Postdoc · Faculty · PI · Lab

For Researchers

Research-grade methods.
Peer-review-ready output.

Postdoctoral researchers, faculty, and research teams working on journal submissions, funded projects, or multi-wave studies. We act as methodological collaborators — not subcontractors.

Typical client

Postdoc / faculty PI
journal submission or funded project

Common need

Peer review readiness
methods, replication, supplementary materials

Scale

Project-based
quoted per project phase, not per hour

Authorship ethics

No ghost work
we are acknowledged, not hidden

What we offer

Methods support for publishable research.

From initial design to replication package — we cover the full methodological lifecycle of a research project.

Design

Study design & pre-registration

Power calculations, sampling strategy, pre-registration drafts (OSF, AsPredicted). We design for the analysis you intend to run, not the other way around.

Analysis

Econometric & statistical modelling

Panel data, IV estimation, DID, RD, structural equation models, hierarchical linear models, Bayesian inference. We work in R and Python with full reproducibility.

Revision

Peer review response

Reviewer 2 asked for a robustness check you weren't planning. We run it, document it, and draft the response section — so you resubmit stronger, not just faster.

Replication

Reproducibility & data documentation

Full replication packages: annotated code, README files, codebooks, version control. Meeting the standards of journals that require open data and open code.

Clinical

Health & clinical study methods

We have MD collaboration for health data, epidemiological methods, and clinical study design. Appropriate for health-sector research requiring medical oversight.

Measurement

Survey methods & measurement

Instrument design, translation validation, measurement invariance testing, scale reliability. For cross-national or Turkish-market surveys in political science, sociology, and public health.

Open source by conviction

R. Python. Linux.
No proprietary lock-in.

We use open-source tools not because they're free-as-in-beer, but because reproducibility requires auditability. Any researcher should be able to run your analysis from your code and your data and reach your results. That's only possible without proprietary walls.

We work in Stata and SPSS if that's what your institution requires — but where the choice is yours, we advocate for an open stack. We can also migrate legacy analyses from proprietary software to R or Python as part of a reproducibility project.

The same transparency we expect in published science, applied to our own work.
R
Primary analysis, econometrics, mixed models, visualisation (ggplot2)
Python
Data engineering, machine learning, scraping, automation, NLP
Stata
Panel econometrics, survey-weighted analysis — institutional client requests
SPSS
Scale reliability, survey analysis — where required by client institution
Git / Linux
Version control, reproducible pipelines, server-side processing
LaTeX / Quarto
Publication-ready typesetting, reproducible manuscript authoring

How we collaborate

A methodological partner, not a subcontractor.

We work with your team's standards, timelines, and theoretical commitments — not around them.

Start with a scope call →
01

Scope call

We read your draft, pre-registration, or project outline in advance. The call is substantive from minute one.

02

Collaboration agreement

A document that names what we're doing, the timeline, the fee, and — critically — how our contribution will be acknowledged. No ghost work.

03

Iterative working sessions

Regular check-ins with shared working documents. You're involved in every methodological decision, not presented with a fait accompli.

04

Reproducible deliverable

Annotated code in your chosen environment. A narrative methods document you can drop into a supplementary file or share with a data editor.

05

Peer review support

When reviewers request additional tests, we're available to run them and help draft the methodological response.

Open source by conviction

R. Python. Linux.
No proprietary lock-in.

We use open-source tools not because they're free-as-in-beer, but because reproducibility requires auditability. Any researcher should be able to run your analysis from your code and your data and reach your results. That's only possible without proprietary walls.

We work in Stata and SPSS if that's what your institution requires — but where the choice is yours, we advocate for an open stack. We can also migrate legacy analyses from proprietary software to R or Python as part of a reproducibility project.

The same transparency we expect in published science, applied to our own work.
R
Primary analysis, econometrics, mixed models, visualisation (ggplot2)
Python
Data engineering, machine learning, scraping, automation, NLP
Stata
Panel econometrics, survey-weighted analysis — institutional client requests
SPSS
Scale reliability, survey analysis — where required by client institution
Git
Version control, reproducible pipelines, server-side processing
Quarto
Publication-ready typesetting, reproducible manuscript authoring

Pricing

A clear fee. Before you commit.

Every engagement starts with a free scope call and a written proposal. You see the total cost before we begin.

Project — defined scope, fixed fee

₺28.000
Proje

Most research projects fall under this tier. For ongoing programmes (ERC, national-council or institutional grants), the Araştırma Programı quarterly retainer is available.

What's included

Full project scope document
Iterative working sessions
Annotated code + reproducible pipeline
Peer review response support
Revision round included

Ready to make sense of your data?

Book a free 20-minute consultation. No commitment, no sales pitch — just a conversation about your research.

Book a free consult → See pricing