A Research Gold statistical analysis service engagement covers the full quantitative pipeline. Before any work begins, a PhD statistician reviews your data, your research questions, and the journal or thesis requirements you are writing for. From there, every project includes:
- A cleaned, deduplicated dataset with documented variable transformations
- Descriptive statistics tables formatted for your target journal or thesis style
- The inferential analyses your study calls for (regression, ANOVA, mixed-effects models, survival analysis, mediation, moderation, or structural equation modeling)
- Diagnostic checks for every model (assumptions, residuals, multicollinearity, influence, fit indices)
- Annotated SPSS syntax, R script, Stata do-file, or SAS program so your analysis is fully reproducible
- A results write-up in APA, AMA, or ICMJE style ready to paste into your manuscript
- A 30-minute results walkthrough so you can defend the work in a thesis viva, peer review response, or grant interview
If your supervisor or reviewer asks for an additional model, sensitivity analysis, or robustness check, that revision is included in the original scope, not billed separately.
Picking the right software matters less than picking the right analyst. We work in whatever your supervisor, journal, or grant body requires.
SPSS data analysis services
SPSS remains the standard for psychology, education, nursing, and health-services research. Our SPSS analysis services cover descriptive statistics, t-tests, ANOVA, ANCOVA, regression, factor analysis, mediation through PROCESS macro, and reliability analysis. We deliver the .sps syntax file alongside the .sav dataset so you can re-run anything during revisions.
R statistical analysis service
R is our default for meta-analysis (metafor, meta), advanced regression, mixed-effects models (lme4, nlme), survival analysis (survival, survminer), and Bayesian methods (brms, rstanarm). All R scripts are written in tidyverse style with comments, and we provide both the script and a knitted HTML report.
Python data analysis with pandas, statsmodels, and scipy
Python statistical analysis is the right choice for projects that cross into machine learning, handle datasets larger than SPSS or Stata can hold in memory, or need to live inside a reproducible Jupyter notebook pipeline. Our Python work uses pandas for cleaning, statsmodels and scipy for inferential statistics (regression, ANOVA, t-tests, mixed-effects models), pingouin for APA-formatted output, and scikit-learn when the analysis extends into predictive modeling. Every notebook is delivered with all cells executed, the environment pinned, and a Markdown methods paragraph for your manuscript.
Stata data analysis service
Stata is preferred in epidemiology, health economics, and many clinical trial settings. We cover regression, panel-data analysis, propensity score matching (psmatch2, teffects), survival analysis, and survey-weighted estimation (svy commands).
SAS, JASP, MPlus, AMOS, and Smart-PLS
For projects that require these specialist tools, particularly structural equation modeling, latent class analysis, or partial least squares, our PhD statisticians deliver fully documented programs with model fit diagnostics and visualizations.
- Discovery call (free, 30 minutes). Share your research questions, data, and target journal or thesis requirements. We confirm whether your study design supports the analysis you have in mind.
- Data audit (24 hours). A PhD statistician reviews your dataset, flags missing-data patterns, distributional issues, and design considerations that need to be resolved before analysis.
- Analysis plan approval. You receive a one-page analysis plan listing every test we will run, the assumptions we will check, and the output you will receive. Work begins after you approve.
- Analysis and write-up (10 to 14 days). Statistical models are run, diagnostics are documented, and the results section is drafted in your target style.
- Walkthrough call. A 30-minute call to walk through every table, figure, and finding. Revisions to the analysis or write-up are part of the original scope.
Statistical analysis service pricing reflects the complexity of the models, the size and condition of your data, and the depth of write-up you need. We never charge an hourly rate. Every quote is a fixed fee approved before work begins.
- Descriptive plus basic inferential analysis (t-tests, chi-square, correlation, simple regression, single-table results) starts from $750.
- Full thesis statistics chapter (multiple regression, ANOVA family, factor analysis, mediation, full APA-style results section) starts from $1,500.
- Advanced models (mixed-effects, structural equation modeling, longitudinal growth models, Bayesian hierarchical models, survival analysis with competing risks) start from $2,500.
For an exact figure, request a quote and we will return a fixed-fee proposal within one business day. Indicative ranges live on the pricing page.
- PhD candidates writing the statistics chapter of a quantitative dissertation
- Journal authors preparing or revising a manuscript with reviewer-requested analyses
- Grant writers who need a statistical analysis plan, justification of sample size, and a methods paragraph
- Clinicians publishing case series, audits, or registry analyses
- Public health and policy researchers analyzing survey data with sampling weights
- Market researchers who need defensible quantitative conclusions for client reports
- A mediation analysis on a psychology thesis with 412 participants, run in SPSS PROCESS macro, with bootstrapped confidence intervals and a publishable APA results section.
- A mixed-effects model for a longitudinal cohort study, fit in R using lme4, with marginal and conditional R-squared, intraclass correlation, and a sensitivity analysis using a different covariance structure.
- A complex survey analysis of a national household survey using Stata svy commands, with weighted estimates, design-adjusted standard errors, and subgroup contrasts.
- A Bayesian logistic regression for a clinical prediction model using R (brms), with posterior predictive checks, calibration curves, and an external-validation report.
If your project is a systematic review with quantitative pooling, our meta-analysis service and systematic review service include statistical analysis end to end. If your project is a clinical trial, observational study, or biomarker analysis, our biostatistics consulting sits closer to clinical research and Good Clinical Practice. The statistical analysis service described here is the right starting point for general research statistics across psychology, education, sociology, business, and public health, where the field expectation is statistics rather than biostatistics.
Before you commission a project, you can run the underlying calculations yourself with our free tools: a linear regression calculator, a confidence interval calculator, an effect size calculator, a sample size calculator, and a power analysis calculator. For meta-analytic outputs, the forest plot generator and funnel plot generator produce publication-ready figures from raw study data. If you discover that your analysis is more involved than the tools handle, that is the moment a PhD statistician pays for themselves.