Methodology

What is RIASEC?

RIASEC is a vocational interests model proposed by psychologist John Holland in 1959 and developed throughout his career (Holland, 1997). It classifies work-related interests into six types: Realistic, Investigative, Artistic, Social, Enterprising, and Conventional.

The model's validity is supported by over 60 years of research, including meta-analyses of hundreds of studies (Nye and colleagues, 2012; Hoff and colleagues, 2020) showing that people working in fields aligned with their RIASEC interests report higher career satisfaction and stronger academic performance.

Where the questions come from

All 48 questions you see come from the International Personality Item Pool (IPIP), a public repository of psychological assessment instruments maintained by Oregon State University.

Specifically we use the IPIP Basic Interest Markers scale developed and validated by Liao, Armstrong, and Rounds (2008), published in the Journal of Vocational Behavior. For the Spanish version, riasec-co uses the Río de la Plata Spanish adaptation by Cupani, Moran, Azpilicueta, and Piccolo (2019), published in the Electronic Journal of Research in Educational Psychology. All questions are public domain: anyone can reuse, translate, or audit them.

How your profile is computed

The computation follows the published IPIP-BIM procedure exactly. No neural networks, no generative models, no machine learning. It is deterministic arithmetic that any researcher can reproduce with the citation in hand.

1. 1. Per response
   Your value (1 to 5) is added to the score of the RIASEC type that question belongs to. If the question is reverse-keyed, we add (6 minus your response) instead of the direct value.
2. 2. The raw score per type
   Is the sum of effective responses to that type's questions. With 8 questions per type in the full quiz and a maximum response of 5, the raw score ranges from 8 to 40.
3. 3. The profile you see
   Is each type's raw score divided by the sum of all six. That's why the percentages always add to 100. They preserve the rank order of the raw sums but express them as a distribution.

See the exact code on GitHub

How programs are matched

Programs come from SNIES (Sistema Nacional de Información de la Educación Superior), Colombia's official Ministry of Education registry. Each program is classified into a broad CINE field per the CINE F 2013 AC taxonomy.

To connect your RIASEC profile to programs, we use a weighted mapping between each type and the broad CINE fields. Each match has a weight between 0 and 1: 1.0 indicates a primary connection, 0.5 to 0.7 a secondary connection. A program's score is the sum of (your interest share in the type) × (mapping weight) across all matches.

The full mapping is public and shown below. Any student or educator can review it:

Limitations we acknowledge

• The quick quiz has lower reliability
  With 12 questions (vs. 48 in the full version), only 2 questions touch each RIASEC type. Internal consistency is far below the academic standard (Cronbach's alpha greater than 0.80) achieved with 8 questions per type. It is a quick exploration tool, not a rigorous assessment.
• All questions are positively phrased
  Rigorous psychometric instruments also include reverse-phrased questions to detect acquiescence bias (the tendency to agree with everything). The public IPIP-BIM bank does not include reverse forms, so we are limited by the available dataset.
• The RIASEC-to-program mapping is the least validated part
  As explained above, the mapping to CINE fields is expert-based, not empirically derived. The scores you see are an informed orientation, not a validated prediction.
• RIASEC measures interests, not aptitude or destiny
  A RIASEC profile describes what interests you right now. It does not measure your capability or intelligence. And interests change with experience.
• The programs shown are a sample
  We show up to 15 programs from the SNIES universe; these are not the only options. Hundreds of other programs exist, plus non-university paths (technical, technology programs, SENA certifications, entrepreneurship) that the quiz does not list.

Privacy

Your responses and results never leave your device. Everything is stored in the browser's localStorage under the key hicotea-quiz-v2, accessible only from your browser.

We don't send your responses to any server, including Fundación Hicotea. To clear everything, just clear your browser data or use the Start Over button in the quiz.

Code and data

The quiz engine is open source. Anyone can view, audit, or propose changes to the code and data.

• affromero/riasec-co · Main repository: engine, mapping, data
• packages/core/src/scoring.ts · Exact implementation of profile computation
• data/canonical/ · IPIP item bank, RIASEC→CINE mapping, SNIES data

Academic citations

Each citation includes a verified link to the original source. If any link breaks, write to us and we will fix it.

1. Holland, J. L. (1997). Making vocational choices: A theory of vocational personalities and work environments (3rd edition). Psychological Assessment Resources. https://archive.org/details/makingvocational0000holl
2. Liao, H.-Y., Armstrong, P. I., and Rounds, J. (2008). Development and initial validation of public domain Basic Interest Markers. Journal of Vocational Behavior, 73(1), 159-183. https://doi.org/10.1016/j.jvb.2007.12.002
3. Cupani, M., Moran, V. E., Azpilicueta, A. E., and Piccolo, N. V. (2019). Alternate Forms Public Domain RIASEC Markers for Interests and Self-Efficacy: Spanish version. Electronic Journal of Research in Educational Psychology, 17(2), 359-382. https://ojs.ual.es/ojs/index.php/EJREP/article/view/2136
4. Nye, C. D., Su, R., Rounds, J., and Drasgow, F. (2012). Vocational interests and performance: A quantitative summary of over 60 years of research. Perspectives on Psychological Science, 7(4), 384-403. https://pubmed.ncbi.nlm.nih.gov/26168474/
5. Hoff, K. A., Song, Q. C., Wee, C. J. M., Phan, W. M. J., and Rounds, J. (2020). Interest fit and job satisfaction: A systematic review and meta-analysis. Journal of Vocational Behavior, 123, 103503. https://doi.org/10.1016/j.jvb.2020.103503
6. Nye, C. D., Prasad, J., and Rounds, J. (2021). The effects of vocational interests on motivation, satisfaction, and academic performance: Test of a mediated model. Journal of Vocational Behavior, 127, 103583. https://linkinghub.elsevier.com/retrieve/pii/S0001879121000555
7. IPIP: International Personality Item Pool, ipip.ori.org. The exact 48 items we use are published in the riasec-co repository (data/canonical/items_en.json and items_es.json). https://github.com/affromero/riasec-co/blob/main/data/canonical/items_en.json
8. SNIES: Sistema Nacional de Información de la Educación Superior, Colombian Ministry of Education. The program data we use comes from the public program search: https://hecaa.mineducacion.gov.co/consultaspublicas/programas