Module SOC2094 for 2016/7
- Overview
- Aims and Learning Outcomes
- Module Content
- Indicative Reading List
- Assessment
Undergraduate Module Descriptor
SOC2094: Data Analysis in Social Science III
This module descriptor refers to the 2016/7 academic year.
Module Aims
The aim of this module is to introduce you to more advanced quantitative techniques for the analysis of social data that goes beyond simple descriptive statistics. More specifically, we will study different data reduction and classification techniques, such as factor analysis and cluster analysis. After completing this module you will be able to understand and interpret the results of multivariate statistical analysis frequently reported in social science journals and policy papers, as well as independently conduct analysis using R, a statistically oriented programming language. Multivariate statistical analysis is applied for data analysis outside the academia, and employers often value these skills.
| On successfully completing the programme you will be able to: | |
|---|---|
| Module-Specific Skills | 1. carefully interpret and explain the results of multivariate statistical analysis reported in the social science literature; 2. conduct multivariate statistical analysis using selected methods independently at the intermediate level using R; |
| Discipline-Specific Skills | 3. recognize research designs and data where various techniques of multivariate statistical analysis can be applied; 4. explain the results of multivariate statistical analysis in substantive terms and relate them to substantive social science problems; |
| Personal and Key Skills | 5. report the results of statistical analysis in writing in a way that would be understood by non-specialists; and 6. use general-purpose statistical software (such as R) for the analysis of social data at the intermediate level. |
Module Content
Syllabus Plan
Whilst the module’s precise content will vary from year to year, it is envisaged that the syllabus will cover some of the following themes:
- Factor analysis
- Cluster analysis
- Correspondence analysis
- Multidimensional scaling
- Latent class analysis
- Data manipulation in R
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 |
|---|---|---|
| 22.5 | 127.5 | 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 activity | 6 | 6 x 1 hour lectures |
| Scheduled learning and teaching activity | 16.5 | 11 x 1.5 hour computer lab sessions |
| Guided independent study | 77.5 | Reading and preparation for lectures and lab sessions |
| Guided independent study | 50 | Reading, preparation and writing of the statistical report |
Online Resources
This module has online resources available via ELE (the Exeter Learning Environment).
Web based and electronic resources:
http://vle.exeter.ac.uk/
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 |
|---|---|---|---|
| Formative statistical assignments in class | About 20 minutes each | 1-5, 7 | Peer and tutor 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 |
|---|---|---|---|---|
| Final statistical report | 100 | 4,000 words + tables and figures | 1-7 | Written feedback |
| 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 |
|---|---|---|---|
| Statistical report | Statistical report (4,000 words) | 1-7 | August/September 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.
Basic reading:
D.J.Bartholomew et al., Analysis of Multivariate Social Science Data, 2nd ed, Chapman and Hall/CRC (2008).
Supplementary reading:
I.Borg & P.Groenen, Modern Multidimensional Scaling: Theory and Applications, 2nd ed, Springer (2005).
B.Everitt & T.Hothorn, An Introduction to Applied Multivariate Analysis with R, Springer (2011)
M.Greenacre, Correspondence Analysis in Practice, 2nd ed., Chapmand and Hall/CRC (2011).
S.A.Mulaik, Foundations of Factor Analysis, 2nd ed. Chapman and Hall/CRC (2010).
P.Spector, Data Manipulation with R, Springer (2008).
A.Unwin, Graphical Data Analysis with R, CRC Press (2015).
N.Matloff, The Art of R Programming, No Starch Press (2011).
W.Chang, R Graphics Cookbook, O’Reilly (2013).
Basic reading:
Alan Agresti and Barbara Finlay, Statistical Methods for the Social Sciences, 4th ed., Pearson (2009).
D.J.Bartholomew et al., Analysis of Multivariate Social Science Data, 2nd ed, Chapman and Hall/CRC (2008).
Supplementary reading:
John Fox, Applied Regression Analysis and Generalized Linear Models, 2nd ed., Sage (2008).
John Fox and Sanford Weisberg, An R Companion to Applied Regression, 2nd ed., Sage (2011).
Andrew Gelman and Jennifer Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press (2007).
Jeffrey Wooldridge, Introductory Econometrics: A Modern Approach, 5th ed., Cengage (2013).Donald J. Treiman, Quantitative Data Analysis: Doing Social Research to Test Ideas, John Wiley and Sons (2009).
I.Borg & P.Groenen, Modern Multidimensional Scaling: Theory and Applications, 2nd ed, Springer (2005).
B.Everitt & T.Hothorn, An Introduction to Applied Multivariate Analysis with R, Springer (2011)
M.Greenacre, Correspondence Analysis in Practice, 2nd ed., Chapmand and Hall/CRC (2011).
S.A.Mulaik, Foundations of Factor Analysis, 2nd ed. Chapman and Hall/CRC (2010).
P.Spector, Data Manipulation with R, Springer (2008).
A.Unwin, Graphical Data Analysis with R, CRC Press (2015).
N.Matloff, The Art of R Programming, No Starch Press (2011).
W.Chang, R Graphics Cookbook, O’Reilly (2013).
