9am - 5:45pm
Monday 11 February - Wednesday 13 February 2019
Statistical Modelling in R
This course will demonstrate how quantitative analysis techniques can be used to leverage this data and answer complex questions about the social world.
We live in a world where large quantities of data are regularly collected about people, institutions, and social structures. This course will demonstrate how quantitative analysis techniques can be used to leverage this data and answer complex questions about the social world. Questions like ‘why some people are more at risk of crime than others?’, ‘what explains differences in life expectancy between countries?’, and ‘do gender inequalities persist in the workplace’.
Throughout the course, the emphasis is on the underlying principles and uses of statistical models and not on the mathematical and statistical theory. It therefore gives participants a solid empirical grounding to be able to critically evaluate the findings from a wide range of quantitative social science research. In the accompanying workshops you will get hands on experience of estimating a number of different statistical models in R, engaging with important issues including how to select an appropriate model, assessing the adequacy of a fitted model (in comparison to alternative models) and the statistical and substantive interpretation of the results
This course discusses some of the main statistical models available to researches in social sciences, combining this with practical model building experience and critique using R. Indicative content includes:
- Designing and building statistical models to answer social science questions
- The general linear model
- Operationalising concepts and selecting variables
- Interpreting results and finding the narrative
Hands on practical workshops will provide students with experience of:
- Linear regression
- Logistic regression
- Multinomial regression
- Poisson regression
- Interaction effects and nonlinear relationships
- Models for spatial data
On successful completion of this course, participants will be able to:
- Have a critical awareness of the rationale and terminology of statistical modelling (C)
- Be able to engage with existing quantitative research, highlighting its key strengths and weaknesses (C,K)
- Have a comprehensive understanding of the logic of model development and testing (C,K)
- Be able to develop multiple regression, logistic regression, multinomial logistic and poisson regression models and critically evaluate the results (P,T)
- Be able to clearly tabulate and present the results of regression outputs (P,T)
Key: C-Cognitive/Analytical; K-Subject Knowledge; T-Transferable Skills; P- Professional/ Practical skills
Professor Brunton-Smith is a quantitative social scientist with particular expertise in multilevel modelling, survey methodology, and missing data. He has more than 10 years experience teaching statistical modelling.
Level of study
Intermediate (some basic knowledge of simple linear regression will be an advantage)
Varies according to status:
- £595 - Government/commercial sector
- £495 - Educational/charitable sector
- £395 - Students.
None. In the first workshop we will examine the models used in the following paper: Allum, N., Besley, J., Gomez, L., and Brunton-Smith, I. (2018) 'Disparities in science literacy'. Science, 360 (6391), 861-862. (to send).
For participants unfamiliar with R, the following introductory materials will be of use
No prior knowledge is required, but it is assumed that participants will have a basic understanding of regression. All computing workshops will be in R, using the GUI RStudio. For a basic introduction to R for data manipulation and analysis, please click here.
Participants on the course will include some students completing the MSc in Social Research Methods