Quantitative Data Analysis
| Module title | Quantitative Data Analysis |
|---|---|
| Module code | POLM086 |
| Academic year | 2024/5 |
| Credits | 30 |
| Module staff | Dr Andrei Zhirnov (Convenor) |
| Duration: Term | 1 | 2 | 3 |
|---|---|---|---|
| Duration: Weeks | 11 |
| Number students taking module (anticipated) | 15 |
|---|
Module description
The purpose of the module is to bring your quantitative skills to a level at which you can confidently use quantitative methods in your research in social science. A good understanding of data collection, analysis and interpretation is essential for much empirical research in social science, as well as in the wider world. This module aims to complement other closely linked modules on research methods training in social science to deliver detailed methodological and technical knowledge of a wide range of quantitative analytical techniques. You will learn the strengths and weaknesses of descriptive and inferential statistics, mainly from a classical statistics perspective. The module examines descriptive measures at different levels of measurement (nominal, ordinal, interval and ration) with implications for analysis and are and issues of data collection and its relationship with observational and experimental research designs. The module is a more advanced alternative to POLM809 and, unlike POLM809, will teach you to use R for data analysis.
Module aims - intentions of the module
By the end of a course of practical demonstrations, associated lectures, and practical assignments, this module aims to have significantly developed your skills in the analysis and presentation of quantitative data appropriate to a range of research problems. There will be an emphasis placed upon applying the techniques learned and the practical experience of analysing quantitative data sets with R. You will learn how to construct data sets from individual and aggregate level data. You will then learn how to analyse the data using the appropriate statistical tools. You will learn how to apply techniques in parametric and non-parametric inferential statistics, from simple t- tests for the comparison of means to more complex multivariate statistics, including linear multiple regression. You will also learn techniques for the visual display of data. There will be a brief introduction to analysis of categorical (e.g. binary) data, time series, and panel data to provide a route map for further independent study. You will focus on the analysis of different types of data including survey data and various sources of official data, and their associated problems for analysis.
Intended Learning Outcomes (ILOs)
ILO: Module-specific skills
On successfully completing the module you will be able to...
- 1. recognize and evaluate in writing the diversity of specialized techniques and approaches involved in analysing research information, both quantitative and qualitative;
- 2. critically evaluate in writing the issues involved in application of research design in the context of the social sciences;
- 3. demonstrate acquired skills, confidence, and competence in data analysis;
- 4. demonstrate acquired skills. confidence and competence in a computer package for statistical analysis (e.g. R, SPSS, Stata);
- 5. show ability to present analysed data in a coherent and effective manner.
ILO: Discipline-specific skills
On successfully completing the module you will be able to...
- 6. demonstrate understanding in the use of advanced tools and techniques of quantitative research;
- 7. construct well thought out and rigorous data analysis, tables and reports for both written and oral presentation.
- 8. examine relationships between complex theoretical concepts and real world empirical data.
ILO: Personal and key skills
On successfully completing the module you will be able to...
- 9. develop an advanced ability to study independently
- 10. deliver detailed and nuanced presentations to your peers, and communicate effectively in speech and writing
- 11. use IT for the retrieval and the presentation of information
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: why use quantitative data and
Descriptive statistics, measures of central tendency, graphical presentation
Collecting data, sampling, data management and data integrity
Describing data, graphical presentation of data and dealing with missing data
Inferential statistics and research design
Comparing means
Testing relationships between variables
Multivariate statistics including ordinary least squares regression
Categorical data analysis including logit and probit models
Advanced techniques, including panel data, and paths for future study
Student Presentations
The module will be taught through 11 weekly two-hour sessions (including introductory session). There will be a mix of formal lectures led by the co-ordinator, practical experience, student presentations and student discussion. The emphasis is on active seminar participation, practical experience and the development of techniques and tools with regard to assessed work. The techniques will be explored through appropriate practical work and independent study.
Learning activities and teaching methods (given in hours of study time)
| Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
|---|---|---|
| 22 | 278 | 0 |
Details of learning activities and teaching methods
| Category | Hours of study time | Description |
|---|---|---|
| Scheduled Learning and Teaching Activities | 22 | 11 x 2 hour sessions |
| Guided independent study | 200 | Completion of assessment tasks |
| Guided independent study | 78 | Preparation for seminars |
Formative assessment
| Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|
| Individual presentation of data analysis problem, based on final project | 10 minutes | 10, 11 | 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 |
|---|---|---|---|---|
| Practical assignments + data analysis including results tables and graphs | 45 | 3 x 500 words | 1-9 | Written |
| Final assignment + data analysis including results tables and graphs | 55 | 3,000 words | 9-11 | Written |
| 0 | ||||
| 0 | ||||
| 0 | ||||
| 0 |
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 |
|---|---|---|---|
| Practical assignments + data analysis including results tables and graphs | Practical assignments + data analysis including results tables and graphs | 1-9 | August reassessment period |
| Final assignment + data analysis including results tables and graphs | Final assignment + data analysis including results tables and graphs | 9-11 | August reassessment period |
Indicative learning resources - Basic reading
Diamond, Ian, and Julie Jefferies. 2001. Beginning Statistics. SAGE Research Methods (available via UoE library at https://methods.sagepub.com/book/beginning-statistics).
Feinstein, Charles H., and Mark Thomas. 2002. Making History Count: A Primer in Quantitative Methods for Historians. Cambridge: Cambridge University Press (available via UoE library at https://uoelibrary.idm.oclc.org/login?url=http://dx.doi.org/10.1017/CBO9781139164832).
Hudson, Pat. 2000. History by Numbers: An Introduction to Quantitative Approaches. London: Bloomsbury.
Creswell, John W., and J. David Creswell. 2018. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. Los Angeles: SAGE (available via UoE library at https://app.kortext.com/borrow/254557).
Pollock III, Philip H. 2020. The Essentials of Political Analysis (3rd ed.). Washington, DC: Congressional Quarterly Press (available via UoE library at https://app.kortext.com/borrow/607045).
Fogarty, Brian. 2019. Quantitative Social Science Data with R: An Introduction. Los Angeles: SAGE (available via UoE library at https://app.kortext.com/borrow/369087).
Field, Andy, Jeremy Miles, and Zoë Field. 2012. Discovering Statistics Using R. Los Angeles: SAGE (available via UoE library at https://read.kortext.com/reader/pdf/2726).
Big Book of R, https://www.bigbookofr.com/index.html
Hoover, Kenneth, and Todd Donovan. 2013. The Elements of Social Scientific Thinking. Cengage Learning (available via UoE library at https://www.vlebooks.com/Product/Index/496466).
Additional resources available on ELE – http://vle.exeter.ac.uk/
| Credit value | 30 |
|---|---|
| Module ECTS | 15 |
| Module pre-requisites | None |
| Module co-requisites | None |
| NQF level (module) | 7 |
| Available as distance learning? | No |
| Origin date | 01/12/2013 |
| Last revision date | 25/02/2022 |
