Data Analysis in Social Science II

Module titleData Analysis in Social Science II
Module codePOL2077
Academic year2019/0
Module staff

Dr Lorien Jasny (Convenor)

Duration: Term123
Duration: Weeks


Number students taking module (anticipated)


Description - summary of the module content

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.  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

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:


Topic 1: Review of Inferential Statistics

Topic 2: Introduction to Bivariate Regression

Topic 3: Estimation with Regression

Topic 4: Goodness of fit and R-squared

Topic 5: Confidence Intervals and Hypothesis Tests

Topic 6: Residuals and Outliers

Topic 7: Dummy Variables and Interaction Terms

Topic 8: Violations of Assumptions

Topic 9: Multiple Regression I

Topic 10: Multiple Regression II

Topic 11: Model Selection Methods

Learning and teaching

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

Scheduled Learning and Teaching ActivitiesGuided independent studyPlacement / study abroad

Details of learning activities and teaching methods

CategoryHours of study timeDescription
Scheduled learning and teaching activity16.511 x 1.5 hour sessions of lectures and demonstration
Scheduled learning and teaching activity1010 x 1 hour computer lab sessions
Guided independent study50Time spent in computer lab undertaking data analysis for exercises.
Guided independent study73.5Completing required reading for lectures and computer lab sessions; exam preparation


Formative assessment

Form of assessmentSize of the assessment (eg length / duration)ILOs assessedFeedback method
Lab assignments4 practical exercises using statistical software to solve problems based on material covered in lecture3-4, 6-8, 10-11Written

Summative assessment (% of credit)

CourseworkWritten examsPractical exams

Details of summative assessment

Form of assessment% of creditSize of the assessment (eg length / duration)ILOs assessedFeedback method
Mid term examination4050 minutes1-9, 11Written
Final assignment: Guided Data Analysis Essay60Equivalent to 3,000 words in total1-9Written


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

Original form of assessmentForm of re-assessmentILOs re-assessedTimescale for re-assessment
Mid-Term ExaminationFinal Examination (50 minutes)1-9, 11August/September re-assessment period
Final AssignmentA data analysis exercise that has students conduct their own data analysis1-9August/September re-assessment period

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 60% of the final module mark. 


Indicative learning resources - Basic reading

Basic reading:


Gujarati, Damodar.  2009. Basic Econometrics, 5th Edition.  McGraw-Hill.

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

Argesti, Alan and Finlay, Barbara. 1997. Statistical Methods for the Social Sciences, 3rd Edition. Upper.

Additional resources available on ELE –

Indicative learning resources - Web based and electronic resources



Module has an active ELE page

Key words search

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

Credit value15
Module ECTS


Module pre-requisites


Module co-requisites


NQF level (module)


Available as distance learning?


Origin date


Last revision date