Quantitative Research Services – Large-Scale Data Analysis for Evidence-Based Decisions

Make decisions with confidence. Research Bureau delivers robust quantitative research and statistical analysis for organisations that need clear, reproducible evidence from large-scale data. Whether you need nationally representative survey analysis, enterprise-grade predictive models, or rigorous impact evaluation, our team transforms complex data into actionable insight.

Contact us for a quote — share your project details through the contact form, click the WhatsApp icon, or email [email protected]. We’ll respond with a tailored proposal and timeline.

Why choose Research Bureau for quantitative research?

We combine methodological rigor, practical experience, and transparent reporting so stakeholders can rely on results for strategy, policy, investment, or publication. Our clients value:

  • Expertise from PhD-level statisticians and seasoned data scientists.
  • Scale: architecture and workflows to handle millions of records and complex linkages.
  • Reproducibility: documented code, version control, and reproducible reports for auditability.
  • Actionability: clear recommendations tied to confidence intervals, effect sizes, and business impact.

We never provide medical diagnoses or clinical services. We focus on data-driven decision support across market research, public policy evaluation, operational analytics, and academic research.

Core services — What we deliver

We cover the full quantitative research lifecycle, from design to delivery:

  • Study design and power analysis
  • Survey sampling, weighting, and nonresponse adjustment
  • Large-scale data engineering and cleaning
  • Descriptive and inferential statistics
  • Regression modeling (linear, logistic, hierarchical/multilevel)
  • Time-series and panel-data analysis
  • Causal inference (RCTs, quasi-experimental methods)
  • Predictive analytics and machine learning
  • A/B testing and experimentation design
  • Segmentation and clustering
  • Visualisation, dashboards, and interactive reporting
  • Data governance, anonymisation, and compliance support

Our approach: rigorous, transparent, outcome-focused

Every project follows a well-documented pipeline tailored to your objectives. We emphasise reproducible workflows and clear decision criteria.

  1. Problem definition and KPIs

    • We translate stakeholder questions into testable hypotheses and measurable KPIs.
    • We define success criteria and acceptable error margins before analysis begins.

  2. Study design and sampling

    • We design surveys, experiments, or observational studies with appropriate sampling plans.
    • We run power calculations and margin-of-error estimates to ensure results meet your precision needs.

  3. Data acquisition and engineering

    • We integrate survey data, transactional databases, CRM, sensor feeds, and third-party datasets.
    • We document ETL processes and maintain lineage for all inputs.

  4. Statistical analysis and modeling

    • We select methods aligned with assumptions and data quality, from weighted estimators to causal inference.
    • We quantify uncertainty using confidence intervals, Bayesian credible intervals, and sensitivity analyses.

  5. Visualisation and reporting

    • We produce concise, executive-level summaries plus detailed technical appendices and code.
    • We deliver reproducible notebooks and dashboards for ongoing monitoring.

  6. Implementation support

    • We translate findings into actionable recommendations and support deployment of predictive models or dashboards.

Research design & sampling: getting the foundation right

A flawed design yields misleading conclusions. We invest in upfront design work to avoid biases and ensure representativeness.

  • Power analysis & sample size: We compute required sample sizes for proportions, means, and regression coefficients. Example: for a binary outcome with p = 0.5, 95% confidence, and ±5% margin of error, required sample ≈ 385 respondents. We provide adjustments for design effects and stratification.
  • Stratified and cluster sampling: We design complex sampling frames and calculate appropriate weights to produce population-level estimates.
  • Nonresponse adjustment & calibration: We apply weighting, raking, and post-stratification to correct known biases.
  • Survey instrument design: We provide question design, piloting, and cognitive testing to reduce measurement error.

Data engineering & scale: collections to clean analytics-ready datasets

Large datasets require disciplined engineering and reproducibility.

  • Data ingestion using scalable pipelines (batch and streaming)
  • Deduplication, record linkage, and entity resolution
  • Missing data management: multiple imputation, model-based approaches
  • Feature engineering for time-based, text-derived, and event-sequence features
  • Data versioning and provenance tracking for auditability

We tailor architecture to your environment: on-premise, cloud (AWS/Azure/GCP), or hybrid implementations.

Statistical modeling & predictive analytics

We choose models that match your research questions and data-generating processes.

