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Abstract The inherent qualitative nature of textual data poses significant challenges for direct integration into statistical models. This paper presents a two-stage process for analyzing longitudinal textual data, offering a solution to this inherent challenge. The proposed model comprises (1) initial data preprocessing and sentiment extraction, followed by (2) applying a growth curve model to analyze the extracted sentiments directly. The paper also explores four distinct approaches for extracting sentiment scores in the dialogue, providing versatility to the proposed framework. The practical application of the proposed model is demonstrated through the analysis of an empirical longitudinal textual dataset. This framework offers a valuable contribution to the field by addressing the challenges associated with modeling qualitative textual data, providing a robust methodology for extracting and analyzing sentiments longitudinally. 

Belonging to a community is essential for wellbeing, but potentially unattainable for those dissimilar from a group. In the present work, we ask whether belongingness is better predicted by acting and thinking like peers or believing you act and think like peers. Students (N = 1181) reported their belonging and how much they, their friends, and an “average student” endorsed local behavioral norms and general values. We calculated difference scores for behaviors and values capturing perceived similarity to the average, actual similarity to the average, and accuracy around the norm. Key results indicate that perceived behavioral similarity to the average, when controlling for other differences, predicts belonging and most robustly mediates between identity and belonging. Using social network analysis, we find behavioral differences from friends are meaningfully linked to network density and racial homophily. Efficient interventions for enhanced belonging could highlight similarities between students and their peers. 

Academic Abstract Interpersonal synchrony, the alignment of behavior and/or physiology during interactions, is a pervasive phenomenon observed in diverse social contexts. Here we synthesize across contexts and behaviors to classify the different forms and functions of synchrony. We provide a concise framework for classifying the manifold forms of synchrony along six dimensions: periodicity, discreteness, spatial similarity, directionality, leader–follower dynamics, and observability. We also distill the various proposed functions of interpersonal synchrony into four interconnected functions: reducing complexity and improving understanding, accomplishing joint tasks, strengthening social connection, and influencing partners’ behavior. These functions derive from first principles, emerge from each other, and are accomplished by some forms of synchrony more than others. Effective synchrony flexibly adapts to social goals and more synchrony is not always better. Our synthesis offers a shared framework and language for the field, allowing for better cross-context and cross-behavior comparisons, generating new hypotheses, and highlighting future research directions.