Expanding psychometrics with pretrained language models: Evaluating pseudo-factor analysis in applied and multilingual contexts

The integration of Pretrained Language Models (PLMs) into psychometrics introduces new opportunities for scale development and validation. Pseudo-Factor Analysis (PFA), which employs item-level embeddings as proxies for human responses, offers a data-efficient alternative to Exploratory Factor Analysis (EFA). This study examines PFA’s capacity to replicate EFA outcomes across clinical and health psychology measures in English and Italian. PFA showed high performance in English (factor recovery up to 100%, congruence >.90), while Italian results were more variable. Findings highlight both the promise and language sensitivity of PFA, underscoring its potential for cross-linguistic psychometric applications.