Policy Analytics: Data Driven Policy Analysis and Evidence Based Decision-making

Module title	Policy Analytics: Data Driven Policy Analysis and Evidence Based Decision-making
Module code	SOCM028
Academic year	2021/2
Credits	30
Module staff	Dr Chris Playford (Lecturer)

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 advanced data mining and analysis 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 data analysis 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 and aims to equip you with an understanding of the core concepts related to policy analytics and the research methods and data analysis 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 them to formulate research questions and later carry out their 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. Develop understanding of the principles of policy analytics and the policy process
2. Develop critical understanding of policy cycle
3. Understand the relationship between data analysis and evidence-based decision making
4. Develop appreciation of different data analysis techniques and links to policy analysis and evaluation

ILO: Discipline-specific skills

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

5. Develop understanding of issues posed by evidence based decision-making and policy analysis
6. Develop understanding of principles of research design, causal inference and data quality
7. Develop understanding of a range of advanced quantitative methods

ILO: Personal and key skills

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

8. Demonstrate familiarity with a range of data analysis techniques
9. Communicate analysis 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:

Seminars:

Policy analytics and evidence based decision making: definition and key themes
The politics of the policy process and evidence-based decision making: The Policy Cycle & Problem Definition
Decision making under uncertainty
Causal Inference and Mechanisms – principles of research design
Types of data for policy analytics: Open, administrative, secondary, primary (to include section on data quality)
Applications of Policy Analytics: Analysis, Evaluation, Cost Benefit & Decision-making
Policy Analytics Techniques I – RCTs and policy interventions
Policy Analytics Techniques II - Meta-analysis; combining evidence and multi-level analysis
Policy Analytics Techniques III – Modelling Uncertainty
Policy Analytics Techniques IV – Time series
Policy Analytics Techniques V – Longitudinal Analysis

Computer Analytics Sessions

Data Sources: Secondary Data Analysis
Data Management & Extraction
Designing Surveys – Qualtrics
Basics of Analysis I – Communicating Evidence
Basic of Analysis II - Regression Discontinuity Designs
Basic of Analysis II – Differences in Differences
Basic of Analysis II – Logit & Probit Models

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

Scheduled Learning and Teaching Activities	Guided independent study	Placement / study abroad
38	262	0

Details of learning activities and teaching methods

Category	Hours of study time	Description
Scheduled Learning and Teaching Activities	24	12*2 hours of lectures, seminars and practical labs lectures cover the main concepts and data analysis skills of the course. (Each 11x2 hour session will entail lecture time plus seminars to discuss the material introduced)
Scheduled Learning and Teaching Activities	14	7 x 2 hour Computer lab sessions - Application of data analysis techniques to be demonstrated with by teaching assistant.
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	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
	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 (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
3,000 word research report	4,000 word research report	1-9	August/September reassessment period

Indicative learning resources - Basic reading

Basic reading:

Argyrous, George, ed. Evidence for policy and decision-making: a practical guide. UNSW Press, 2009.

Alan Agresti, Barbara Finlay. Statistical methods for social sciences. Prentice Hall, 4th edition

Eugene Bardach A Practical Guide for Policy Analysis: The Eightfold Path to More Effective Problem Solving,

Burtless, G (1995) 'The Case for Randomized Field Trials in Economic and Policy Research'. Journal of Economic Perspectives, Spring 1995, pp 63-84.

Dunn, William N. Public policy analysis. Routledge, 2015.

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.

Smith P.C et al (2000) What Works? Evidence Based Policy and Practice in Public Services, Bristol, Policy Press.

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

http://imai.princeton.edu/software/index.html

UK Data Services - https://www.ukdataservice.ac.uk

NCRM - http://www.ncrm.ac.uk

Indicative learning resources - Other resources

There are a range of data sets that will be used in the course:

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

Understanding Society: Waves 1-6, 2009-2015

Office for National Statistics Longitudinal Study, 1971-

Supporting better evidence generation and use within social innovation in health in low- and middle-income countries

All of the above are available at UKDS

Key words search

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

Credit value	30
Module ECTS	15
Module pre-requisites	None
Module co-requisites	None
NQF level (module)	7
Available as distance learning?	No
Origin date	28/11/2016