2012 Summer Programme

This course explores the theory and practice of case study as a rigorous methodology for understanding and evaluating the complexity and dynamics of innovative programmes and organisations. The course is designed to be useful for those who wish to commission or conduct case study research or evaluation to inform policy decisions or professional practice. The course will be workshop- and seminar-based, working with examples from the field and practical applications. Participants will be encouraged to share potential case study designs and field work dilemmas throughout the course.
 

This course is designed for those familiar with qualitative research approaches who are interested in using NVivo to assist with the tasks of qualitative data management and analysis. While the course will provide an overview of the full NVivo tool kit, the main focus is on learning basic data analysis tools through hands-on experience. Participants will systematically apply software tools to the tasks of developing a research project including: planning for collection of data, preparation of data for analysis and beginning analysis. They will also be introduced to advanced analysis tools including those for theory building, validation and presentation of findings. Participants will explore applications of the software to their own research projects. Sample data will be provided but participants should bring their own data sets, if they have them, and/or copies of articles and research reports relevant to their field of study.

This course introduces information and data collection methods used by social scientists working on social networks. It familiarises participants with the principal software packages used in social network analysis (UCINET and Netdraw) and provides hands-on experience of working with these packages. Participants carry out a small network project on their own personal community to develop a real familiarity with methods and techniques of social network research and data analysis.
 

The course is designed as an introductory, applied course in the use of Structural Equation Modelling (SEM) using the Amos18 program. SEM is used widely by researchers to find and test complex relationships amongst observed (measured) variables and latent (unobserved) variables and amongst the latent variables themselves.
 
SEM subsumes other analytical techniques such as regression, path analysis, factor analysis, and canonical correlation. Because of its user-friendly graphics interface, Amos is an ideal way to learn the principles of model analyses using SEM.
 

The basic purpose of the course is to provide students will the ability to use SAS to analyse data from complex (including longitudinal) surveys using a range of statistical techniques appropriate to the research question and the nature of the response variable.
 

The aim of the course is to provide the participants with understanding and experience to undertake a basic research project using Stata as the statistical tool. Stata is a comprehensive integrated package for data management, analysis and graphics. Stata version 12 has a comprehensive GUI interface but the course focuses on Stata syntax (i.e. Do files).
 

Mplus is rapidly becoming the program of choice for the analysis of SEMs. Mplus offers a general modelling framework that allows both the modelling of cross-sectional and longitudinal data using observed variables that are a combination of continuous and categorical variables.

In addition, Mplus allows for multilevel modelling structures. Multilevel modelling takes into account that individuals interact with their social contexts. In other words, individuals are influenced by the social groups or contexts to which they belong (e.g. patients in hospitals; voters in electorates; students in classes in schools).

Standard multivariate models are not appropriate for such data because the assumption of independent observations underlying such analyses is not valid. In instances such as these, which are frequent in social and political research, data are nested in a hierarchical manner and multilevel modelling is the appropriate data analysis technique. Mplus also has the capacity to analyse models involving categorical nominal and ordinal dependent variables (e.g. latent class analysis).