Design research turns questions about people, contexts, and experiences into evidence that can guide responsible design decisions. This course introduces a systematic process for framing research, selecting qualitative methods, conducting fieldwork, synthesizing findings, and communicating actionable insights.
Which statistical comparison matches our study design and data?
What is the difference between a statistically significant result and a practically meaningful one?
How can qualitative evidence help explain the numerical patterns?
Learning Goal
Choose an appropriate t-test or ANOVA for the study design, interpret descriptive and inferential results, and integrate quantitative patterns with qualitative explanations.
Lecture Materials
FC Lecture: Data Analysis #2: Comparing Conditions with T-tests and ANOVA
Analysis Template
Paper Translation Guide · Results Writing
Unlocks when Week 11 class ends
Nov. 13, 4:00 PM (KST)
In-class Activity
In-class Lecture:
Selecting a t-test or ANOVA from the study design
Descriptive statistics, inferential reporting, visualization, and the boundary between results and interpretation
In-class Activity:
Clean the quantitative dataset and select an appropriate analysis
Calculate descriptive statistics and conduct a basic t-test or ANOVA
Create clear figures and connect numerical patterns with qualitative themes
Check null, mixed, and unexpected findings for accurate reporting
In-class Submission:
Results Snapshot · one per team · before class ends
Unlocks when Week 11 class ends
Nov. 13, 4:00 PM (KST)
Homework
FCWatch ‘Data Analysis #1: Finding Patterns in Qualitative Data.’DueNov. 27
HomeworkUse the submitted Results Snapshot to draft the Data Overview and Quantitative Results subsections.View Homework 10 →Due2026.11.27
Key Takeaways
Statistical analysis should follow the study design rather than be selected after seeing the results.
Descriptive statistics and visualizations remain necessary when inferential tests are used.
Statistical significance does not automatically indicate practical importance.
Null, mixed, or unexpected findings are legitimate research outcomes.
Available after class
Unlocks Nov. 20, 4:00 PM (KST), when this class ends.
Related Readings
No required reading
Review the provided statistical reporting examples