58吃瓜

PP5007 Survey Design: Measurement and Inference for Policy

Academic year

2026 to 2027 Semester 2

Further information on which modules are specific to your programme.

Key module information

SCOTCAT credits

15

The Scottish Credit Accumulation and Transfer (SCOTCAT) system allows credits gained in Scotland to be transferred between institutions. The number of credits associated with a module gives an indication of the amount of learning effort required by the learner. European Credit Transfer System (ECTS) credits are half the value of SCOTCAT credits.

SCQF level

SCQF level 11

The Scottish Credit and Qualifications Framework (SCQF) provides an indication of the complexity of award qualifications and associated learning and operates on an ascending numeric scale from Levels 1-12 with SCQF Level 10 equating to a Scottish undergraduate Honours degree.

Availability restrictions

Limited to Master of Public Policy Students

Planned timetable

Mon

This information is given as indicative. Timetable may change at short notice depending on room availability.

Module coordinator

Prof D A Jaeger

This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module Staff

Prof David Jaeger

This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module description

This module develops students鈥� ability to design, measure and interpret survey data for public policy, with a focus on applied decision-making and ethical evidence use. Building students' prior training in Python and statistics, students learn how to operationalise policy-relevant concepts, design survey instruments, and diagnose data quality using the Total Survey Error framework. The module emphasises hands-on experience with professional survey tools and modern modes of data collection, including online and panel-based surveys. Students conduct descriptive and causal analysis using Python and learn to evaluate bias, uncertainty and representativeness in real-world policy data. Particular attention is given to issues of consent, privacy, power and inclusion, and to the responsible communication of survey evidence in policy contexts.

Relationship to other modules

Pre-requisites

BEFORE TAKING THIS MODULE YOU MUST PASS PP5001 AND PASS PP5003

Assessment pattern

Coursework= 100%

Re-assessment

Coursework= 100%

Learning and teaching methods and delivery

Weekly contact

2 hour lecture (x 11)

Intended learning outcomes

  • Demonstrate critical understanding of key principles of survey design and measurement, including operationalisation of policy-relevant concepts, question wording, and construct validity.
  • Design and implement a policy-focused survey instrument using professional survey software, with appropriate attention to sampling, ethics, and data quality.
  • Apply quantitative methods in Python to analyse survey data, including descriptive analysis, regression and embedded experimental designs.
  • Critically evaluate sources of bias, uncertainty and missing values in survey data, and assess how these affect interpretation and policy conclusions.
  • Communicate survey-based findings in a clear and policy-relevant manner.
  • Demonstrate the ability to critically appraise and commission survey-based research for policy purposes, including assessing the suitability of survey methods relative to alternative data sources and policy questions.