Abstract
Affective phenomena have noteworthy complexity and heterogeneity-shared experiences and emotions evoke distinct responses and risk for affective problems across individuals (e.g., higher rates in women than men). Yet by averaging across individuals, affective science research traditionally treats affect as homogenous. Directly modeling person-specific heterogeneity in affective complexity (AC)-like the granularity and covariation of affective experiences-is paramount for identifying shared (i.e., common; nomothetic) and/or unshared (i.e., personal; idiographic) features of AC. The present study applied a person-specific technique to capture heterogeneity in daily affect and risk for affective problems in men and women and leveraged personalized results to improve general understanding of AC. Young adults (n = 56; 25 female) reported affect on each of 75 days of an intensive longitudinal study. AC was modeled using p-technique (i.e., person-specific factor analysis), and its utility over traditional, between-person models of affect (i.e., bivariate positive and negative affect) was compared for prediction of risk for affective problems in women compared to men. A community detection network algorithm was then applied to estimate person-specific AC to develop an idiographically informed nomothetic model of AC. Person-specific analyses detected wide variation in AC across individuals (i.e., range of 2-8 factors). Relative to the traditional bivariate model, idiographic models had incremental utility for differentiating risk for affective problems by gender. Nomothetic review of idiographic results (via community detection) revealed distinct dynamics in positive and negative affect networks. Person-specific science holds particular promise for mapping heterogeneity in AC and uncovering risk pathways for affective problems. (PsycInfo Database Record (c) 2022 APA, all rights reserved).