We perform all analyses using industry-standard statistical software recognized by Cochrane, the Joanna Briggs Institute, and leading peer-reviewed journals. The table below summarizes each platform and its typical applications.
| Software | Strengths | Common Research Applications |
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| R | Open source, extensive package ecosystem, advanced visualization, Bayesian methods | Meta-analysis (metafor, meta), regression modeling, survival analysis, publication-quality ggplot2 figures, machine learning, text mining |
| Stata | Intuitive syntax, strong epidemiological tools, panel data capabilities | Clinical trial analysis, survey-weighted analysis, longitudinal panel models, epidemiological methods (metan, stcox, melogit) |
| SPSS | Menu-driven interface, widely taught in graduate programs | Descriptive statistics, ANOVA, chi-square tests, logistic regression, scale reliability analysis, common in social science and nursing research |
| SAS | Regulatory acceptance, macro programming, large dataset handling | Clinical trial reporting for FDA and EMA submissions, pharmaceutical research, health insurance claims data, CDISC-compliant outputs |
All code is fully annotated with inline comments explaining each analytical step. We select the software that fits your project, or use the platform required by your institution, funder, or target journal. If you have no preference, we default to R for its flexibility and open-source reproducibility.
Our workflow follows a structured, transparent sequence from initial consultation through final deliverables. Every step is documented so you and your reviewers can trace the analytical decisions.
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Consultation and study review. We discuss your research question, study design, outcome variables, and analytical goals. We review your dataset structure and identify any immediate data quality issues such as missing values, outliers, or coding inconsistencies.
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Statistical analysis plan. We draft a formal analysis plan documenting all primary and secondary analyses, covariates and confounders, handling of missing data (listwise deletion, multiple imputation, or maximum likelihood), planned sensitivity analyses, and the significance threshold. This plan serves as a methodological roadmap and is useful for ethics applications and journal submissions.
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Data cleaning and preparation. We clean and restructure your data as needed, including variable recoding, data transformation, outlier assessment, and merging of multiple data files. We document every data manipulation step in the code for full transparency.
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Analysis execution. We run all planned analyses with appropriate model diagnostics and assumption checks. Each model is evaluated for correct specification, and alternative approaches are tested when assumptions are not met.
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Results compilation. We produce formatted tables (descriptive statistics, regression coefficients, odds ratios, hazard ratios), publication-quality figures (forest plots, Kaplan-Meier curves, ROC curves, scatter plots), and a narrative interpretation of every result.
Every statistical analysis project from Research Gold includes a comprehensive deliverable package designed for immediate use in your manuscript, thesis, or grant application.
Reproducible Code
Fully annotated R scripts, Stata do-files, or SPSS syntax files documenting every step from data import through final output. Your code runs independently on any machine with the specified software installed, enabling you and your reviewers to verify and replicate every result.
Publication-Ready Tables
Formatted tables following the conventions of your target journal. This includes Table 1 (baseline characteristics with means, standard deviations, frequencies, and percentages), regression output tables (coefficients, standard errors, confidence intervals, p-values), and any additional summary tables required by your study design.
High-resolution figures delivered in PNG, PDF, and editable vector formats. Common figures include forest plots, Kaplan-Meier survival curves, ROC curves, scatter plots with regression lines, residual diagnostic plots, and box plots. All figures are sized to match standard journal column widths.
Narrative Results Interpretation
A written summary of your results in academic prose, suitable for direct inclusion in the results section of your manuscript. This narrative covers the magnitude and direction of effects, statistical significance, confidence intervals, effect sizes, and clinical or practical interpretation. We explain what the numbers mean in the context of your research question, not just whether a p-value crossed a threshold.
Methods Section Draft
A draft of the statistical methods paragraph for your manuscript, describing the analytical approach, software version, packages used, and reporting standards followed. This section is written to satisfy peer reviewer scrutiny and meets the statistical reporting requirements of CONSORT, STROBE, or other applicable EQUATOR Network guidelines.
