HD·R1·03 Research
Quantitative & Mixed Methods in Design Research
When design research needs numbers to hold up.
Quantitative methods let design researchers test hypotheses and generalise findings across a population with measurable confidence. This course covers survey design, basic statistical analysis, and how to combine quantitative and qualitative methods rigorously within a single research design.
In Development
This course's full module content is being written. The outline below is final, subscribe for updates when it publishes.
LEARNING OUTCOMES
- 01 Design a survey instrument that avoids common wording and bias pitfalls
- 02 Apply basic descriptive and inferential statistics to design research data
- 03 Determine an appropriate sample size for a given research question
- 04 Design a mixed-methods study that integrates qualitative and quantitative data meaningfully
- 05 Distinguish statistical significance from practical, design-relevant significance
- 06 Report quantitative findings honestly, including limitations
- Question wording bias is measurable and controllable, not unavoidable
- Pilot testing a survey catches problems before they contaminate real data
- Descriptive and inferential statistics answer different questions
- Overclaiming generalisation from descriptive statistics alone is a common error
- Underpowered studies risk both false negatives and misleading noise
- Sample size should be planned before data collection, based on the effect size sought
- Integration plan should be specified upfront, not improvised after data collection
- Side-by-side collection without integration wastes the method's real value
- Statistical significance does not guarantee design-relevant importance
- Effect size should be reported and evaluated alongside significance
- Disclosure of limitations is a rigour requirement, not an optional caveat
- Selective reporting of only favourable findings undermines research integrity