Quantitative consulting service
Advanced models for data that simple analysis cannot represent well
Represent clustered, repeated, censored, latent, nonlinear, missing, or predictive structures without forcing them into a simpler model.
Question→Design→Data→Model→Diagnostics→Interpretation
When this support is useful
When ordinary regression cannot represent the design or the decision requires stronger validation.
- What dependence structure matters?
- How should missingness or censoring be handled?
- What validation matches the intended use?
How the decision is approached
The workflow starts with the research question and data-generating structure. Methods and software follow from that logic, with assumptions, alternatives, and failure modes recorded before conclusions are polished.
- Multilevel and longitudinal models
- SEM, survival, and advanced regression
- Prediction, calibration, and leakage checks
What a defensible handover can include
Deliverables are agreed to fit the decision and the people who must review or continue the work. They are not a fixed bundle of software output.
- Model specification
- Diagnostic record
- Validation and interpretation summary
Start with the decision
Discuss this statistical decision
Bring the research question, design, or existing output. A first review can identify the next statistical decision and the information needed to scope it.