Choosing the right statistical software depends on your discipline, research design, institutional requirements, and analytical complexity. The comparison below helps you decide which platform best fits your project.
| Factor | R | Stata | SPSS |
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| Cost | Free and open source | Commercial license required | Commercial license required |
| Learning curve | Steeper, code-based | Moderate, code or menu | Gentle, menu-driven |
| Best for | Advanced modeling, Bayesian methods, meta-analysis, custom visualization | Epidemiology, clinical trials, panel data, survey analysis | Descriptive statistics, ANOVA, scale reliability, social science |
| Reproducibility | Excellent (scripts, R Markdown, Quarto) | Excellent (do-files, logs) | Limited (syntax files less commonly used) |
| Visualization | Superior (ggplot2, plotly) |
When you use our R statistical analysis service, you receive scripts built with tidyverse, ggplot2, and domain-specific packages such as survival, lme4, brms, and metafor. When you choose our Stata analysis service, deliverables include do-files with Stata commands optimized for clinical and epidemiological workflows. Our SPSS analysis service provides syntax files alongside annotated output for researchers whose programs or committees require SPSS-based results.
If you are unsure which software to select, we will recommend the best option based on your discipline, analytical requirements, and target journal conventions.
Our clients span every stage of the research lifecycle and represent a wide range of disciplines and career stages.
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PhD candidates and doctoral researchers. Your dissertation committee expects rigorous statistical analysis, but your training may not have covered the specific methods your data require. Our service delivers defensible results with code your committee can inspect at your viva or defense. This is the most common use case for our data analysis service for PhD researchers.
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Clinical researchers and physician-scientists. You have patient data from a hospital-based study or clinical trial and need a biostatistician to analyze it correctly, handle missing data, and produce results that satisfy journal peer reviewers and institutional review board requirements.
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Grant applicants. Preliminary statistical results strengthen grant proposals by demonstrating feasibility and effect size estimates. We deliver analyses formatted for National Institutes of Health, National Institute for Health and Care Research, and institutional funding applications. See our grant methodology service for additional support.
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Systematic review teams. You have completed a qualitative review and now need quantitative analysis of your extracted data. Our biostatisticians pool results, generate forest plots, and assess heterogeneity. Learn more on our meta-analysis service page.
Our statistical analysis pricing is based on project scope and complexity rather than a fixed menu. Variables that influence the quote include the number of outcome variables, the complexity of the analytical methods, the size and quality of the dataset, and the turnaround timeline.
| Project Type | Typical Price Range | Includes |
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| Single regression model with descriptive statistics | $400 to $600 | Data cleaning, assumption checks, regression output, formatted table, code |
| Multi-model analysis (3 to 5 models) | $600 to $1,000 | Multiple regression or survival models, tables, figures, narrative interpretation |
| Complex analysis (mixed-effects, propensity score, Bayesian) | $1,000 to $2,000 | Advanced modeling, sensitivity analyses, multiple figures, methods section draft |
| Comprehensive analysis package | $2,000 and above | Full analytical pipeline, multiple outcomes, subgroup analyses, complete results chapter |
All projects include reproducible code, formatted tables, publication-quality figures, narrative interpretation, and revisions for reviewer comments within the engagement scope.
View full pricing details on our view our research service tiers page or request a free project scope evaluation for a personalized estimate. We reply with a detailed quote within 2 hours.
We offer free, browser-based statistical calculators for researchers who want to explore methods, verify calculations, or run preliminary analyses before ordering our full service.
- Power analysis calculator: Determine the sample size needed to detect a meaningful effect with adequate statistical power for your study design.
- Effect size calculator: Calculate Cohen's d and Hedges' g for standardized mean differences, along with odds ratios and risk ratios.
- Chi-square calculator: Run a free chi-square calculator test of independence or goodness of fit with expected frequencies and p-values.
- ICC calculator: Compute the ICC calculator intraclass correlation coefficient for inter-rater reliability and measurement agreement studies.
These tools complement our professional service. Use them for teaching, preliminary exploration, or quick verification. For publication-ready results with expert oversight and narrative interpretation, our full statistical analysis service provides the rigor and documentation that peer reviewers expect.
For categorical outcomes, choosing the right test matters. Learn when to use chi-square versus Fisher's exact test based on sample size and expected cell counts.
Professional statistical services now deliver reproducible R code alongside results, allowing you and reviewers to verify every step of the analysis.
