Analytics

Analytics support for theses & manuscripts

From data cleaning and modeling to publication-ready figures, we deliver transparent, reproducible analytics aligned to your rubric or target journal.

Plagiarism-checked Reproducible scripts Methods & Results write-up

What’s included

Scoped plan with milestones, datasets required, and expected outputs.
Data preparation (cleaning, coding books, outlier/assumption checks, tidy data).
Statistical analysis (descriptives, inference, regression, GLM/GLMM, time-series as needed).
Reproducible outputs (scripts, annotated notebooks, tables/figures ready for submission).
Write-up support for Methods/Results with effect sizes, confidence intervals, and limitations.

Great for

Thesis chapters Journal submissions Pilot studies Grant methods

Toolstack

R Python SPSS Stata Power BI Excel

Sub-services

Data cleaning & coding

Handling missingness, outliers, recoding, normalization; codebooks & data dictionaries.

Exploratory analysis (EDA)

Descriptives, cross-tabs, correlation, principal plots to understand patterns.

Classical inference

t/ANOVA/ANCOVA, χ², non-parametric tests, multiple-comparison control.

Regression & GLM/GLMM

Linear/logistic/Poisson/neg-bin; random effects; marginal means & contrasts.

Survey analytics

Reliability (α/ω), EFA/CFA (intro), scoring, weights, complex sampling notes.

Time-series (intro)

Seasonality & trend, ARIMA/ETS, intervention checks, forecast visuals.

Visualization

Journal-style figures, model diagnostics, effect plots, tables for DOCX/LaTeX.

Typical domain use-cases

Social sciences

Treatment effects, survey scales, regression/GLM; clear assumptions & effect sizes.

Health & medical

Outcomes (binary/count/continuous), mixed models, CONSORT-style tables.

Engineering & CS

Performance metrics, experimental design, error analysis, reproducible pipelines.

Business & management

A/B, cohorts, drivers analysis, forecasting, dashboards for insights.

Models & tests (quick reference)

FamilyTypical useNotes
t/ANOVA/ANCOVAGroup comparisonsPost-hoc with correction; assumptions reported
Regression (OLS)Continuous outcomesDiagnostics, collinearity checks, influence
Logistic/Poisson/Neg-BinBinary/Count outcomesOdds/Rate ratios with CI; dispersion checks
Mixed (LMM/GLMM)Clustered/RepeatedRandom intercept/slope; marginal means
Time-series (ARIMA/ETS)Trends & forecastStationarity, seasonality, error diagnostics
Non-parametricRobust to violationsMann-Whitney, Kruskal-Wallis, etc.

We tailor methods to your question, data structure, and rubric/journal rules; transparent assumptions and limitations are documented.

Engagement examples

Quick Analysis (8–12 hrs)
Starting scope

Targeted clean-up + basic models + 1 figure/table.

Custom quote
Request proposal
Chapter-Ready (25–35 hrs)
Most popular

Full EDA + inference/regression + 3–5 figures/tables + write-up.

Custom quote
Request proposal
Manuscript Pack (50–70 hrs)
Best for submission

Advanced models + journal formatting + response-to-reviewers support.

Custom quote
Request proposal

Rates depend on scope, domain complexity, data condition, and turnaround. You’ll get a precise quote after discovery.

Recent snapshots

Healthcare RCT (GLMM)

Modeled patient-reported outcomes with random effects; reported OR with 95% CI and adjusted p-values; CONSORT-style tables.

Education survey (scales)

Reliability, EFA, composite scoring; regression for predictors; APA figures for manuscript.

Operations (forecasting)

Monthly demand time-series; ARIMA with diagnostics; 3-month forecast uncertainty bands.

Data privacy & integrity

  • Confidential handling; NDAs available; version-controlled folders & backups
  • Original, cited work; plagiarism-checked before delivery
  • Transparent assumptions, diagnostics, effect sizes, and limitations documented
  • Ethics/IRB: we assist with methods summaries and de-identification plans (approvals remain with your institution)

Process & timeline

1Discovery

Clarify aims, data availability, rubric/journal requirements.

2Scope & quote

Milestones, dates, and deliverables agreed.

3Data & setup

Secure transfer, templates, codebook, backups.

4Analysis sprints

Iterative models/figures shared for feedback.

5Write-up

Methods/Results with effect sizes & limitations.

6Handover

Scripts, data (as permitted), figures, and change log.

Typical deliverables

  • Cleaned dataset (where permissible) with a data dictionary/coding book
  • Reproducible scripts (R/Python/SPSS/Stata) or annotated notebooks
  • Publication-ready tables and figures (DOCX/PNG/PDF/LaTeX)
  • Methods & Results write-up with assumptions, effect sizes, and references

FAQ

Yes—APA, MLA, Chicago, Harvard, IEEE, AMA, or journal-specific author guidelines.

Absolutely. You receive scripts (R/Python/SPSS/Stata), figures, and (when allowed) datasets plus a change log.

We help with methods summaries, consent language suggestions, and de-identification plans. Final IRB approval remains with your institution.

We report assumptions, diagnostics, effect sizes, confidence intervals, and limitations. Choices are justified in plain language.

Small tasks: days. Full chapters/manuscripts: weeks with staged milestones agreed before kickoff.

DOCX, PDF, LaTeX, XLSX/CSV, and .R/.py/.sav/.do as applicable. Tell us your preference at discovery.

Yes—coding frameworks, reliability checks, theming (NVivo/ATLAS.ti) and quantitative integration if needed.

Yes—based on your research question, design, and data structure. We’ll also list alternatives and trade-offs.

Yes—journal-ready tables/figures and references styled per the required guide.

By scope, data condition, complexity, and turnaround. You’ll receive a clear plan and quote after discovery.

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