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Large-scale Surveys

Qualitative research tells you what's happening, but not how widespread it is. I help teams build the quantitative foundation that makes related research more focused and decisions more confident.


Large-scale surveys that give teams a quantitative foundation for confident decisions and sharper qualitative research.

  • Rigorous survey design that measures what it's supposed to measure
  • Validated usability scales that make findings comparable across studies
  • Quantification of user needs, behaviours, and usability trends
  • Segmentation by persona, cohort, or user group
  • Statistical analysis and reporting

Design for measurement integrity

A small wording change can undermine an entire study. I design surveys with meticulous attention to what is actually being measured.

  • Define research questions tied to specific product or design decisions
  • Select and apply validated usability scales where appropriate
  • Follow best practices in question wording, order, and structure to encourage high response rates and quality

Run and analyze the survey

Surveys paired with web analytics and prior research tell a more complete story than either can alone.

  • Conduct statistical analysis to identify trends, patterns, and segment differences
  • Cross-reference findings with web analytics and other quantitative data sources

Shape what comes next

Valuable survey data enables confident decisions in product directions and further research strategy.

  • Use findings to inform preliminary product and design decisions
  • Identify potential pain points to probe further in interviews or usability studies
  • Cross-reference survey themes with qualitative findings to build confidence across methods

  • Survey instruments built on validated usability scales
  • Statistical analysis reports with segmented findings
  • Preliminary product and design recommendations

When you're ready to track how design changes affect those baselines, usability benchmarking closes the loop.

Building a survey dataset that a design team could trust

A national mixed-methods survey of 384 adults, run online and on paper, hit an influx of fraudulent responses. The fix was a modular Python ETL pipeline with validation as its own auditable stage, ahead of any cleaning, that turned into a design toolkit for remote healthcare technology.

Read the case study