Evidence map›Paper›PMID 41136554›Full record

ArticleScientific reports2025

Rethinking psychometrics through LLMs: how item semantics shape measurement and prediction in psychological questionnaires.

Federico Ravenda, Antonio Preti, Michele Poletti, Antonietta Mira, Andrea Raballo

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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1 · What the graph read from it

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3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Federico RavendaEuler Institute, Faculty of Informatics, Università della Svizzera italiana, Lugano, Switzerland. federico.ravenda@usi.ch.
Antonio PretiDepartment of Neuroscience, University of Turin, Turin, Italy.
Michele PolettiDepartment of Mental Health and Pathological Addiction, Child and Adolescent Neuropsychiatry Service, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy.
Antonietta MiraEuler Instituite, Faculty of Economics, Università della Svizzera italiana, Lugano, Switzerland.
Andrea RaballoChair of Psychiatry, Faculty of Biomedical Sciences, Università della Svizzera italiana, Lugano, Switzerland.

Funding

SNFS 200557
6 · The paper itself

Abstract

Psychological questionnaires are typically designed to measure latent constructs by asking respondents a series of semantically related questions. But what if these semantic relationships, rather than reflecting only the underlying construct, also impose their own structure on the data we collect? In other words, to what extent is what we "measure" in questionnaires shaped a priori by item semantics rather than revealed solely a posteriori through empirical correlations? To examine this epistemological question, we propose LLMs Psychometrics, a novel paradigm that harness LLMs to investigate how the semantic structure of questionnaire items influences psychometric outcomes. We hypothesize that the correlations among items partly mirror their linguistic similarity, such that LLMs can predict these correlations-even in the absence of empirical data. To test this, we compared actual correlation matrices from established instruments-the Big 5 Personality (Big 5) and Depression Anxiety Stress Scale (DASS-42)-with the semantic similarity structures computed by LLMs. Among the top 3 semantically similar items, the empirically most correlated item was found in 95% of DASS cases and 82% of Big 5 cases. Building on this, we developed PsychoLLM, a neural proof-of-concept architecture, which uses item semantics to predict responses to new items-demonstrated with the Generalized Anxiety Disorder-7 (GAD-7) and Patient Health Questionnaire-9 (PHQ-9). PsychoLLM achieved  70% accuracy when predicting one scale's responses from the other, enabling new analyses based on semantic relationships. This work underscores an important epistemological implication for psychometrics: item semantics may influence measurement outcomes to varying degrees, more extensively than previously assumed. By leveraging LLMs to expose this a priori semantic structure, researchers can refine questionnaire design, assess data quality, and expand interpretive possibilities, ultimately inviting a reexamination of "what" and "how" we truly measure in psychology.

Indexed as

PsychometricsSemanticsAdultFemaleHumansMaleSurveys and QuestionnairesLarge Language ModelsPsychological QuestionnairePsychometrics

Identifiers

PMID41136554
PMCPMC12552711

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.