Introductory description

Advanced techniques of data analysis contribute to understanding and explaining social and political phenomena as well as to solving social problems. The module is designed to enhance students' ability to identify and prepare data, carry out a range of analyses and report the findings in a rigorous way. It will provide a firm foundation on techniques such as factor analysis, logistic regression, and multilevel modelling using real-world data. By the end of the module students will be able to identify, address and report on substantive research questions using a range of techniques. A one-hour lecture will explain the methods that you will apply in the two-hour seminar that follows.

Module web page

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

Term 1
Week 1 Introduction to the module: Reflections on learning to date, structure of the module, theory driven empirical research, data source and documentation.
Week 2 Factor analysis I: Introducing factor analysis, interpreting and reporting the results.
Week 3 Factor analysis II: Introducing factor analysis, interpreting and reporting the results.
Week 4 Structural equation modelling I: Introducing structural equation modelling, interpreting and reporting the results.
Week 5 Structural equation modelling II: Introducing structural equation modelling, interpreting and reporting the results.
Week 6 Reading week.
Week 7 Panel models I: Introducing panel models, interpreting and reporting the results.
Week 8 Panel models II: Introducing panel models, interpreting and reporting the results.
Week 9 Linear probability, logit and probit model I: building the models and interpreting the results.
Week 10 Linear probability, logit and probit model II: reporting the results.

Term 2
Week 1 Recap of term 1; theory driven empirical research, and logistic/logit regression..
Week 2 Models for nominal outcomes I: Introducing the multinomial logit model, interpreting and reporting the results.
Week 3 Models for nominal outcomes II: Testing theoretically relevant model assumptions and model modifications.
Week 4 Models for ordinal outcomes I: Introducing the ordinal logit model, interpreting and reporting the result.
Week 5 Models for ordinal outcomes II: Testing theoretically relevant model assumptions and model modifications.
Week 6 Reading Week.
Week 7 Introducing multilevel modelling.
Week 8 Building a multilevel model I: Comparing groups and random intercept models.
Week 9 Building a multilevel model II: Random slopes/coefficient models and contextual effects.
Week 10 Multilevel models for binary data and outlook.

Learning outcomes

By the end of the module, students should be able to:

• To understand the basic principles of advanced quantitative methods
• To gain practical experience of applying advanced methods to real-world data using statistical software
• To appreciate the context in which advanced quantitative methods are best applied

Indicative reading list

Acock, A. (2013). Discovering Structural Equation Modeling Using Stata. College Station, Texas: Stata Press.
Andress, H.-J., Golsch, K., and Schmidt, A.W. (2013). Applied Panel Data Analysis for Economic and Social Surveys. Berlin: Springer.
Child, D. (1970 / 2006). The Essentials of Factor Analysis. New York: Continuum International Publishing.
Long, J.S., and J. Freese (2014). Regression Models for Categorical Dependent Variables Using Stata. 3rd Edition. College Station, Texas: Stata Press.
Rabe-Hesketh, S., and A. Skrondal (2012a). Multilevel and Longitudinal Modeling Using Stata (Third Edition). Volume I: Continuous Responses. College Station, TX: Stata.
Rabe-Hesketh, S., and A. Skrondal (2012b). Multilevel and Longitudinal Modeling Using Stata (Third Edition). Volume II: Categorical Responses, Counts, and Survival. College Station, TX: Stata.

Research element

Students work on own data analysis projects

Interdisciplinary

Interdisciplinary module for students in the social sciences

Opportunities for interdisciplinary learning are communicated to students

Subject specific skills

Systemtaic understanding of forms of statistical analysis and advanced quantitative approaches

Awareness of the value of, and practical experiecience of applying advanced quantitative analysis techniques

Heightened awareness of both the technical and theoretical/conceptual dimensions of quantitative data analysis

Ability to manipulate and to analyse secondary survey data using statistical computing software, and to present and interpret the results of these analyses appropriately at an advanced level

Transferable skills

By developing and conducting own analyses ...

the exercise of initiative and personal responsibility,

decision-making in complex and unpredictable contexts in data analysis.