Module SOCM034 for 2019/0
- Overview
- Aims and Learning Outcomes
- Module Content
- Indicative Reading List
- Assessment
Postgraduate Module Descriptor
SOCM034: Social Data Science and Policy Analytics
This module descriptor refers to the 2019/0 academic year.
Module Aims
This is the core module for the MSc in Policy Analytics/Applied Social Data Science and aims to equip you with an understanding of the core concepts related to policy analytics and social data science skills to support evidence-based decision making in the policy process. It aims to equip you with a broad range of relevant skills and knowledge, allowing you to formulate research questions and later carry out your own research projects or a consultancy project.
On successfully completing the programme you will be able to: | |
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Module-Specific Skills | 1. demonstrate a comprehensive understanding of the principles of policy analytics and the policy process 2. Demonstrate a critical understanding of the policy cycle 3. demonstrate a clear understanding of the relationship between data analysis and evidence-based decision making 4. Practically apply different social data science techniques and links to policy analysis and evaluation |
Discipline-Specific Skills | 5. Demonstrate comprehensive understanding of the issues posed by evidence based decision-making and policy analysis 6. clearly articulate the principles of research design, causal inference and data quality 7. Employ a range of advanced quantitative social data science methods |
Personal and Key Skills | 8. Demonstrate familiarity with a range of social data science techniques 9. Communicate analysis effectively to a broad audience |
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 |
---|---|---|---|
Practical exercises | 2 short exercises to be completed in class of 15 minutes each | 1-4,8 | Peer and Oral 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 |
---|---|---|---|---|
Data analysis short report 1 | 25 | 2,000 words plus tables, graphs based on data analysis | 1-9 | Written feedback |
Data analysis short report 2 | 25 | 2,000 words plus tables, graphs based on data analysis | 1-9 | Written feedback |
4,000 word research report | 50 | 4,000 words plus tables, graphs based on data analysis | 1-9 | 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 |
---|---|---|---|
Data analysis short report 1 | Practical exercise 1 (2,000 words) | 1-9 | August/September reassessment period |
Data analysis short report 2 | Practical exercise 2 (2,000 words) | 1-9 | August/September reassessment period |
4,000 word research report | 4,000 word research report | 1-9 | 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.
Agresti, A. and Finlay, B. (2014) Statistical methods for social sciences. Upper Saddle Hall, NJ: Prentice Hall (4th edition).
Argyrous, G. (Ed.) (2009). Evidence for policy and decision-making: a practical guide. Sydney: University of New South Wales Press.
Bardach, E. (2011). A practical guide for policy analysis: The eightfold path to more effective problem solving. Washington, DC: CQ Press.
Burtless, G (1995) 'The Case for Randomized Field Trials in Economic and Policy Research'. Journal of Economic Perspectives, Spring 1995, pp 63-84.
Davis, H., Nutley, S. and Smith, P. (Eds.). What works?: evidence-based policy and practice in public services. Bristol: Policy Press.
Dunn, W. N. (2015). Public Policy Analysis (6th ed.). London: Routledge.
Imai, K. (2017). Quantitative Social Science: An Introduction. Princeton, NJ: Princeton University Press.
Layard, R. and Glaister, S. (2003). Cost benefit analysis. Cambridge University Press.
Positer, T.H. (2003). Measuring Performance in Public and Nonprofit Organizations. The Jossey-Bass Nonprofit and Public Management Series.
Wholey, J.S, Hatry, H.P. and Newcomer, K.E. (2004). Handbook of Practical Program Evaluation. Jossey Bass Nonprofit & Public Management Series.