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

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.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2025-10-24
DOI
https://doi.org/10.1038/s41598-025-21289-8
Citations
2
Primary Topic
Mental Health via Writing
Type
article
Field-Weighted Citation Impact
4.88
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Michele Poletti, Antonietta Mira, Antonio Preti, Andrea Raballo et al.
2 citations
Scientific Reports
Mental Health via Writing
4.88
article

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

Michele Poletti, Antonietta Mira, Antonio Preti, Andrea Raballo, Federico Ravenda
article en
2 citations

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.

Scientific ReportsVol. 15(1)
University of Insubria (IT), Azienda Sanitaria Unità Locale di Reggio Emilia (IT), Repubblica e Cantone Ticino (CH), University of Turin (IT), Università della Svizzera italiana (CH)
Openalex Percentile: Top 5%
Mental Health via Writing
4.88
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.