MSc in Policy Analysis and Evaluation: Prospectus
11. Taught Modules
Compulsory modules
Optional modules
Introduction (No credits)
Commissioning and Managing Social Research
Aims
Provide students with the skills required to contract and manage research for government in accordance with UK and EU procurement legislation and in a fashion than ensures the quality and timeliness of the research and its value for money.
Objectives
- Provide an understanding of the key components of managing research for government.
- Provide an overview of UK and EU procurement rules.
- Provide a detailed understanding of the elements associated with good quality research specification and management.
- Provide an overview of project management techniques.
- Provide an overview of risk management.
Ethics: Issues, procedures and practice
Aims
To increase the awareness of ethical issues amongst GSR members in terms of conducting, commissioning and managing social research and to ensure GSR members are aware of the processes and procedures that can be used to manage ethical risk.
Objectives
- Examine the competing ethical issues associated with research participants, research peers, research customers and society at large.
- Critically consider the ethical issues associated with government social research.
- Examine current practice within government and beyond.
- Examine the procedures that can be used to manage ethical risk.
- Ensure awareness of the GSR Professional Guidance document on ethics.
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Research for government (15 credits)
Aims of the module
- To critically examine the role of evidence-based policy making;
- to explore the policy-research relationship and understand the inherent responsibilities and constraints;
- to understand the wider role of research and analysis in government decision making and to critically evaluate how it works in practice;
Intended learning outcomes
After successful completion of the course, students will:
- have a critical understanding of the theory and practice of using research and analysis in policy and delivery in a government context;
- understand their role and their responsibilities in the research-policy relationship and know what guidance and support is available.
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Pre-statistics course
The pre-statistics course is not compulsory for all students and is not covered in the core course fees for the MSc. However, many students will need to attend this course in order to be able to successfully complete the other statistical courses. It has been designed to prepare students for the ‘Statistical Analysis’ and the ‘Sampling Design and Survey Data Collection’ modules by allowing them to familiarise themselves with the principles underlying statistical thinking and practice and the practical considerations behind different statistical concepts. Students will be advised after application whether they need to take this course.
Aims of module
- review the most commonly-used descriptive statistics for summarising survey data: arithmetic mean, proportion/percentage, median, mode, percentile, range, variance, standard deviation
- learn about the Normal distribution and understand its importance in survey data analysis
- critically evaluate data presented in simple tables
- familiarise with the concepts of: sampling distribution, sampling error, standard error, margin of error, variance, bias, levels of measurement
- understand the distinction between random non-random methods of selecting a sample
- appreciate the importance of confidence intervals when generalising data from the sample to the population
- learn how to calculate confidence intervals for means, proportions and differences
- identify and understand common errors when using simple statistics
- review basic algebra
- introduce and familiarise students to one of the main statistical packages for data analysis in social sciences, SPSS.
Intended Learning Outcomes
After successful completion of this module students will be comfortable with the following concepts:
- survey process and levels of measurement
- basic descriptive statistics
- normal distribution
- estimation based on random (probability) samples
- sampling distribution, sampling error and bias
- margin of error and standard error
- confidence intervals for means/proportions and differences
- basic algebra
- Use of SPSS statistical analysis package.
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Statistical Analysis (30 credits)
Aims of module
- to develop a practical understanding of the methods of modelling relationships between variables
- to understand which tests of significance are appropriate and how to carry them out
- to learn about Analysis of Variance, Covariance and Correlation
- to understand the principles and assumptions of regression-based analysis methods such as Linear, Logistic and Multinomial Logistic Regression
- to study the impact of violating analysis assumptions, their diagnosis and solutions
- to understand and practice the use of non-regression based analysis methods such as Principal Component Analysis and Factor Analysis
- to learn the principles of Cluster Analysis methods
Intended Learning Outcomes
After successful completion of this module students will be able to:
- select methods of data analysis appropriate to their data and research questions
- carry out a range of analyses using SPSS: t-test, chi-square, analysis of variance, correlation, linear, logistic & multinomial logistic regression, principal component analysis and factor analysis.
- diagnose whether the assumptions have been adhered to in the analyses covered, discuss the potential impact on the results and propose solutions to any problems that may have arisen
- critically evaluate the data analysis methods proposed or undertaken in a given study
- ensure interpretation of data analysis findings is done correctly and be able to defend the interpretation
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Experimental and quasi-experimental design (15 credits)
Aims of module
- to develop a better understanding of experimental and quasi-experimental methods
- to develop a better understanding of the use of experimental and quasi-experimental methods in government research and evaluation
- to appreciate the strengths and limitations of experimental and quasi-experimental methods of research and evaluation
- to appreciate the relationship between experimental and quasi-experimental designs and other methods of research and evaluation
- to learn to write experimental and quasi-experimental research designs.
Intended Learning Outcomes
After successful completion of this Module, students will be able to:
- understand the main elements of carrying out a randomised control trial
- understand the principles on which four main types of quasi experimental design are based
- interpret the outcome and results of experimental and quasi-experimental research and its quality
- know when it is appropriate to use such research designs in government research.
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Research synthesis for policy and practice (30 credits)
Aims of module
- to develop a critical understanding of the principles and practice of research synthesis
- to develop a protocol for a question-led systematic review
- develop conceptual frameworks and inclusion criteria for reviews
- search for evidence and map systematically the available evidence
- critically appraise primary studies
- plan and appraise statistical analysis for systematic reviews, meta-analysis and other forms of synthesis
- test for heterogeneity and homogeneity in primary studies
- apply principles of data extraction for systematic reviews
- use databases and other systems for managing a review
- analyse and present data for a systematic review
- plan methods to assure the quality of the systematic review process
- be able to identify different types of systematic review including conceptual synthesis
- evaluate the role played by systematic reviews within evidence-informed decision-making in policy and practice.
