Introductory description

N/A

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 multivariate analysis
Week 2: Multiple regression I: Linear regression
Week 3: Multiple regression II: Logistic regression
Week 4: Multiple regression Ill: Interaction effects
Week 5: Issues in the secondary analysis of large and complex surveys/ Interpreting published articles based on multivariate analyses
Week 6: (Reading week)
Week 7: Concept operationalisation and index construction
Week 8: Hierarchical log-linear models I
Week 9: Hierarchical log-linear models II/
Links to logistic regression
Week 10: Event history modelling: Cox's proportional hazard model

Learning outcomes

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

• Understand the value of, and apply, a number of key multivariate statistical analysis techniques.
• Carry out a competent secondary analysis of survey data on a substantive topic, and demonstrate an enhanced ability to evaluate the merits, limitations and specificities of existing surveys as sources of data.
• Demonstrate a heightened awareness of both the technical and theoretical/conceptual dimensions of quantitative data analysis.
• Apply statistical software to manipulate survey data and analyse it using multivariate techniques.
• Present and interpret the results of multivariate statistical analyses appropriately

Indicative reading list

Dale, A., Fieldhouse, E. and Holdsworth, C. 2000. Analyzing Census Microdata. London: Arnold.
Dale, A., Wathan, J. and Higgins, V. 2008. 'Secondary Analysis of Quantitative Data Sources'. In Alasuutari, P., Bickman, L. and Brannen, J. (eds) The SAGE Handbook of Social Research Methods. London: Sage. [Chapter 31: pp.520-535]
DeVellis, R.F. 2003. Scale Development: Theory and Applications. (2nd edition). London: Sage.
Field, A. 2013. Discovering Statistics Using SPSS (4th edition). London: Sage.
Fielding, J. and Gilbert, N. 2006. Understanding Social Statistics (2" edition) London: Sage.
Foster, J. 2006. `Log-linear Analysis'. In Foster, J. Barkus, E. and Yavorsky, C. Understanding and Using Advanced Statistics. London: SAGE. [Chapter 4, pp47-56].
Garner, R. 2005. The Joy of Stats: A Short Guide to Introductory Statistics in the Social Sciences. London: Broadview Press.
Jaccard, J. 2001. Interaction Effects in Logistic Regression. London: Sage.
Linneman, T.J. 2014. Social Statistics: Managing Data, Conducting Analyses, Presenting Results (Second Edition). London: Routledge.
Marsh, C. and Elliott, J. 2009. Exploring Data: An Introduction to Data Analysis for Social Scientists (2nd edition). Cambridge: Polity Press.
Menard, S. 2001. Applied Logistic Regression Analysis (2nd Edition). London: Sage. (QASS). Pampel, F.C. 2000. Logistic Regression: A Primer. London: Sage.
Roberts, K. 2011. 'Class Schemes and Scales'. In Class in Contemporary Britain (2nd edition). Basingstoke: Palgrave Macmillan. [Chapter 2: pp18-47].
Sturgis, P. 2008. 'Designing Samples'. In Gilbert, N. Researching Social Life (3rd edition). London: SAGE. [Chapter 9, pp165-181].
Tarling, R. 2008. Statistical Modelling for Social Researchers: Principles and Practice. London: Routledge.

View reading list on Talis Aspire

Research element

This is a quantitative research methods module!

Interdisciplinary

The material taught is applicable across various social studies disciplines.

International

The material taught is relevant across different international contexts.

Subject specific skills

Ability to analyse multivariate data to establish mediation and/or moderation.

Practical skills in the secondary analysis of social survey data sources.

Transferable skills

Ability to bring together literature, concepts and data analysis in a research-based project report.

Ability to work independently to generate findings and handle their unpredictability and complexity in relating them to a research question.