  • Descriptive analytics: weighted estimates, cross-tabulations, complex survey variance estimation.
  • Regression analysis: OLS, GLMs, Poisson/negative binomial for counts, logistic regression for binary outcomes.
  • Multilevel/hierarchical models: handle nested structures (students in schools, customers in regions) and borrow strength across groups.
  • Time-series and panel models: ARIMA, state-space models, VAR, fixed and random effects for longitudinal data.
  • Causal inference:
    • Randomised controlled trials (A/B tests) design and analysis
    • Difference-in-differences, regression discontinuity, instrumental variables, synthetic control
    • Propensity score matching and doubly robust estimators
  • Machine learning: tree-based models (XGBoost, Random Forests), regularised regression (LASSO, Ridge), gradient boosting, neural networks for high-dimensional prediction
  • Model evaluation: cross-validation, ROC/AUC, precision-recall, calibration, uplift modeling and business-metric oriented scoring

We always accompany predictive models with interpretation, uncertainty quantification, and checks for fairness and stability.

Example outputs: what you receive

Our deliverables are structured for stakeholders and technical reviewers.

  • Executive summary with actionable recommendations
  • Technical appendix with methodology, assumptions, and code (R/Python/Stata)
  • Reproducible analysis notebooks and scripts
  • Interactive dashboards (Power BI, Tableau, or web-based)
  • Model artefacts (serialized models, feature dictionaries, scoring pipelines)
  • Data dictionary and provenance documentation

Tools, technology & security

We use proven tools for reproducible, scalable analysis:

  • Programming and analysis: R, Python (pandas, scikit-learn, statsmodels), Stata
  • Big data: SQL, Spark, Hadoop, cloud data warehouses (Redshift, BigQuery, Snowflake)
  • Visualisation and dashboards: ggplot2, matplotlib, Plotly, Tableau, Power BI
  • Experimentation platforms and A/B testing frameworks
  • Version control: Git, code reviews, CI/CD for models
  • Security: encrypted storage, secure transfer (SFTP/HTTPS), role-based access, SOC-compliant cloud deployments

We design workflows to comply with data protection frameworks such as POPIA and GDPR, and we apply strong anonymisation and pseudonymisation practices where necessary.

Anonymised case studies — representative examples

We present anonymised examples to illustrate our methods and impact.

Case study A — National household survey (policy)

  • Objective: Estimate national prevalence of a socio-economic indicator with sub-national precision.
  • Approach: Stratified multi-stage cluster sampling; sample size designed for provincial estimates; post-stratification weighting.
  • Analysis: Multilevel logistic regression to identify risk factors; small-area estimation to produce district-level maps.
  • Outcome: Delivered a detailed policy brief and interactive maps; findings informed targeted interventions and resource allocation.

Case study B — Customer churn prediction (telecommunications)

  • Objective: Reduce monthly churn by identifying high-risk customers for targeted retention.
  • Approach: Engineered usage, billing, complaint, and network quality features from 36 months of transactional data. Handled class imbalance via sampling and cost-sensitive learning.
  • Model: Gradient boosting with temporal cross-validation; SHAP analysis for interpretability.
  • Outcome: Top decile score customers had a 4x higher churn rate; marketing intervention increased retention by 7% among targeted cohort, improving ARPU.

Case study C — Product A/B test (fintech)

  • Objective: Measure the causal effect of a new onboarding flow on activation and first-month retention.
  • Approach: Randomised experiment with blocking on device type and geography. Pre-registered analysis plan and sequential monitoring rules.
  • Analysis: Intention-to-treat and complier average treatment effect estimation; subgroup analysis and uplift modeling.
  • Outcome: Detected a statistically significant 6 percentage point lift in activation; rollout plan implemented with ROI projections.

Sample calculations & methodological notes

Power calculation for a difference in proportions:

  • Example: detect a 5 percentage-point difference (p1=0.20, p2=0.25) with 80% power at α=0.05.
  • Approximate required sample per group: n ≈ [(Zα/2 * sqrt(2p(1-p)) + Zβ * sqrt(p1(1-p1) + p2(1-p2)))^2] / (p2-p1)^2.
  • We provide exact calculations tailored to your baseline rates, clustering, and design effects.

Weighting and variance estimation for complex survey:

  • We compute base weights, adjust for nonresponse, and calibrate to known population totals.
  • Variance estimation uses Taylor linearisation or replicate weights (jackknife/bootstrapping) as appropriate.

Interpreting regression outputs:

  • We report coefficients with confidence intervals and explain effect sizes in real-world units.
  • For logistic models, we present odds ratios, marginal effects at representative values, and predicted probabilities.

