A New Perspective on Clinical Scale Validation: Leveraging Sentence Embeddings for Pre-Validation Semantic Analysis

Traditional clinical scale development is a lengthy and resource-intensive process, particularly during the empirical validation phase. This paper proposes a paradigm shift by introducing a computational pre-validation framework that leverages sentence embedding models to assess semantic properties of scale items prior to data collection. By transforming items into high-dimensional vector representations and analyzing their relationships within an MTMM-like structure, our method identifies potential item ambiguity, evaluates construct coherence, and tests discriminant validity at an early stage. We demonstrate its utility using widely used clinical instruments-including the SNAP-IV, CBCL Aggressive Behavior scale, and DSM-5-TR Oppositional Defiant Disorder (ODD) criteria-revealing semantic patterns that align with established psychometric properties. While not intended to replace traditional validation, this approach offers a novel, cost-effective complement that can inform early revisions and reduce pilot testing cycles. We develop an open-source R package "embedScaleValid" to facilitate practical implementation. This work introduces a new perspective in clinical assessment science, integrating advances in NLP to enhance the efficiency and validity of psychological measurement tools.