Data Analysis in Social Science 1
| Module title | Data Analysis in Social Science 1 |
|---|---|
| Module code | SSI1006 |
| Academic year | 2021/2 |
| Credits | 15 |
| Module staff | Patrick English (Convenor) |
| Duration: Term | 1 | 2 | 3 |
|---|---|---|---|
| Duration: Weeks | 11 |
| Number students taking module (anticipated) | 100 |
|---|
Module description
The purpose of the module is to introduce you to data analytical tools commonly used in social science research. It is designed for students with no previous experience of quantitative methods or statistics. It will provide you with a basic knowledge of the foundations of descriptive statistics and inference, focusing especially on methods for data presentation, description, and visualization. You will also become familiar with statistical software packages (Excel and R) commonly used in academic and workplace settings. Laboratory (Lab) sessions will be used to re-enforce the material covered in the lectures and to help apply the core concepts seen in class to relevant practical problems. Throughout the module, you will link the techniques and methods learned in class to substantively relevant political science questions – e.g., are there differences in corruption levels and its determinants across countries? What are the key determinants of income inequality in the UK?
SSI1005 (SOC1004/POL1008) is a pre-requisite and no co-requisites are required.
Module aims - intentions of the module
This module aims to provide you with an introductory knowledge of data analytical tools, including techniques for both descriptive and basic inferential statistics. It aims to teach you how to read and interpret quantitative information, to construct datasets from individual and aggregate level data, to summarize and present the important quantitative information in an effective and rigorous way, to look for and identify relevant trends and patterns in your data, and to conduct basic hypothesis tests. By the end of the module, you should be able to understand basic quantitative methods, to critically interpret quantitative information, and to conduct basic statistical analyses.
Intended Learning Outcomes (ILOs)
ILO: Module-specific skills
On successfully completing the module you will be able to...
- 1. understand and apply a variety of statistical methods used in quantitative political science research;
- 2. evaluate and contrast alternative quantitative methods based on an understanding of their advantages, drawbacks, and compatibility with the available data and the substantive questions of interest;
- 3. demonstrate acquired skills: confidence and competence in a computer package for statistical analysis (e.g. Excel, SPSS, Stata, R);
ILO: Discipline-specific skills
On successfully completing the module you will be able to...
- 4. read, understand, interpret and evaluate basic statistical analyses in the professional literature;
- 5. use statistical evidence to empirically evaluate the (relative) validity of social science theories and hypotheses;
- 6. construct arguments based on (quantitative) empirical evidence for both written and oral presentation;
- 7. examine relationships between theoretical concepts in social science with real world data;
ILO: Personal and key skills
On successfully completing the module you will be able to...
- 8. study independently;
- 9. communicate effectively in speech and writing;
- 10. use statistical software packages to summarize, analyze, and present statistical information; and
- 11. demonstrate the ability to 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:
- Introduction to data analysis in the social sciences
- Creating data: conceptualization, operationalization, and measurement
- Describing data I: tables and figures
- Describing data II: descriptive “statistics”
- Correlation and dependence
- Randomness and probability
- Sampling and “sampling distributions”
- Statistical inference: confidence intervals
- Hypothesis testing: introduction
- Testing the difference between two means
- Using quantitative methods in politics, sociology and criminology: illustration and examples
Learning activities and teaching methods (given in hours of study time)
| Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
|---|---|---|
| 26.5 | 123.5 | 0 |
Details of learning activities and teaching methods
| Category | Hours of study time | Description |
|---|---|---|
| Scheduled Learning and Teaching Activities | 16.5 | 11 x 1.5 hour sessions |
| Scheduled Learning and Teaching Activities | 10 | 10 x 1 hour computer lab sessions |
| Guided independent study | 50 | Writing up problem sets and completing lab assignments |
| Guided independent study | 45.5 | Reading and preparing for lectures and tutorials, and completing online quizzes |
| Guided independent study | 28 | Web-based activities to familiarise students with statistical software. |
Formative assessment
| Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|
| Lab assignments | 8 statistical software-related activities to be complete during lab sessions | 1-10 | Written |
Summative assessment (% of credit)
| Coursework | Written exams | Practical exams |
|---|---|---|
| 100 | 0 | 0 |
Details of summative assessment
| Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|---|
| Problem set | 40 | 1,500 words | 1-11 | Written |
| Data analysis essay | 50 | 2,500 words | 1-10 | Written |
| Online quizzes | 10 | 3-5 multiple choice questions each week X 10 | 1-11 | Written |
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 |
|---|---|---|---|
| Problem set | A data analysis exercise that reinforces lecture material, from data collection to hypothesis testing (1,500 words) | 1-10 | August/September reassessment period |
| Data analysis | A data analysis essay that demonstrates that ability to analyse and effectively communicate empirical information (2,500 words) | 1-10 | August/September reassessment period |
| Online quizzes | A quiz on key data analysis techniques that reinforce the material presented in lecture, tutorials, and the reading | 1-11 | August/September reassessment period |
Indicative learning resources - Basic reading
Argesti, Alan and Finlay, Barbara. 1997. Statistical Methods for the Social Sciences, 3rd edition. Upper Saddle River, NJ: Prentice Hall.
Ayres, Ian. 2007. Super Crunchers: How Anything can be Predicted. New York: Bantam Dell.
Dalgaard, Peter. 2002. Introductory Statistics with R. New York: Springer.
Dilnot, Andrew and Michael Blastland. 2007. The Tiger That Isn't: Seeing Through a World of Numbers. London: Profile Books Ltd.
Imai, K. 2016. A First Course in Quantitative Social Science. Princeton, NJ: Princeton University Press.
Pollock, P. 2012. The Essentials of Political Analysis. 4th ed. Washington: CQ Press.
| Credit value | 15 |
|---|---|
| Module ECTS | 7.5 |
| Module pre-requisites | SSI1005 (SOC1004/POL1008) |
| Module co-requisites | none |
| NQF level (module) | 4 |
| Available as distance learning? | No |
| Origin date | 11/12/2019 |
| Last revision date | 16/07/2020 |
