Emotion regulation (ER) variability refers to how individuals vary their use of ER strategies across time. It helps individuals to meet contextual needs, underscoring its importance in well-being. The theoretical foundation of ER variability recognizes two constituent processes: strategy switching (e.g., moving from distraction to social sharing) and endorsement change (e.g., decreasing the intensity of both distraction and social sharing). ER variability is commonly operationalized as the standard deviation (SD) between strategies per observation (between-strategy SD) or within a strategy across time (within-strategy SD). In this paper, we show that these SD-based approaches cannot sufficiently capture strategy switching and endorsement change, leading to ER variability indices with poor validity. We propose Bray-Curtis dissimilarity, a measure used in ecology to quantify biodiversity variability, as a theory-informed ER variability index. First, we demonstrate how Bray-Curtis dissimilarity is more sensitive than SD-based approaches in detecting ER variability through two simulation studies. Second, assuming that higher ER variability is adaptive in daily life, we test the relation between ER variability and negative affect (NA) in three experience sampling method (ESM) datasets (total N = [70, 95, 200], number of moment-level observations = [5040, 6329, 14098]) At both the moment-level and person-level, higher Bray-Curtis dissimilarity predicted lower NA more consistently than SD-based indices. We conclude that Bray-Curtis dissimilarity may better capture moment-level within-person ER variability and could have implications for studying variability in other multivariate dynamic processes. The paper is accompanied by an R tutorial and practical recommendations for using Bray-Curtis dissimilarity with ESM data.