Methodology

Full transparency on how we compute career recommendations — the validated instruments, the direction of every contribution, and the specific numeric weights we use.

The three tiers of scientific backing

Every element of our recommendation system falls into one of three tiers, and we label each explicitly:

  1. Validated psychometrics — the actual instruments we run (Holland RIASEC, Big-5 BFI-2, MBTI, SRQ-A, Vallerand Passion, VARK, Schwartz Values, ICAR). Peer-reviewed, cited, real.
  2. Research-informed direction — the direction each instrument contributes to each axis is defensible from published literature. Effect sizes come from Schmidt & Hunter (1998), Sackett et al. (2017), and instrument-specific meta-analyses.
  3. Pragmatic engineering weights — the exact numeric weights (e.g. RIASEC 0.30, Big-5 Agreeableness 0.08) are our engineering defaults, not derived from a meta-analysis. We calibrate them internally and publish the values here so the choice is transparent.

Validated instruments (Tier 1)

  • Holland RIASEC — Holland (1997). Career interest inventory. Cronbach's α = 0.80–0.90.
  • Big-5 (BFI-2) — John, Naumann & Soto (2008). Personality dimensions. Cronbach's α = 0.84–0.88.
  • MBTI — Myers-Briggs. Test-retest reliability 0.60–0.70. Psychometrically contested but widely used.
  • SRQ-A — Ryan & Connell (1989). Self-Determination Theory motivation quality.
  • Vallerand Passion Scale — Vallerand et al. (2003). Harmonious vs Obsessive passion.
  • Schwartz Values — Schwartz (1992). 10 universal human values across 70+ cultures.
  • ICAR — Condon & Revelle (2014). Open-source cognitive reasoning; CHC-aligned.
  • VARK — Fleming (2001). Learning styles.

The four Ikigai axes

❤️ Love — what you enjoy

Weights: interest_content 0.70 · personality_fit 0.30 · RIASEC 0.30 · Vallerand passion 0.20. Includes SRQ-A multiplier (0.6–1.4 based on Relative Autonomy Index).

💪 Good at — where you excel

Weights: subject_aptitude 0.25 · strength_match 0.15 · follow_through 0.05 · CHC 0.30 · behavioural taste-test 0.25. Weights rebalance when instruments are missing.

💰 Paid for — economic viability

Weights: salary_level 0.50 · salary_consistency 0.20 · education_ROI 0.30. Big-5 Neuroticism tilts consistency weight by ±0.10.

🌍 World needs — contribution

Weights: mission 0.20–0.30 · Schwartz prosocial 0.15 · market_demand 0.22–0.25 · AI_durability 0.22–0.25 · sustainability 0.20–0.21 · Big-5 Agreeableness 0.08.

The composite

The four axes converge through a harmonic mean (deliberately punishing to any single weak axis, faithful to the Ikigai model). Then a piecewise linear calibration maps raw scores 0–10 to a displayed 0–10 range where accurate matches read as 7+.

Calibration formula:

  • raw ≤ 4 → displayed = raw × 1.75
  • raw > 4 → displayed = 7 + (raw − 4) × 0.5

So raw 4 = displayed 7, raw 5 = displayed 7.5, raw 6 = displayed 8, raw 10 = displayed 10. Purely a UX choice, not a psychometric one.

Two ranker adjustments before calibration

  • Authenticity multiplier — decision drivers from the retrospective intake produce a −1..+1 score, applied as 1 + 0.30 × score on Ikigai-seed component weights. Grounded in Sheldon & Elliot (1999) self-concordance model.
  • Regret penalty — past-decision regrets keyword-matched to SOC prefixes; matching careers get raw × (1 − penalty). Pragmatic.

What's proprietary vs validated

  • The Career DNA quiz, Career Taste Test, and the personalisation questionnaire (Ikigai seed) are proprietary discovery tools — not validated psychometrics. They contribute signal but shouldn't replace Holland / Big-5.
  • The Ikigai four-circle Venn diagram is a 2014 Western adaptation by Marc Winn, not authentic traditional Japanese ikigai. Useful framework nonetheless.

Data sources

  • Occupation profiles — U.S. Bureau of Labor Statistics (BLS) Occupational Employment Statistics + O*NET Database
  • University data — College Scorecard (US Dept. of Education) + NIRF (India) + UCAS (UK) + public university websites
  • India-specific salaries — aggregated from public reports (PayScale, Ambition Box, Glassdoor)
  • AI-exposure scores — Felten, Raj & Seamans (2023)

Selected citations

  • Schmidt & Hunter (1998). "The validity and utility of selection methods in personnel psychology." Psychological Bulletin.
  • Sackett, Zhang, Berry & Lievens (2017). "Revisiting meta-analytic estimates of validity in personnel selection." Journal of Applied Psychology.
  • Ryan & Connell (1989). "Perceived locus of causality and internalization." Journal of Personality and Social Psychology.
  • Vallerand et al. (2003). "Les passions de l'âme: On obsessive and harmonious passion." J. Pers. Soc. Psychol.
  • John, Naumann & Soto (2008). "Paradigm shift to the integrative Big Five trait taxonomy."
  • Holland (1997). Making vocational choices: A theory of vocational personalities and work environments.
  • Condon & Revelle (2014). "The International Cognitive Ability Resource." Intelligence.
  • Schwartz (1992). "Universals in the content and structure of values." Advances in Experimental Social Psychology.
  • Sheldon & Elliot (1999). "Goal striving, need satisfaction, and longitudinal well-being."
  • Felten, Raj & Seamans (2023). "Occupational, industry, and geographic exposure to AI."

Last updated: 2026-07-14. This methodology page is versioned and updated whenever the weights change.