Room 2062 (Second floor – Building U7). - Via Bicocca degli Arcimboldi, 8 – 20126 Milano
Prof. Marc Scott. Department of Applied Statistics, Social Science, and Humanities, New York University, Steinhardt School
Abstract
The life course perspective considers the entire history of individuals as the primary unit of analysis. There is empirical evidence and socio-behavioural theory supporting the notion that "history matters." The sequence analysis community of researchers has established robust methods for organising individual historical pathways into typologies that inform narratives of the life course. These are often linked to social position, providing deeper understanding of the constraints and variation in how lives evolve. Yet these "types" are also commonly viewed as rough proxies for something more subtle and potentially predictive of later life outcomes. When it is possible to identify a precise "feature" of the history, such as a single parent household, such measures can be incorporated into models for important later outcomes, including health and income. In this study, we approach the question of "what matters" in life course histories using two methods: clustering to produce typologies and a mathematical projection based on categorical functional data analysis. Each method distills the history into a feature set that is used in a predictive model. We compare and contrast the performance of these methods on a known dataset to uncover strengths and limitations of each methodology, informing best practices and new methodological research.
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