Research and Evaluation Design and Methods
Graduate course examining applied research and evaluation design, covering research process components like measurement and sampling, practicing techniques such as surveys and interviews, and culminating in a research proposal.
Summary
What you’ll be able to do
Become familiar with the concepts, language, methods, and applications of research and evaluation
Formulate clear research questions
Identify an appropriate research design to answer research questions and contribute to generalizable knowledge
Assess the strengths and weaknesses of the research design in any given study
Learn and practice a range of research techniques
Incorporate anti-oppression practice into research studies
Write a detailed, feasible, and rigorous research proposal
Source: UW Guide
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Prerequisites
Course mapGraduate/professional standing
- Graduate/professional standing
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- Graduate/professional standing
Professors
Spring 2027No instructors recorded for this selection.
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Calendar & sections
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Student experience
the class
Examines applied research and evaluation design. Explains the basic components of the research process: conceptualization and measurement, sample selection, and techniques for data generation. Explores and practices research techniques, including survey, focus groups, interviews, etc. Entails a developmental sequence of assignments culminating in a research proposal.
difficulty & workload
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Topics
- Research process components: conceptualization, measurement, sampling, and data generation
- Specific research techniques: surveys, focus groups, and interviews
- Anti-oppression practice in research
Skills
- Conceptualization, measurement, sample selection, and data generation techniques
- Practical application of surveys, focus groups, and interviews
- Writing a detailed, feasible, and rigorous research proposal
- Integrating anti-oppression practice into research studies
Grades
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Catalog & offerings
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Grade history
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Explore recorded gradesRecorded 2026-10-14
Model outputs & technical records
LLM outputs across runs
Full model traces
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Model & dataset provenance
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"observed_at": "2026-10-14 17:50:09.728220+00:00"
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