Overview
Universitas quantitative research applies statistical modeling, econometrics, and computation to test hypotheses and analyze data patterns. From study design to regression models, our code is documented and reproducible.
We deliver descriptive and inferential statistics, parametric and non-parametric tests, linear and logistic regressions, clustering, and predictive modeling using standard scientific languages: R and Python, with support for SPSS and Stata routines.
For foundational methodological principles, explore our guide on what is quantitative research.
Who it is for
- Academia: Researchers, graduate students, and lab directors needing statistical support for doctoral dissertations, research grants, and peer-reviewed journal submissions.
- Businesses: Product, marketing intelligence, and strategy teams seeking to identify customer patterns, market segmentation, and measurable ROI.
- Public Sector: Government bodies focused on public policy evaluation, socioeconomic diagnostics, and open data transparency.
- NGOs & Non-Profits: Civil society organizations seeking social impact assessment, territorial indicators, and quantitative evidence for grants and advocacy.
How we work
- Problem definition: Alignment on analytical questions, hypotheses, and variables.
- Data cleaning: Systematic handling of missing values, outlier detection, and schema validation.
- Statistical modeling: Execution of models tailored to data properties, validating theoretical assumptions.
- Delivery: Methodological documentation, standardized tables, and full source code.
