Practical help with the tools themselves: running analyses in your chosen software, fixing errors, cleaning and visualizing data, turning output into well-reported results, and making code reproducible.
Sometimes the method is clear but the software is the obstacle: an SPSS dialog that hides an important option, a Stata do-file that stops at an error, an R package conflict, or a Python notebook that nobody else can run. The software services cover SPSS, Stata, R and Python individually, and a combined service covers SAS, EViews, Excel, MATLAB, JASP and jamovi.
Three cross-cutting services sit alongside them. Data cleaning, coding and visualization prepares raw data for analysis and produces clear figures. Results interpretation and reporting turns output into accurate APA-style tables and explanations you can write from. Code review and reproducibility packages check that an analysis runs from raw data to final tables, which journals and funders increasingly require.
Practical help with IBM SPSS Statistics: setting up variables correctly, choosing and running procedures, saving syntax so the work is repeatable, and reading the output tables with confidence.
Hands-on Stata support for economists, health and policy researchers: well-structured do-files, safe merges and reshapes, the right estimation commands and publication-ready tables.
Support for analysis in R: tidy data preparation, choosing and fitting models from the right packages, clear ggplot2 figures and reproducible Quarto or R Markdown reports.
Support for research analysis in Python: tidy pandas pipelines, statistical inference with statsmodels and SciPy, predictive modeling with scikit-learn, and notebooks or scripts that run the same way on another machine.
Analysis support in six tools with distinct user bases: SAS for clinical data, EViews for time-series econometrics, Excel for applied models, MATLAB for engineering data, and JASP or jamovi for free point-and-click statistics.
Preparing raw research data for analysis: consistent coding, documented handling of missing values and outliers, reverse scoring, merging files, a codebook, and clear exploratory and publication figures.
Help translating statistical output into accurate, well-formatted results: what each number means, which values to report, APA 7 notation, effect sizes and confidence intervals, and tables and figures that follow the manual.
An independent check that your analysis code does what you think it does, and preparation of a replication package that a journal data editor or another researcher can run from start to finish.
Tell us what you’re working on and where you’re stuck. We’ll recommend the right kind of help and send a quote.