Psychometric validation for an AI assessment platform
A validation layer, LLM benchmarks and item repair for a venture-backed AI personality-assessment platform.
- tension-detection accuracy (vs 56% / 46%)
- 70%
- respondents
- 15,000+
- Cronbach's α after repair
- .46 → .82
- items
- 257 → <190
The Challenge
A venture-backed AI personality-assessment platform needed its AI-generated content checked against real psychometric evidence, and its six-framework assessment battery brought up to measurement standard.
The Approach
I built a rules-based validation layer grounded in real correlations across 15,000+ respondents, then used it to benchmark the platform's AI-generated content against Claude Opus and GPT. Underneath that sat classic test construction and validation – factor analysis, item revision, reliability and disparate-impact analysis – on a six-framework battery with 9,500+ respondents: three rounds of field-tested item repair, and cross-instrument analysis to shorten the battery.
Key Features
- Rules-based validation layer grounded in correlations across 15,000+ respondents
- Benchmark of AI-generated content against Claude Opus and GPT on psychometric-tension detection
- Factor analysis, item revision, reliability and disparate-impact analysis
- Six-framework battery, 9,500+ respondents, three rounds of field-tested item repair
- Cross-instrument analysis to cut redundant items
- Related work: certification reports for assessment institutes
Results & Impact
- 70% accuracy on psychometric-tension detection, versus 56% for Claude Opus and 46% for GPT
- Weak scales raised from Cronbach's α .46 to .82
- Battery cut from 257 items to under 190 with no loss of measurement quality