Introductory description

QS905 introduces to quantitative methods for the social sciences. It is suitable for all students interested in applied data analysis, from a background in any social science, or at least having an interest in these disciplines. Prior background in quantitative methods before the module may range from none to intermediate.

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: How to design a quantitative research project. Fundamental statistical concepts. How to obtain and manage data. R and RStudio for social statistics.
Week 2: Descriptive statistics and graphs
Week 3: Probability and inference. Bivariate statistics
Week 4: Confidence intervals and significance tests
Week 5: Bivariate regression I
Week 6: Not taught (“reading week”)
Week 7: Bivariate regression II. Research design and causality
Week 8: Multivariate statistics, including Multivariate regression I
Week 9: Multivariate regression II
Week 10: Refining regression models and overview of further methods

Learning outcomes

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

• Conceptualise research problems rigorously;
• Make use of a variety of available data;
• Understand the principles of statistical description and inference, statistical tests, statistical control and OLS regression;
• Select, perform, interpret and present a range of univariate, bivariate or multivariate statistical and graphical methods relevant to their research project;
• Critically engage with quantitative social and political science literature;
• Have some experience of a standard statistical software, such as R.

Indicative reading list

• AGRESTI A. and FINLAY B. 2008. Statistical Methods for the Social Sciences, 4th ed. Upper Saddle River
• BALNAVES M. and CAPUTI P. 2001. Introduction to Quantitative Research Methods. An investigative Approach. Sage
• BARTHOLOMEW D. J. et al. 2008. Analysis of Multivariate Social Science Data. CRC Press [more advanced]
• DE VAUS D. 2002. Analysing Social Science Data. 50 Key problems in Data Analysis. Sage [a problem-based approach [one problem per chapter]
• FIELDING J and GILBERT N. 2006. Understanding Social Statistics, Sage
• FIELD A., MILES J. and FIELD Zoë. 2012. Discovering statistics using R. Sage [big, but assuming absolutely no background in mathematics or statistics, with very detailed explanations and many illustrations]
• FOGARTY B. 2018. Quantitative Social Science Data with R: An Introduction. Sage
• GILL J. 2006. Essential Mathematics for Political and Social Research. Cambridge University Press [a mathematics refresher]
• IMAI K. 2017. Quantitative Social Science: An Introduction. Princeton University Press [a short and gentle introduction]
• KELLSTEDT P. and WHITTEN G. 2009. The Fundamentals of Political Science Research. Cambridge
• KRANZLER. 2011. John H. Statistics for the terrified, Pearson Prentice Hall
• MARSH C. and ELLIOTT, J. 2009. Exploring Data: An Introduction to Data Analysis for Social Scientists. Polity Press
• SAPSFORD R. and JUPP V. 2006. Data Collection and Analysis. Sage
• STINEROCK R. 2018. Statistics with R. A Beginner’s Guide. Sage
• TARLING R. 2009. Statistical Modelling for Social Researchers. Routledge
• TEETOR P. 2011. R Cookbook. O’Reilly.
• TEETOR P. 2011. 25 recipes for getting started with R. O’Reilly.

View reading list on Talis Aspire

Research element

QS905 teaches how to learn and practice quantitative research.

Interdisciplinary

QS905 applies quantitative methods to all social sciences, with some degrre of transferability beyond this domain.

Subject specific skills

Cognitive Skills
Have the ability to read quantitative studies, extract concepts and discuss the results
Be able to connect a research question with appropriate data, tools and research design
Assess and cite published literature

Transferable skills

Key Skills; Subject-Specific/Professional Skills
Know how to plan a basic research project and convince about its relevance
Conduct quantitative research autonomously
Present results in writing in a rigorous manner.