Synthetic Research Datasets Grounded in Real Data.

Generate psychometrically constrained survey data, exact SPSS (.sav), reproducible R scripts, and publication-ready APA 7th Chapter 4 Word dissertations—audited by the Jev Decision Engine and anchored to verified national public statistics.

Try Verified Presets:

Autonomous Methodology & Verification

Why Our Synthetic Data Passes University Scrutiny

Unlike generic random data generators, Sleek Academia Data embeds two institutional safeguards:

Institutional Gate 1

Embedded Jev Decision Engine

Every statistical path passes through Jev (TypeSafe AI). Jev assesses degrees of freedom, statistical power ($1 - \beta$), and methodological appropriateness before synthesising data. If a proposed test violates sample size thresholds (e.g. $N < 150$ for mediation), Jev blocks execution and routes you to an academically valid alternative.

🎯 Jev: Operational Gate ActiveRisk Score: < 0.10/3.00

Institutional Gate 2

Anchored to Real Public Macro Data

The engine connects to public records (KNBS, EPRA, Central Bank of Kenya, World Bank). If your study is on Electric Vehicle Adoption in Kenya, the engine queries verified baseline statistics ($\sim 5,300$ registered EVs, EPRA off-peak tariffs, charging infrastructure friction). Simulated Likert item means are anchored to empirical reality so trends never artificially collapse.

Public Sources: KNBS • EPRA • CBKAnchored Means

Interactive Synthesis Studio

Choose Your Synthesis Mode

1Upload Proposal

2Review Model

32-Option Path

Drop Your Chapters 1–3 Thesis Proposal

Upload your Word document (.docx) or PDF (.pdf). AI parses your framework and questionnaire items automatically.

Click to browse or drop proposal (.docx or .pdf)Fast parsing: Chapters 1, 2, 3 and Appendix

Or configure variables manually:

Real-World Public Macro Baseline

Kenya / East Africa

Approx. 5,300+ registered electric vehicles/motorcycles (EPRA 2024 report; 4.8% of new motorcycle registrations).

Empirical Macro Context:

Strong upward adoption trend driven by high fuel prices and EPRA e-mobility tariff incentives, constrained by charging station density.

• High upfront purchase capital vs internal combustion engine vehicles• Charging station infrastructure scarcity outside Nairobi metropolitan• EPRA special electric mobility tariff rate (KSh 16/kWh off-peak)

Source Verification:Energy and Petroleum Regulatory Authority (EPRA) Energy & Mobility Statistics (2024)

Select Deliverable Package Tier

TIER 1$19 / KSh 2,500

Data Only

SPSS .sav + Excel .xlsx + Codebook.

TIER 2$29 / KSh 3,800

Data + Raw Outputs

Tier 1 + SPSS Syntax .sps + R script + Viewer log.

Recommended

TIER 3$49 / KSh 6,400

Full Chapter 4

Tier 2 + APA 7th Word .docx with 3-line tables & narrative.

Deliverable Output Portal

Upload your proposal on the left. The wizard will extract your framework, confirm the 2-option path, and generate your SPSS and Word package here.

Guaranteed Package Deliverables:

SPSS Data (.sav) with labels & Likert codes

Excel Workbook (.xlsx) with Codebook sheet

Automated SPSS Syntax (.sps)

Reproducible R Script (.R)

APA 7th Chapter 4 Word Document (.docx)