Module POL1041 for 2018/9
- Overview
- Aims and Learning Outcomes
- Module Content
- Indicative Reading List
- Assessment
Undergraduate Module Descriptor
POL1041: Data Analysis in Social Science
This module descriptor refers to the 2018/9 academic year.
Module Aims
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.
| On successfully completing the programme you will be able to: | |
|---|---|
| Module-Specific Skills | 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); |
| Discipline-Specific Skills | 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; |
| Personal and Key Skills | 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. |
How this Module is Assessed
In the tables below, you will see reference to 'ILO's. An ILO is an Intended Learning Outcome - see Aims and Learning Outcomes for details of the ILOs for this module.
Formative Assessment
A formative assessment is designed to give you feedback on your understanding of the module content but it will not count towards your mark for the module.
| 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
A summative assessment counts towards your mark for the module. The table below tells you what percentage of your mark will come from which type of assessment.
| Coursework | Written exams | Practical exams |
|---|---|---|
| 100 | 0 | 0 |
...and this table provides further details on the summative assessments for this module.
| Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|---|
| Problem set | 40 | 2500 words | 1-11 | Written |
| Data analysis essay | 50 | 3500 words | 1-10 | Written |
| Online quizzes | 10 | 3-5 multiple choice questions each week X 10 | 1-11 | Written |
| 0 | ||||
| 0 | ||||
| 0 |
Re-assessment
Re-assessment takes place when the summative assessment has not been completed by the original deadline, and the student has been allowed to refer or defer it to a later date (this only happens following certain criteria and is always subject to exam board approval). For obvious reasons, re-assessments cannot be the same as the original assessment and so these alternatives are set. In cases where the form of assessment is the same, the content will nevertheless be different.
| Original form of assessment | Form of re-assessment | ILOs re-assessed | Timescale for re-assessment |
|---|---|---|---|
| Problem set (2500 words) | A data analysis exercise that reinforces lecture material, from data collection to hypothesis testing (2500 words) | 1-10 | August/September re-assessment period |
| Data analysis (3500 words) | A data analysis essay that demonstrates that ability to analyse and effectively communicate empirical information (3500 words) | 1-10 | August/September re-assessment 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 re-assessment period |
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.
Imai, Kosuke. 2016. A First Course in Quantitative Social Science. Princeton, NJ: Princeton University Press.
Argesti, Alan and Finlay, Barbara. 1997. Statistical Methods for the Social Sciences, 3rd edition. Upper
Saddle River, NJ: Prentice Hall.
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.
