HMPR1003 Introduction to quantitative methods - Evidence for Answers
Aims and Objectives
To prepare you for thinking in a critical way about data and how to answer questions using data. The module is an introduction to probability and the statistical methods used in the analysis of healthcare data. It is designed around teaching the concepts rather than the algebra of statistical methods, with examples drawn from primary sources such as research papers.
Having successfully completed this module you will be able to:
- Understand how probabilities, odds and risks are estimated from healthcare data
- Understand common epidemiological designs for the collection and analysis of data
- Describe and graph healthcare data
- Test quantitative hypotheses using the appropriate statistical methods
- Analyse data using the statistical application SPSS and/or other packages where appropriate
- Critically reflect on published analyses
• Probabilities, odds and risk • Populations, samples and sampling error • Key epidemiological concepts • Describing data • Quantifying risk: absolute risk, relative risk and odds ratios • Screening for health: sensitivity, specificity and predictive value • Hypothesis testing: significance and effect size • Testing hypotheses of association and difference • Statistical power and sample size • Introduction to statistical computing using SPSS and R
Learning and Teaching
Teaching and learning methods
The module is structured around teaching sessions (formal lectures and group activities on the principles of statistics) and computing practicals (data analysis). Typically, the computing practical will follow directly after the relevant lecture and will comprise a guided tutorial followed by some exercises in data analysis. Formative feedback will be a key component of practical activities to ensure that you can move forward in your learning. We will use the statistical applications SPSS and R: you are not expected to have previous experience of these applications, but will be expected to have basic computer (Windows) literacy.
|Practical classes and workshops||11|
|Wider reading or practice||30|
|Preparation for scheduled sessions||44|
|Completion of assessment task||80|
|Total study time||187|
Resources & Reading list
Field A and Hole G (2003). How to design and report experiments.
Pallant J (2007). SPSS Survival Manual: A step by step guide to data analysis using SPSS.
Christine Dancey, John Reidy, & Richard Rowe (2012). Statistics for the Health Sciences: A Non-Mathematical Introduction.
Cambell MJ and Machin D (1990). Medical Statistics: A common sense approach.
Streiner DL and Norman GR (2004). Health Measurement Scales, a practical guide to their development and use.
Everitt BS (1999). Chance Rules: An informal guide to probability, risk and statistics.
Diamond I and Jefferies J (2001). Beginning Statistics: An Introduction for Social Scientists.
Bland M (1995). An Introduction to Medical Statistics.
Field A (2009). Discovering statistics using SPSS.
Lomax RG (2001). An Introduction to Statistical Concepts for Education and Behavioural Sciences.
McKillup S (2005). Statistics Explained. An introductory guide for the life sciences.
Rees DG (1995). Essential Statistics.
Formative Assessment Formative assessment activities and subsequent feedback will be a key component of seminars and workshops. This might involve presenting your ideas verbally or in written form, either as an individual or in a group. This formative assessment is not compulsory but is designed to ensure that you can move forward in your learning and so support the successful completion of your summative assessments. Summative Assessment These assessments are compulsory and linked to whether you pass this module and to your progression on the degree programme.
|Assignment (1500 words)||60%|
|Assignment (2500 words)||100%|
Repeat type: Internal & External