# MPH Occupational Health

Year of entry: 2024

## Course unit details:Practical Statistics for Population Health

Unit code POPH60982 15 FHEQ level 7 – master's degree or fourth year of an integrated master's degree Semester 2 No

### Overview

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 important for a career in public health.

Students choosing Practical Statistics for Population Health will need to be available for the face to face/online teaching sessions that will be in the timetable. These will be delivered on campus at the Univeristy of Manchester as well as online. This is an interactive online course. Students musts work through the online course material. Students are encouraged to use the Blackboard discussion boards to ask questions and check their understanding of the course material.

### Pre/co-requisites

This unit is mandatory for the Dental Public Health stream.

### Aims

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).

### Learning outcomes

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

### Syllabus

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

Multiple regression

Logistic regression

Statistical inference for continuous and categorical data

Survival Analysis

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.

### Employability skills

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.
Research
Students will develop skills in conducting quantitative research and interpreting the findings of quantitative research studies.

### Assessment methods

Written assignment (statistical analysis of data set). Final assignment worth 100% of marks – 2500-3000 words or equivalent.

### Feedback methods

Students will be provided with personalised feedback for their final summative assignment within 20 working days.

Further opportunities for formative feedback (on non-assessed work) will also be provided during a course unit.

### Study hours

Independent study hours
Independent study 150

### Teaching staff

Staff member Role
Islay Gemmell Unit coordinator