Quality assurance, reproducibility & ethics

We prioritise transparency and scientific integrity.

  • Reproducible code and analytic pipelines — we hand over notebooks and scripts.
  • Versioned datasets and archived raw files for audit trails.
  • Pre-analysis plans and registered designs for experiments where required.
  • Sensitivity analyses and robustness checks to test assumptions.
  • Ethical data handling, anonymisation, and compliance with POPIA/GDPR.
  • Formal quality review cycles, including peer code review and independent validation where needed.

Packages & pricing (indicative)

Choose a package that fits your scale. Final pricing depends on scope, data complexity, and delivery timelines. Share project details for an exact quote.

Package Typical use case Deliverables Timeline Indicative budget (ZAR)
Starter Small survey or focused analysis Executive report, technical appendix, code 2–4 weeks 30,000 – 80,000
Advanced Multi-source integration, predictive model Dashboards, models, reproducible scripts, stakeholder workshop 6–12 weeks 120,000 – 350,000
Enterprise Large-scale national studies, production ML pipelines End-to-end deployment, monitoring, training 3–6 months 500,000+
Custom Tailored research & consulting Fully scoped proposal Varies Quoted on request

All budgets are indicative. We provide fixed-fee proposals or time-and-materials options based on agreed milestones. Contact us with project details for a tailored estimate.

Typical timelines & milestones

We structure projects with clear milestones and sign-off points.

  • Week 1–2: Project scoping, KPI agreement, and data access setup
  • Week 3–4: Prototype analysis, pilot survey or proof-of-concept model
  • Week 5–8: Full analysis, model development, sensitivity tests
  • Week 9–12: Reporting, dashboards, stakeholder review, and handover

Turnaround times are flexible; accelerated timelines are available for urgent projects with scoped deliverables.

How we measure impact

We focus on metrics that matter to your organisation:

  • Policy: coverage gaps addressed, resource reallocation efficiency, measurable outcome changes
  • Business: lift in conversion, reduction in churn, ROI of targeted interventions
  • Operations: improved forecasting accuracy, cost savings via automation
  • Research: publication-quality outputs, reproducibility, and citations

We can design impact measurement frameworks to track outcomes post-implementation.

Getting started — what we need from you

To provide a fast, accurate quote, share the following:

  • Project objectives and primary questions
  • Types and volume of available data (sample sizes, databases, file formats)
  • Target population and desired level of granularity (national, regional, segment)
  • Expected deliverables (report, dashboard, model deployment)
  • Timeline constraints and budget range
  • Any legal/compliance considerations (POPIA, data-sharing agreements)

Send these through the contact form, click the WhatsApp icon, or email [email protected] and we’ll reply with a scoping plan and timeline.

Frequently asked questions

Q: Can you work with proprietary or sensitive data?

  • A: Yes. We implement secure transfer, encrypted storage, role-based access, and can sign NDAs or data processing agreements.

Q: Do you provide post-deployment support for models?

  • A: Yes. We offer monitoring, retraining schedules, and operational support for production models.

Q: Will we get the analysis code?

  • A: Yes. We deliver reproducible scripts and notebooks. Where requested, we provide deployment-ready code for integration.

Q: Can you handle non-survey data like logs or transactional records?

  • A: Absolutely. We specialise in linking and analysing heterogenous datasets, including event streams and relational databases.

Q: How do you ensure unbiased results?

  • A: We pre-specify analysis plans, perform sensitivity tests, test for model drift and fairness, and document limitations transparently.

FAQ quick-read — what to expect in a proposal

  • Clear scope, deliverables, and exclusions
  • Project milestones with acceptance criteria
  • Data access and security requirements
  • Transparent pricing and change control process
  • Code and data handover plan

Testimonials (anonymised)

  • “Provided the methodological rigour and operational support we needed to scale our evaluation nationally. Clear, actionable findings.” — Government agency (anonymised)
  • “Their predictive modelling translated directly into targeted campaigns that lifted retention and reduced acquisition costs.” — Telco client (anonymised)

Final note — evidence that drives decisions

Quantitative evidence should reduce uncertainty, not compound it. At Research Bureau we prioritise clarity, reproducibility, and stakeholder alignment so your organisation can act with confidence. Whether your challenge is scientific, commercial, or policy-oriented, our team translates data into defensible, decision-ready insight.

Ready to proceed? Share project details for a quote via the contact form, click the WhatsApp icon, or email [email protected]. We’ll reply with a scoped proposal and next steps.