Addressing Discrepancies in the Assessment Emotional Granularity: Development of a Novel Method Using Open-Ended Descriptions.

Emotional granularity (EG), the ability to differentiate between specific emotions with a high level of complexity and precision, is commonly assessed behaviorally through experience sampling methods (ESM). Participants rate the intensity of their momentary emotions over several days, and their ratings are then used to compute an EG index via within-person intraclass correlations (ICCs). Recently, an alternative approach has been developed to code affective natural language descriptions using two indices, namely the specificity index and the nuance score. Yet, these two measures overlook some relevant aspects, such as the actual richness and variability of affective vocabulary. Moreover, preliminary findings suggested that EG as computed via ICCs is unrelated to both the specificity index and the nuance score. The present study proposes a novel mathematical derivation of an EG index calculated from open-ended descriptions of emotional states. Moreover, we explore whether the ICCs EG index correlates with EG assessed through natural language descriptions. Sixty females suffering from chronic pelvic pain participated in a 1-month diary study. For each pain episode, participants freely narrated their pain experience focusing on its affective correlates and rated their affective experience using a set of 14 negative emotional adjectives. Concerning results, we expect that EG measured via predetermined adjectives will be unrelated to the specificity index and the nuance score but will correlate with our new derivation. This study may provide new evidence about the different methods commonly used in EG assessment to determine if they yield comparable results and are, therefore, equally valid.