Self-report measures of subjective time: An overview of existing measures and their semantic similarities

The proliferation of self-report measures for psychological constructs, often developed withoutsystematic evidence, raises concerns about construct redundancy and the validity of researchfindings. This study provides a comprehensive review of scales designed to assess subjectivetime, employing literature analysis and transformer-based natural language processing (NLP)methods to explore semantic relationships among these measures. Thirty scales meetinginclusion criteria were identified, revealing two primary clusters: temporal experience and timeperspective. Temporal experience encompasses diverse dimensions of how individualsperceive and conceptualize time, while time perspective focuses on orientations toward past,present, and future. Hierarchical clustering and semantic analysis highlighted overlaps anddistinctions. Our findings underscore recurring challenges in differentiating betweentheoretically distinct constructs, such as temporal orientation and time perspective, due tosignificant overlap in operationalizations. Furthermore, discrepancies between theoreticaldefinitions and scale content emphasize the importance of scrutinizing alignment to ensurevalidity. Automated semantic analysis offers a valuable tool for identifying redundancy andimproving measurement clarity, particularly given the proliferation of self-report measures inpsychology. However, methodological variations and domain expertise are crucial tointerpreting results meaningfully. This study demonstrates the utility of NLP in advancing theconceptual and empirical understanding of subjective time while highlighting the need for morerobust and distinct measurement approaches.