Data Analysis in Social Science 2
Module title | Data Analysis in Social Science 2 |
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Module code | SSI2005 |
Academic year | 2021/2 |
Credits | 15 |
Module staff | Dr Lorien Jasny () |
Duration: Term | 1 | 2 | 3 |
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Duration: Weeks | 11 |
Number students taking module (anticipated) | 60 |
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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:
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 activities and teaching methods (given in hours of study time)
Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
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26.5 | 123.5 | 0 |
Details of learning activities and teaching methods
Category | Hours of study time | Description |
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Scheduled Learning and Teaching activity | 16.5 | 11 x 1.5 hour sessions of lectures and demonstration |
Scheduled learning and teaching activity | 10 | 10 x 1 hour computer lab sessions |
Guided independent study | 50 | Time spent in computer lab undertaking data analysis for exercises. |
Guided independent study | 73.5 | Completing required reading for lectures and computer lab sessions; exam preparation |
Formative assessment
Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
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Lab assignments | 4 practical exercises using statistical software to solve problems based on material covered in lecture | 3-4, 6-8, 10-11 | Written |
Summative assessment (% of credit)
Coursework | Written exams | Practical exams |
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60 | 40 | 0 |
Details of summative assessment
Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
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Mid term examination | 40 | 50 minutes | 1-9, 11 | Written |
Final assignment: Guided Data Analysis Essay | 60 | Equivalent to 3,000 words in total | 1-9 | Written |
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Details of re-assessment (where required by referral or deferral)
Original form of assessment | Form of re-assessment | ILOs re-assessed | Timescale for re-assessment |
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Mid-Term Examination | Final Examination (50 minutes) | 1-9, 11 | August/September reassessment period |
Final Assignment | A data analysis exercise that has students conduct their own data analysis | 1-9 | August/September reassessment 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
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.
Credit value | 15 |
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Module ECTS | 7.5 |
Module pre-requisites | SSI1006 (POL/SOC1041) |
Module co-requisites | none |
NQF level (module) | 5 |
Available as distance learning? | No |
Origin date | 11/12/2019 |
Last revision date | 26/08/2020 |