Module overview
This module provides an introduction to the use of statistical methods for the analysis of quantitative data and their application in public health. This will include descriptive statistics and basic inferential statistics. The emphasis will be on the practical application of statistical methods and the interpretation of results using the statistical software package SPSS. In addition to teaching you when and how to apply appropriate quantitative analytical techniques when carrying out research or evaluating a programme, it will enable you to interpret your own results and those reported by others.
Aims and Objectives
Learning Outcomes
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- Apply the concepts of statistical hypothesis tests.
- Choose and apply appropriate statistical techniques to analyse data.
- Present quantitative research findings appropriately.
- Interpret the related effect size, confidence interval and P value.
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- The fundamental principles of statistical inference and hypothesis testing.
- The difference between the various summary statistics and graphical displays, and how to choose between them.
- The difference between the various statistical techniques, know when to apply them, how to justify the choice of technique and be able to report the results to a publication standard.
- The difference between the various types of data and how to choose between them.
Transferable and Generic Skills
Having successfully completed this module you will be able to:
- Use the statistical software package SPSS to set up a database, enter data, create and manipulate variables and analyse data.
- Identify, extract and interpret the key results from SPSS output.
Syllabus
- Types of data
- Descriptive statistics and graphical presentation
- Sampling from a population
- Hypothesis testing and confidence intervals
- Analysing data with continuous and categorical outcomes
- Correlation
- Linear and logistic regression
- Sample size
- Statistical writing
- Data handling, manipulation and statistical analysis in SPSS
Learning and Teaching
Teaching and learning methods
A variety of methods are used including lectures, reference to online resources, interactive tools for learning and self-assessment, SPSS computer demonstrations and practical SPSS exercises using computers.
| Type | Hours |
|---|---|
| Teaching | 40 |
| Independent Study | 110 |
| Total study time | 150 |
Assessment
Assessment strategy
The assessment includes both formative and summative elements. The formative assessments are SPSS exercises that are self-directed. There are two summative assessments: an online multiple choice quiz (MCQ) and a written data analysis report. The pass mark for the summative written data analysis report and the module is 50%. You must also pass the online MCQ quiz by achieving a mark of 90% or above. If you fail the MCQ quiz you are allowed to take it again until you pass within the specified time period. If you fail the module, you will have the opportunity to submit work at the next referral (re-sit) opportunity. On passing your referral, your final module mark will be capped at 50%.
Formative
This is how we’ll give you feedback as you are learning. It is not a formal test or exam.
Practical assessment
- Assessment Type: Formative
- Feedback: Self-assessed against the worksheets with full instructions and answers
- Final Assessment: No
- Group Work: No
Summative
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Individual written report | 100% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
| Method | Percentage contribution |
|---|---|
| Individual written report | 100% |