MPH Master of Public Health (Environment and Public Health)
Year of entry: 2021
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Course unit details:
Practical Statistics for Population Health
|Unit level||FHEQ level 7 – master's degree or fourth year of an integrated master's degree|
|Teaching period(s)||Semester 2|
|Offered by||Division of Population Health, Health Services Research and Primary Care|
|Available as a free choice unit?||No|
This course is relevant to current or future professionals whose careers will involve either conducting quantitative research or interpreting the findings of quantitative research studies. Statistical analysis of data is a key part of research and many research findings and recommendations are based on the results of statistical analysis. An awareness of statistical methods and the ability to interpret data from published studies is invaluable in a career in public health.
The aim of this course unit is to provide students with an understanding of statistics that they can apply within their own professional practice. This could include conducting quantitative research, interpreting the findings of quantitative research studies or applying statistical thinking to public health practice. The course will teach you how to conduct statistical analyses using a statistical package (SPSS or R).
On completion of this unit, successful students will be able to:
- Apply statistical thinking when conducting or reviewing research in professional practice.
- Demonstrate an understanding of the relationship between populations, samples and variability in research studies.
- Define different types of data and demonstrate an understanding of confidence intervals and the normal distribution.
- Perform correlation and simple linear regression and interpret the results.
- Construct and interpret multiple regression models and logistic regression models demonstrating an understanding of confounding.
- Demonstrate the use of methods for statistical inference.
- Perform and interpret survival analyses.
- Use a statistical package to analyse a data set
Introduction to statistical thinking
Types of data
Populations and sampling, variability and sample size
The normal distribution and confidence intervals
Correlation and simple linear regression
Statistical inference for continuous and categorical data
Statistics in Practice
Teaching and learning methods
Online distance learning with course materials provided via the virtual learning environments Blackboard and Articulate Rise. The course consists of 10 topics and within each topic there is a self-test to complete. There are weekly discussion board topics and the discussion boards are moderated by the course unit leader and teaching assistants. The core text is referenced in each topic, and although you should be able to complete the topic adequately without the core text book we recommend that you obtain a copy as it will help you gain a deeper understanding of the subject. The course can be seen as a tutorial in using a statistical analysis package (SPSS or R) and includes demonstrations of how to carry out statistical tests in these packages.
- Analytical skills
- Students will develop their analytical skills by learning how to conduct statistical analyses using a statistical package and how to interpret the results of their analysis.
- Problem solving
- Students will develop problem solving skills through developing their skills in statistical thinking.
- Students will develop skills in conducting quantitative research and interpreting the findings of quantitative research studies.
Weighting within unit (if relevant)
Further opportunities for formative feedback (on non-assessed work) will also be provided during a course unit.
|Independent study hours|
|Islay Gemmell||Unit coordinator|
For further information please watch this video from our Course Unit Leader: https://video.manchester.ac.uk/faculties/edfa2331ca0cd9a14d717cb1d233466f/ca24dc84-09b1-41b6-827e-d852d22d2ee8/
If you have any questions about the content of this unit, please contact the course unit leader, Isla Gemmell, via email on firstname.lastname@example.org. If you have any other queries, please contact the PGT programme administrators via email on email@example.com