Statistical Programming
Point-and-click analysis works until your data changes, your reviewer asks for a variation, or you need to run the same process next quarter. Statistical programming solves that: we write scripts and pipelines in R, Python, SAS, and Stata that you — or we — can rerun, audit, and extend, instead of starting from scratch every time.
Programming - Code You Can Reuse, Not Just a One-Time Report
Statistical programming sits a level below standard statistical analysis: instead of running a test once through a menu-driven interface, we write the code that performs it — which means the same process can be applied to next month's data, handed to your team, or adjusted without redoing the whole analysis by hand. This matters most for projects with recurring data, complex custom models, or a need to document exactly how a result was produced, step by step, for review or replication.
We build this code the way we'd want to receive it ourselves: commented, structured, and organized so someone other than the original author can pick it up.
What statistical programming covers
Data cleaning & preparation pipelines
- Scripted, repeatable processes for handling missing data, recoding variables, merging datasets, and validating structure, and cleaning doesn't have to be redone by hand each time.
- Custom statistical models
- Regression, mixed-effects, time-series, and other models built specifically for your data structure, beyond what a default software menu offers.
- Simulation studies
- Monte Carlo simulations and other computational methods for testing statistical properties, power, or robustness before or alongside real-data analysis.
- Automated & reproducible reporting
- Scripts that generate the same report format automatically as new data comes in — useful for recurring surveys, dashboards, or ongoing research.
- Syntax handover & documentation
- Clean, commented code delivered with documentation, so your team can run, modify, or extend it independently once the project wraps.
- Algorithm & workflow translation
- Converting analysis built in one program (e.g., SPSS syntax) into another (e.g., R or Python) when your workflow or collaborators require it.
Languages and Software we program in
- R — the most common choice for custom statistical modeling, reproducible reports (R Markdown/Quarto), and open-source flexibility
- Python — for data pipelines, automation, and analysis that borders on data science or machine learning
- SAS — for large-scale, regulated, or clinical/pharmaceutical programming environments
- Stata — for econometrics, panel data, and epidemiological programming
- SPSS syntax — for teams that need scripted, repeatable SPSS analysis rather than manual menu use
When You Need Statistical Programming (vs. Standard Data Analysis)
Not every project needs custom code — a one-off statistical analysis is often faster to run directly in SPSS or Stata's menus. Statistical programming becomes the better fit when:
- The same analysis needs to run again on new data (recurring surveys, monitoring, ongoing research)
- Your model or method isn't available as a built-in menu option
- You need a documented, auditable process — for regulatory review, replication, or handover to a team
- You're combining or automating multiple analysis steps into one pipeline
- You want to run a simulation or test a method's properties before applying it to real data
If you're not sure which applies to your project, that's a normal question to bring to the free consultation. We will help you resolve it.
Who This Page Is For
- Researchers with recurring or longitudinal data that needs a repeatable analysis pipeline
- Teams needing custom models outside standard SPSS/Stata menu options
- PhD candidates and academics who need documented, reproducible code for methodology review
- Businesses automating recurring reporting or analytics from operational data
Your Personal Consultant
Angela Kasemi (Statistician & Academic Consultant)
+44 7848 117 104
excellentstatistics@outlook.com
Request a free consultation for your project right away, and receive a free non-binding quote.
Send us your data, we'll tell you how we can support you
Not sure whether your dataset needs cleaning, a specific statistical analysis method, or is already ready for interpretation? Send it over and we'll assess it as part of your free quote.