This is a general course in data analysis using generalized linear models. It is designed to provide a relatively complete course in data analysis for post-graduate students. Analyses for many different types data are included; OLS, logistic, Poisson, proportional-odds and multinomial logit models, enabling a wide range of data to be modelled. Graphical displays are extensively used, making the task of interpretation much simpler.
A general approach is used which deals with data (coding and manipulation), the formulation of research hypotheses, the analysis process and the interpretation of results. Participants will also learn about the use of contrast coding for categorical variables, interpreting and visualising interactions, regression diagnostics and data transformation and issues related to multicollinearity and variable selection.
The software package R is used in conjunction with the R-commander and the R-studio. These packages provide a simple yet powerful system for data analysis. No previous experience of using R is required for this course, nor is any previous experience of coding or using other statistical packages.
This course provides a number of practical sessions where participants are encouraged to analyse a variety of data and produce their own analyses. Analyses may be conducted on the networked computers provided, or participants may use their own computers; the initial sessions cover setting up the software on lap-tops (all operating systems are allowed).
The main objective of this course is to provide a general method for modelling a wide range of data using regression-based techniques. Participant will be able to select, run and interpret models for continuous, ordered and unordered data using modern graphical techniques.
• Introduction: A system of analysis
• Software: R, Rstudio and the Rcommander.
• Data coding, manilulation and management
• Defining models: Representing research questions
• Analysis: an introduction to generalized linear models
• Interpretation: using effect displays
• Modelling continuous data
• Contrast coding: dealing with categories explanatory variables
• Modelling count data
• Including and interpreting interactions
• Modelling categories (using logit models)
• Modelling ordered categorical variables (proportional odds models)
• Modelling unordered categorical variables (multinomial logit models)
• Exercises modelling categorical variables
• Model diagnostics and data transformations (Box-Cox and Box-Tidwell)
• Variable selection (strategies for dealing with collinearity using limited variable models and multimodel presentations)
There are no pre-requisites for this course as instruction is provided for all techniques. However, it will be of most use to those who are interested in modelling social science datasets (survey and quasi-experimental) and applying graphics to interpret these.
Agresti, A. (1996). An Introduction to Categorical Data Analysis. Wiley.
Fox, J. and Weisberg, S. (2011). An R companion to Applied Regression (second edition). Sage Publications
Harrell, F. E. (2001). Regression modelling strategies. Springer.
Hutcheson, G. & Sofroniou, N. (1999). The multivariate social scientist. Sage Publications.
Hutcheson, G. & Moutinho, L. (2008). Statistical modelling for management. Sage Publications.
This course will be presented by Graeme Hutcheson, who is a lecturer in the Manchester Institute of Education and has published extensively in the the field of regression models and the analysis of social science data.
Booking is now open for the Summer School 2017.
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If you are based at the University of Manchester and your fee is being paid by your department please complete the booking form and contact us to arrange an internal journal transfer.