Training and Events
Statistical Modelling (postponed)
|The University of Edinburgh|
Professor Vernon Gayle is a Co-Director of the ESRC National Centre for Research Methods.
Chrystal Macmillan Building
View in Google Maps (EH8 9LD)
The social world is complex and messy. Statistical models provide a formal approach to evaluate data, test ideas and investigate research questions.
This is a one day workshop on statistical models for social science data analysis. It will introduce the underlying concepts associated with multivariate analysis using statistical models. The workshop will concentrate on models within the generalized linear modelling framework. It will cover linear regression, and models for binary, categorical, ordered categorical and count data. The focus of the workshop will on social science applications, and social science data and research questions will be showcased throughout. The emphasis will be on interpreting outputs (e.g. from data analysis software packages) and understanding published results.
The event is intended to be engaging and informative and there will be audience participation and practical demonstrations.
This is not a practical workshop and it does not provide training in the use of data analysis software. It will however provide a strong theoretical foundation for future engagement at practical workshops that are designed to provide hands-on training in data analysis.
A high level of mathematical ability is not required, but participants should ideally have undertaken an introductory statistics and data analysis course (e.g. a semester long module as part of a Masters degree).
Intermediate (some prior knowledge)
The fee per teaching day is:
Website and registration
Linear regression, The Multivariate World , , , , , , , Generalized Linear Models , , , , , , , Modelling Binary Outcomes , , , , , , , Modelling Categorical Outcome , , , , , , , Modelling Order Categorical Outcomes , , , , , , , Modelling Count Data , , , , , , , More Exotic Models , , , , , , , Choosing and Criticising
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