Intended Learning Outcomes
After successful completion of this course students will:
- have a critical understanding of the purpose of systematic research synthesis and its relevance to evidence informed policy and practice;
- be able to identify a diversity of approaches to synthesis along with principles and decision points central to all;
- be able to develop a protocol for a systematic review to include a review question, conceptual framework, methods for searching, screening, describing, appraising and synthesizing studies, and a strategy for communication and implementation;
- be able to draft a plan for accessing tools and other resources for managing a systematic review;
- have explored the potential for systematic approaches for their work.
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Sampling design and survey data collection (30 credits)
Aims of module
- to appreciate the importance of probability sample designs in terms of both practical and statistical considerations
- learn the concepts of stratification, oversampling, clustering and multistage sampling
- to understand the ways in which survey design affects accuracy of estimation
- to carry out basic calculations for sample size determination
- to acquire a practical understanding of the rationale of weighting for differential selection probabilities as well as non-response
- to be familiar the implications of sampling and weighting when analysing and presenting survey data
- provide students with an understanding of different forms of data collection and data collection techniques and provide an understanding of the different methods of survey data collection that can be used to answer key policy questions;
- provide an understanding of the techniques used in quantitative data collection in order to ensure good quality, robust data.
Intended Learning Outcomes
After successful completion of this module students will be able to:
- critically evaluate the sampling methods proposed or undertaken for a given survey
- comment on the appropriate sample size needed for a particular survey
- understand weighting, its rationale, application and implications in survey analysis and reporting
- design probability samples including stratified, boost, clustered and multi-stage samples
- choose appropriate software to estimate standard errors for complex survey designs including the analysis of weighted survey data
- to develop a better understanding of the differences between qualitative and quantitative data, and their relative strengths and weaknesses
- to introduce computer-assisted methods of data collection (including CASI, CAPI, CATI, online data collection), as well as more traditional postal-data collection and paper and pencil techniques
- to understand how to obtain high quality data, high response rates, low measurement errors
- to develop an understanding of survey questionnaire design
- to understand how surveys vary if they are longitudinal data collection
- to understand the issues around data linking, whereby data from a number of different sources are linked to provide a more comprehensive picture.
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Qualitative research and analysis (30 credits)
Aims of module
- to understand the principles and practices of a range of approaches to qualitative research and evaluation
- to understand how the use of qualitative research can be incorporated in government research and evaluation, and other policy contexts
- to appreciate the strengths and limitations of qualitative research and evaluation, and critically evaluate work of this kind
- to appreciate the relationship between qualitative and quantitative approaches to research and evaluation
- to develop the skills and understandings required to commission and interpret research involving qualitative data collection and analysis
- to appreciate key ethical and political issues in the conduct and dissemination of qualitative research and evaluation.
Intended Learning Outcomes
After successful completion of this course students will be able to:
- understand the theoretical and methodological basis of a range of approaches to qualitative research and evaluation, and how this affects the design and conduct of research and the analysis of data
- assess the strengths and limitations of qualitative research and evaluation in order to use that knowledge in the design, commissioning, application and dissemination of policy relevant research
- assess when to use qualitative as opposed to quantitative methods, and when to combine approaches
- evaluate and analyse qualitative research studies in a critical and informed manner
- commission a project that appropriately uses qualitative methods and conforms to contemporary ethical and other standards
- understand how to carry out a qualitative evaluation of policy initiatives.
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Evaluation methods and economic appraisal (30 credits)
Aims of module
- to develop the student’s understanding of how to design and undertake a quantitative impact evaluation and carry out an economic appraisal of a policy intervention
- to develop students’ understanding of how evaluation methods can be used to assess the impact of policy interventions on outcomes of interest and identify causal effects
- to enable students to critically apply their understanding of evaluation and economic appraisal methods to policy evaluation questions in a variety of fields, including education.
Intended Learning Outcomes
On completion of the module students should be able to:
- understand the concepts and methods required to quantifying the causal impact of a policy intervention or ‘treatment’ on outcomes of interest
- understand the strengths and weakness of the different evaluation approaches and have a clear sense of what methods are appropriate (and inappropriate) for analysing the causal impact of policy interventions
- apply the different evaluation methods learnt in the course to real data using the econometric software package – STATA (students will not be assessed on this outcome)
- design a quantitative policy evaluation that relies on appropriate evaluation methods, with a clear understanding of what the evaluation will and will not be able to identify in terms of causal effect
- critically assess the strengths and weakness of previous policy evaluations.
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Longitudinal research and analysis (30 credits)
Aims of module
- to develop students’ understanding of the evaluation problem and the role of longitudinal research
- to develop students’ understanding of how longitudinal data may be used to model the impact of policy interventions and uncover causal effects
- to develop students’ understanding of the ways of designing longitudinal surveys
- to enable students to apply their understanding of longitudinal data to practical policy problems in a variety of fields, including education and crime.
Intended Learning Outcomes
After studying the module you should be able to:
- design an evaluation using longitudinal data
- apply the different methods of longitudinal analysis covered in the course
- critically evaluate others’ use of these methods
- understand the different ways of designing a longitudinal survey and which are appropriate to particular data collection needs
- conduct valid statistical analysis of longitudinal models using specified data and software packages (e.g.SPSS)
- critically analyse research studies and policy evaluations that have used longitudinal data.
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