Module POLM809 for 2019/0
- Overview
- Aims and Learning Outcomes
- Module Content
- Indicative Reading List
- Assessment
Postgraduate Module Descriptor
POLM809: Applied Quantitative Data Analysis
This module descriptor refers to the 2019/0 academic year.
Module Aims
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
On successfully completing the programme you will be able to: | |
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Module-Specific Skills | 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. |
Discipline-Specific Skills | 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; |
Personal and Key Skills | 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. |
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: 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 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 |
---|---|---|
14 | 136 | 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 Activities | 14 hours | 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 |
Online Resources
This module has online resources available via ELE (the Exeter Learning Environment).
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 |
---|---|---|---|
Performance in sessions | Weekly | 1-11 | Verbal Feedback |
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 |
---|---|---|---|---|
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 |
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 |
---|---|---|---|
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 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.
Basic reading:
Making History Count: A Primer in Quantitative Methods for Historians (2002, Cambridge) by Charles H. Feinstein and Mark Thomas
History by Numbers: An Introduction to Quantitative Approaches by Pat Hudson
Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (2003, Sage) by John W. Creswell
Discovering Statistics Using SPSS (Introducing Statistical Methods S.) (2005, Sage) by Andy Field
Research Methods in the Social Sciences w/Data Bank CD (2007, Worth Publishers)
by Chava Frankfort-Nachmias and David Nachmias
The Elements of Social Scientific Thinking (9th edition, Thomson Learning) by Kenneth Hoover and Todd Donovan
Additional resources available on ELE – http://vle.exeter.ac.uk/