teaching will include interactive, asynchronous material delivered over 10 weeks. Live lectures and tutorials will be delivered during the 10 weeks.

Requirements of Entry

None

Excluded Courses

MED5021 Advanced Statistics-On Campus

Co-requisites

none

Assessment

1. Written assessment-1500 words (50%) assessing ILOs 1, 3 and 4

2. Set exercise (50%) assessing ILOs 2 - 4

Course Aims

■ To build on the statistical concepts and methods introduced in the Introduction to Epidemiology and Statistics module.

■ To introduce students to the theory and application of more advanced but commonly used methods of statistical data analyses

■ To give students further practical experience of applying these methods to analyse data using a suitable statistical computing package.

Intended Learning Outcomes of Course

On successful completion of this course students will be able to:

1. Critically appraise, apply, and interpret appropriate statistical concepts, principles, methods, and analyses within health research.

2. Use an appropriate statistical analysis package and suitable epidemiological and statistical methods to perform data analyses and produce and critically evaluate statistical output.

3. Critically evaluate the fundamental concepts of causal inference and contrast these with conventional statistical analyses.

4. Apply appropriate statistical methods to address confounding, mediation, moderation, missing data, and non-linearity when analysing health data.

Minimum Requirement for Award of Credits

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.