Introductory description

QS903-20

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.

Week 1: Introduction to the module, data and software
Weeks 2-3: Topic 1
Weeks 4-5: Topic 2
Week 6: Reading Week
Weeks 7-8: Topic 3
Weeks 9-10: Revision and Data Analysis Workshops

The three topics offered might vary from year to year, depending on staff availability and student demand.  The methods covered will be announced in advance, and they are likely to be drawn from the following list:

Categorical data analysis (e.g. loglinear analysis)
Causal inferences with observational, experimental and quasi-experimental data
Event history analysis
Qualitative Comparative Analysis
Correspondence Analysis
Latent Class Analysis
Multilevel analysis
Panel data analysis
Social network analysis
Bayesian Analysis
Agent Based Simulation
Spatial Analysis

Learning outcomes

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

• understand and appreciate forms of statistical analysis and advanced quantitative approaches
• have an awareness of the value of, and practical experiecience of applying advanced quantitative analysis techniques
• have a heightened awareness of both the technical and theoretical/conceptual dimensions of quantitative data analysis
• carry out a range of advanced statistical analyses using statistical computing software
• 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
• carry out advanced statistical analyses using statistical software

Indicative reading list

Agresti, A. (2007) An Introduction to Categorical Data Analysis. 2nd ed. Wiley.
Angrist, J.D. and Pischke, J-S. (2009) Mostly Harmless Econometrics. Princeton University Press.
Blossfeld, H-P., Golsch, K. and Rohwer, G. (2007) Event History Analysis with Stata. Mahwah.
Dean, L., et al. (2013) Exponential Random Graph Models for Social Networks. Cambridge University Press.
Firebaugh, G. (2008) Seven Rules for Social Research. Princeton University Press.
Gelman, A., ed. (2009) A Quantitative Tour of the Social Sciences. Cambridge University Press.
Gelman, A. and Hill, J. (2007) Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.
Liberson, S. (1987) Making It Count. University of California Press.
Morgan, S.L. and Winship, C. (2007) Counterfactuals and Causal Inferences. Cambridge University Press.
Murnane, R.J. and Willett, J.B. (2011) Methods Matters. Oxford University Press.
Powers, D.A. and Xie, Y. (2008) Statistical Methods for Categorical Data Analysis. 2nd ed. Howard House.
Singer, J.D. and Willett, J.B. (2003) Applied Longitudinal Data Analysis. Oxford University Press.
Snijders, T.A.B. and Bosker, R.J. (2012) Multilevel Analysis. Sage.
Wasserman, S. and Faust, K. (1995) Social Network Analysis. Cambridge University Press.

Subject specific skills

Key Skills; Subject-Specific/Professional Skills

• carry out a range of advanced statistical analyses using statistical computing software
• 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
• carry out advanced statistical analyses using statistical software
• provide in-depth interpretation about advanced quantitative analysis
  critique and critically engage published quantitative research

Transferable skills

Cognitive Skills

• have a heightened awareness of both the technical and theoretical/conceptual dimensions of quantitative data analysis