Undergraduate Module Descriptor

SSI2005: Data Analysis in Social Science 2

This module descriptor refers to the 2021/2 academic year.

Module Aims

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)
This module's assessment will evaluate your achievement of the ILOs listed here – you will see reference to these ILO numbers in the details of the assessment for this module.
On successfully completing the programme you will be able to:
Module-Specific Skills	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)
Discipline-Specific Skills	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
Personal and Key Skills	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.

Module Content

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

This table provides an overview of how your hours of study for this module are allocated:

Scheduled Learning and Teaching Activities	Guided independent study	Placement / study abroad
26.5	123.5	0

...and this table provides a more detailed breakdown of the hours allocated to various study activities:

Category	Hours of study time	Description
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

Online Resources

This module has online resources available via ELE (the Exeter Learning Environment).

Indicative Reading List

This reading list is indicative - i.e. it provides an idea of texts that may be useful to you on this module, but it is not considered to be a confirmed or compulsory reading list for this module.

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.