Research analysis built for transparency, reproducibility and academic publication
Research methodology and data analysis

Research data analysis you can reproduce

Methodology design, statistical and econometric analysis, reproducible R, Stata and Python code, systematic reviews, bibliometrics, tables and figures for academic research.

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Reproducible deliveryCode, logs and documented workflow
Research-focusedMethods aligned with the research question
Clear scopeDeliverables and deadlines agreed first
Confidential handlingProject-specific file handling

Research Services

Focused analytical support for individual studies and multi-paper research programs.

01

Statistical & Econometric Analysis

Panel data, fixed effects, DiD, event studies, GMM, survival analysis, diagnostics and robustness testing.

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02

Reproducible Research Code

Structured R, Stata or Python scripts with datasets, logs, README instructions and reproducible outputs.

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03

Reviews & Bibliometrics

Systematic review workflows, PRISMA documentation, bibliometrics and science mapping.

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A traceable workflow

Each full-analysis project links the reported results to data, code and documented analytical decisions.

1

Scope

Research question, data and requirements.

2

Method

Models, variables and diagnostics.

3

Analysis

Documented reproducible workflow.

4

Delivery

Code, outputs and documentation.

Multi-study support

One dataset, multiple distinct research questions

Multi-study projects receive separate specifications, analytical outputs and reproducibility folders for each study.

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Typical full-analysis delivery

Analysis dataset, data dictionary, methodology specification, R/Stata/Python code, execution logs, diagnostics, robustness tests, tables, figures, README and results notes.