Research Analytics Dashboard

Production-grade survey analytics delivered for real research clients at the Federal University of Health Sciences, Ila-Orangun (FUHSI) — four studies, 1,471 respondents. Every headline number on this page comes from the verified statistical pipeline: full methodology, aggregate tables and figures are open in the analysis repository.

raw survey data → cleaning & scoring → reliability (α / KR-20) → χ² · Kruskal-Wallis · logistic regression → figures & workbook → APA report
1,471
RESPONDENTS ANALYSED
4
FULL STUDIES DELIVERED
95
AGGREGATE RESULT TABLES
66
PUBLICATION-STYLE FIGURES

1 · Malaria Prevention KAP

Knowledge & practice of malaria prevention among undergraduates — N = 321 · Orem's Self-Care Deficit framework

knowledge 8.78/10 — good: 83.8% practice 29.39/40 — good: 49.5% H1 χ²=20.75, p=0.0004 ✓ REJECT H0 Spearman ρ=0.39, p<0.001 ITN ownership 50.2%

⚡ THE KNOWLEDGE–PRACTICE GAP — category distributions

83.8% hold good knowledge, but only 49.5% translate it into good practice — the gap is structural (comfort, cost, net maintenance), not educational.

⚡ THE ITN LIFECYCLE FUNNEL — where each stage loses half

Ownership (50.2%) → nightly use (5.3%) → replacement intent (44.2%): every stage of the net lifecycle leaks. 45.5% report discomfort sleeping under nets; 200 Level is a practice dip (27.6 → 25.7) despite higher knowledge.

📈 KNOWLEDGE RISES WITH LEVEL, THEN PLATEAUS (Kruskal-Wallis H=25.3)

Smart insights

  • ›98.1% know nets are the best defence, 50.2% own one, 5.3% sleep under one nightly — the failure point is comfort & maintenance, not awareness.
  • ›Each extra correct knowledge item gives OR 1.51 for ITN ownership (95% CI 1.27–1.79) — knowledge works, but saturates.
  • ›The 200-Level practice dip is the actionable anomaly — a targeted refresher beats more 100L orientation.

2 · Substance Abuse & Perceived Health Implications

Prevalence, drivers & harm awareness — N = 395 · Health Belief Model framework

ever-use 15.9% past-3-month 9.4% problem use 5.6% (CAGE-type) α 0.91–0.94 across scales male aOR=10.13, p=0.001

⚡ PREVALENCE CASCADE — use concentrates fast

A leaky pipeline: ever-use (15.9%) narrows to current use (9.4%) and a concentrated problem group (5.6%) — a brief-intervention target of ~22/395 students.

⚡ WHAT SUBSTANCES — alcohol is the gateway (share of sample)

📈 TOP DRIVERS OF USE (strongly agree/agree) — peer pressure ~97.7% is near-saturated

Smart insights

  • ›An awareness–behaviour gap, not a deficit: harm recognition ≥95.5% on all 12 items (mean 4.54/5), prevention endorsement ~97% — information campaigns are already saturated.
  • ›Male students carry ~10× the odds of ever-use — gender-targeted programming is statistically justified, not optional.
  • ›Peer pressure at 97.7% means the lever is normative campaigns ("most students don't use") delivered peer-to-peer.

3 · Internet Access & Self-Directed Learning Readiness

Digital equity & SDL (SDLRSNE, α=0.92) — N = 379

device ownership 99.5% mobile-data dependent 93.4% daily access 66.0% SDL 100.8/120 — high: 82.6% H1 Pearson r=0.371, p<0.001 ✓ H2 H=11.745, p=0.0083 ✓

⚡ SDL READINESS DECLINES WITH SENORITY (Kruskal-Wallis, Bonferroni: 200 vs 400 p=0.016)

100L 103.6 → 400L 99.0: "more experienced = more self-directed" is empirically backwards in this sample — 300/400L scaffolding is the priority.

⚡ SDL SUBSCALES — self-control is the weak spot (scale 1–5)

📈 THE ACCESS PARADOX — devices are universal, quality isn't

Smart insights

  • ›With 99.5% device ownership, the differentiator is what kind of access: 93.4% depend on personal mobile data and only 78.4% call it reliable — campus Wi-Fi investment beats device giveaways.
  • ›Frequency of access (ρ≈0.30) correlates with SDL readiness better than duration — a consistent daily study rhythm beats occasional long sessions.
  • ›Internet access predicts SDL readiness (r=0.371) — but with IT support, not curricula: the 82.6% "high readiness" band is ready for peer-mentoring pilots today.

4 · Menstrual Irregularities — KAP & Health-Seeking Behaviour

Knowledge, attitudes & care-seeking among female undergraduates — N = 376 · HBM framework

knowledge 10.63/12 — good: 69.1% positive attitude 52.7% ever-experienced 68.4% H1 χ²=6.865, p=0.0088 ✓ REJECT H0 knowledge↔self-efficacy r=0.50

⚡ THE CARE-SEEKING LEAK — knowledge & intent don't reach the clinic

Among ever-experienced (68.4%), only 42.0% sought medical care; the campus health centre serves just 13.8%. The top barrier — "it would resolve on its own" — cited by 70.7% of the sample.

Smart insights

  • ›Symptom normalisation, not ignorance, suppresses care: 69.1% good knowledge + 88.0% future intent, yet the modal first response to irregular cycles was to ignore it.
  • ›Knowledge→self-efficacy is the strongest measured link (r=0.50) and self-efficacy is the most trainable HBM construct — pair a "red-flag cycle" module with a concrete action script ("book the clinic within X days").
  • ›Knowledge predicts future intent (OR=1.22, p=0.005) and each level step down reduces it (OR=0.59) — juniors need targeted first-contact support, senior-style self-referral won't reach them.

HBM: KNOWLEDGE CORRELATES WITH (Pearson)

Full reproducibility: every figure and table behind these charts is generated by committed Python pipelines (pandas · scipy · statsmodels · matplotlib) — plus the complete APA-style report, Excel workbook and documentation.

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