Statistical Analysis Methods for Epidemiology and Social Sciences
This advanced level course will provide training in some of the common statistical modelling methods used in epidemiological and social sciences research. It consists of lectures followed by practical sessions based on the STATA statistical package. Students will work with example data sets taken from epidemiological surveys. During the practical sessions students work through exercises individually, with the help of tutors.
The course will cover:
- Linear regression (for analysis of continuous outcomes such as blood pressure).
- Logistic regression (for analysis of binary outcomes such as presence or absence of an illness).
- Poisson, ordered and multinominal logistic regression (for count, ordinal and nominal outcomes).
- Survival models (applicable to prospective studies of mortality and disease incidence).
The course will also provide an introduction to some more advanced techniques used in health and social sciences research together with examples of their applications:
- Multilevel models allow the study of influences on health that operate at different levels, such as the study of associations between area characteristics and health of individuals.
- Principal components and factor analysis are data reduction techniques used to form new variables which are linear composites of the original variable in the former models and latent unobserved construct in the latter models.
- Path models and structural equation models allow the testing of complex models involving hypothesised pathways between risk factors and disease.
The course is suitable for students who have a knowledge of basic statistical methods and linear regression analysis. Students should have some prior experience of using a statistical package such as SPSS to gain the most out of the course. An introduction to using STATA will be provided as part of the course. Students who attend the course will also have an opportunity to book a one hour individual consultation with a statistician. Details of how to book this will be provided at the beginning of the course.
Students from outside the Division of Population Health should contact the course tutor, Jenny Head to discuss whether the course is appropriate for them before registering.
Students will need a UCL computer ID to access course materials and take part in the computer practicals.
Researcher Development Framework CategoriesA1) Knowledge base
A2) Cognitive abilities
Course Recommended for
This course is particularly relevant to the following groups:
- Students in Social & Historical Sciences
- Students in Life Sciences
- Students in Medical Sciences
- Students in Population Health Sciences
- Course Tutor - Prof Jenny Head - (Research Department of Epidemiology & Public Health)
- Course Tutor - Dr Mai Stafford - (MRC Unit for Life Long Health and Ageing)
- Course Tutor - Dr Paola Zaninotto - (Research Department of Epidemiology & Public Health)
- Administrator - Ms Kasia Bronk - (Organisational Development)
Registration information will be available in due course.