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Data Analysis in Social Science 2

Module description

The purpose of the module is to introduce you to regression analysis methods commonly used in political science, sociology and criminology. A good understanding of data collection, analysis and interpretation is essential for much empirical research in social science. Moreover, quantitative methods are becoming increasingly relevant for the competitive professional world. Hence, the module is designed to add to your current understanding of statistical analysis. By the end of the course, you should be able to understand a range of quantitative research methods, including multiple regression analysis, demonstrate competence in performing statistical analyses using popular software packages, apply quantitative methods to real world problems, evaluate their use in published research and employ these methods (where appropriate) in your dissertation.

SSI1006 (POL/SOC1041) is a pre-requisite for this module.

Module aims - intentions of the module

You will learn the strengths and weaknesses of the OLS regression model from a classical statistics perspective. Using a combination of lectures, practical demonstrations and practical assignments, this module aims at developing your skills in the analysis and presentation of quantitative data. Specifically, you will learn how to construct data sets from individual and aggregate level data, how to analyse these data using the appropriate statistical tools – ranging from simple t tests for the comparison of means to more complex multivariate regression analysis - and how to best display summary statistics and estimation results using relevant techniques for the visual – e.g., graphical - display of data. The module will adopt a “hands on” approach, with particular emphasis on applied data analysis and on computational aspects of quantitative social science research

Intended Learning Outcomes (ILOs)

ILO: Module-specific skills

On successfully completing the module you will be able to...

• 1. Recognise and evaluate in writing the diversity of specialised techniques and approaches involved in analysing quantitative data in political science, sociology and criminology
• 2. Use statistical analysis to test research hypothesis
• 3. Present and summarise analysed data in a coherent and effective manner
• 4. Demonstrate acquired skills, confidence and competence in a computer package for statistical analysis (e.g. Excel and R)

ILO: Discipline-specific skills

On successfully completing the module you will be able to...

• 5. Understand and use the tools and techniques of quantitative research for the analysis of political and social data
• 6. Use statistical evidence to empirically evaluate the (relative) validity of political, sociological and criminological theories and hypothesis
• 7. Construct well thought out and rigorous data analysis, tables and reports for both written and oral presentation
• 8. Examine relationships between theoretical concepts with real world empirical data

ILO: Personal and key skills

On successfully completing the module you will be able to...

• 9. Study independently
• 10. Use IT – and, in particular, statistical software packages - for the retrieval, analysis and presentation of information
• 11. Work independently, within a limited time frame, and without access to external sources, to complete a specified task.

Syllabus plan

Whilst the module’s precise content may vary from year to year, it is envisaged that the syllabus will cover some or all of the following topics:

• Review of Inferential Statistics
• Introduction to Bivariate Regression
• Estimation with Regression
• Goodness of fit and R-squared
• Confidence Intervals and Hypothesis Tests
• Residuals and Outliers
• Dummy Variables and Interaction Terms
• Violations of Assumptions
• Multiple Regression I
• Multiple Regression II
• Model Selection Methods

Learning activities and teaching methods (given in hours of study time)

Details of learning activities and teaching methods

Formative assessment

Summative assessment (% of credit)

Details of summative assessment

Details of re-assessment (where required by referral or deferral)

Re-assessment notes

Where you have been referred/deferred as a result of failing or not completing the final assignment to enable you to pass that component of the module’s summative assessment, then you will be asked to undertake an alternative written assignment with a data analysis component. This new written assignment will constitute 50% of the final module mark.

Indicative learning resources - Basic reading

Chatterjee, Samprit, and Ali S. Hadi. 2006. Regression Analysis by Example, 4th Edition. New York: Wiley-Blackwell.

Gujarati, Damodar N, and Dawn C Porter. 2010. Essentials of econometrics, 4th Edition. New York: McGraw-Hill/Irwin.

Fox, John and Weisberg, Sanford. 2011. An R Companion to Applied Regression, 2nd Edition. Sage.

Argesti, Alan and Finlay, Barbara. 2014. Statistical Methods for the Social Sciences, 4th Edition.  Upper Saddle River, New Jersey : Pearson Prentice Hall.

Key words search

Quantitative Data, Regression, Statistics; Econometrics; Political Science; Sociology; Criminology