From questionnaire to defensible analysis — the technical core
The craft underneath every study we run — questionnaire design, sampling, programming, weighting, and analysis — offered as a standalone service for teams that field their own research but want it built to a professional standard.
Who it is for: In-house insight teams, program evaluators, and organizations with data but without dedicated survey methodologists.
Decisions this research supports
- Whether your questionnaire will actually measure what the decision requires
- What sample design gives usable precision within a real budget
- How to weight and clean data so subgroup findings can be trusted
- What an existing dataset — yours or a third party’s — can and cannot support
Capabilities
- Questionnaire design and cognitive pretesting
- Sampling plans and precision estimation
- Survey programming and data collection setup
- Data cleaning and quality control
- Weighting where design or nonresponse requires it
- Cross-tabulation and segmentation
- Driver, regression, and multivariate analysis
- Dashboard-ready outputs
- Analysis of third-party datasets
Typical methods
- Cognitive interviews and pilot testing before full fielding
- Design-based and model-assisted weighting
- Regression and multivariate modeling matched to measurement level
- Reproducible analysis with documented code and decisions
What you receive
- Field-ready programmed questionnaire
- Sampling and weighting specification
- Cleaned, documented datasets
- Analysis outputs: crosstabs, models, and visualizations ready for reporting
Related work
Industries where this work is common
Common questions
Can you improve a survey we already run?
Yes — an instrument review identifies leading questions, scale problems, and coverage gaps, and proposes revisions that preserve trend comparability wherever possible.
What software and formats do you work with?
Deliverables are provided in the formats your team actually uses — spreadsheet workbooks, statistical files, and dashboard-ready extracts — with analysis code documented for reproducibility.
Do you analyze data you did not collect?
Yes, with one condition: the report will state clearly what the dataset’s design allows it to support. Secondary analysis is valuable precisely when its limits are honest.