Machine learning item selection for short scale construction: A proof-of-concept using the SIMS.

This proof-of-concept paper provides evidence to support machine learning (ML) as a valid alternative to traditional psychometric techniques in the development of short forms of longer parent psychological tests. ML comprises a variety of feature selection techniques that can be efficiently applied to identify the set of items that best replicates the characteristics of the original test. In the present study, we integrated a dataset of 329 participants from published and unpublished datasets used in previous research on the Structured Inventory of Malingered Symptomatology (SIMS) to develop a short version of the scale. The SIMS is a multi-axial self-report questionnaire and a highly efficient psychometric measure of symptom validity, which is frequently applied in forensic settings. State-of-the-art ML item selection techniques achieved a 72% reduction in length while capturing 92% of the variance of the original SIMS. The new SIMS short form now consists of 21 items. The results suggest that the proposed ML-based item selection technique represents a promising alternative to standard psychometric correlation-based methods (i.e. item selection, item response theory), especially when selection techniques (e.g. wrapper) are employed that evaluate global, rather than local, item value.