Evaluating Large Language Model-Derived Outputs as Psychological Measures: A Five-Domain Psychometric Framework With Application to Daily Stress.

Background: Large language models (LLMs) are increasingly used to quantify psychological constructs from natural language, yet their outputs are typically evaluated only as predictions of an external label. When that label is itself imperfect, optimizing prediction conflates label agreement with construct measurement. No study has applied a multidomain measurement-oriented evaluation to LLM-derived psychological outputs. Objective: We applied 5 psychometric evaluation domains to LLM-derived outputs to test whether measurement-oriented evaluation surfaces properties that prediction-oriented evaluation does not: extraction repeatability, convergent validity with a lexicon-based tool, ecological concurrent validity decomposed into within-person and between-person components, incremental value in a 4-arm comparison with that tool, and method-blinded expert evaluation with structured error attribution. Methods: Thirteen linguistic features were extracted by GPT-5.2 (OpenAI) from 3556 person-days of daily voice narratives collected from 279 call center workers over 15 days via ecological momentary assessment. Features were evaluated across the 5 domains, with Linguistic Inquiry and Word Count, version 22, as the lexicon-based comparator. Multimodel robustness was assessed by re-extracting all features with Google Gemini 2.5 Flash. Results: Features showed high repeatability (mean intraclass correlation coefficient=0.987), construct-dependent convergent validity (Spearman ρ=0.08 to 0.79), and within-person tracking of same-day stress (negative emotion β=0.68). In a 4-arm comparison (baseline, baseline plus lexicon features, baseline plus LLM-derived features, and baseline plus both), LLM features increased explained variance in daily stress from 13.2% to 30.4%, versus 16.9% for the lexicon-based comparator. Among transcripts sampled for maximal method discordance, experts preferred LLM-derived profiles in 83.7% (261/312) of blinded decisions. Error analysis showed that 53% (35/66) of false-positive attributions reflected reference-label limitations, not model failures. Re-extraction with Google Gemini 2.5 Flash replicated key properties (person-level Conclusions: Prediction metrics alone are insufficient for evaluating LLM-derived psychological measures. Measurement-oriented evaluation revealed properties, including level-dependent effects and asymmetric error attribution, that have direct implications for how LLM-based assessment studies interpret their results. For clinical use, the measure is a complement to existing screening instruments for longitudinal monitoring, not a stand-alone diagnostic.