Overview
Reviewers increasingly ask questions that analysis alone cannot answer after the fact: was the sample adequate, were assumptions tested, can this be reproduced from what you reported?.
We work either at design stage, where those questions are cheapest to answer, or on data you already have — and we report the results in the form your target journal expects rather than as raw software output.
Who this is for
What is included
Everything covered by this service
All of the below is within the agreed scope. Revisions inside that scope are included rather than billed as extras.
Study design review
Sampling, power and instrument choice assessed before collection, where a design flaw can still be corrected.
Descriptive and inferential statistics
The full analysis, with assumptions explicitly tested rather than assumed.
Modelling and hypothesis testing
Model selection justified, diagnostics reported, and limitations stated plainly.
Data visualisation
Figures built to your journal's specification, legible in greyscale and at print size.
Results reporting
Effect sizes, confidence intervals and exact p-values reported to your discipline's standard.
Reproducibility documentation
Analysis scripts and a written methods section so another researcher could repeat what you did.
Tools
Software we work in
We use the packages researchers and reviewers already recognise, so your methods section names a tool your examiners and referees know. Output is delivered in your preferred format, with the analysis scripts included.
SPSS
Quantitative analysis, survey data and standard inferential testing.
R
Modelling, simulation, custom analysis and publication-quality graphics.
STATA
Panel data, econometrics, survey weighting and epidemiological analysis.
NVivo
Qualitative coding, thematic analysis and mixed-methods integration.
Python
Large datasets, machine learning and reproducible analysis pipelines.
SAS
Clinical trial analysis and regulated environments requiring validated output.
MATLAB
Signal processing, numerical methods and engineering data.
Excel
Data preparation, cleaning and straightforward descriptive work.
ATLAS.ti
Qualitative data analysis across text, audio and image sources.
Minitab
Quality control, design of experiments and process capability studies.
JASP
Bayesian and frequentist analysis for teaching and smaller studies.
GraphPad Prism
Life-science data, dose-response curves and biostatistics.
Working in something not listed here? Tell us which package your department or supervisor requires and we will confirm whether we can work in it.
How it works
Four stages, agreed before we begin
Deliverables, owners and dates are fixed at the outset, so you always know what is happening and when.
Data review
We look at your dataset and research questions, then confirm which methods are appropriate and which are not.
Analysis plan
A written plan agreed before analysis begins, so the approach is not chosen after seeing which result it produces.
Analysis
Run, with assumptions tested and diagnostics recorded.
Reporting
Tables, figures, a methods section and reproducible scripts, formatted for your target journal.
Questions
Before you get in touch
The questions we are asked most often about this service.
All frequently asked questions
Spreadsheets, CSV, and the native formats of the common statistical packages. Tell us what you have and we will confirm before you send anything.
No. We report what the data shows. Choosing an analysis because it produces the desired answer is a research integrity failure, and it tends to be caught.
Yes, and this is the best time to involve us. Power calculation and design review at the planning stage prevent problems that cannot be fixed afterwards.
Yes. Scripts and documentation are yours, so the analysis can be repeated, checked or extended without coming back to us.