Data di Pubblicazione:
2024
Citazione:
Identifying and Qualifying Deviant Cases in Clusters of Sequences: The Why and The How / R. Piccarreta, E. Struffolino. - In: EUROPEAN JOURNAL OF POPULATION. - ISSN 0168-6577. - 40:1(2024), pp. 1.1-1.19. [10.1007/s10680-023-09682-3]
Abstract:
Sequence analysis is employed in different fields—e.g., demography, sociology,
and political sciences—to describe longitudinal processes represented as sequences
of categorical states. In many applications, sequences are clustered to identify rel-
evant types, which reflect the different empirical realisations of the temporal pro-
cess under study. We explore criteria to inspect internal cluster composition and
to detect deviant sequences, that is, cases characterised by rare patterns or outliers
that might compromise cluster homogeneity. We also introduce tools to visualise
and distinguish the features of regular and deviant cases. Our proposals offer a more
accurate and granular description of the data structure, by identifying—besides the
most typical types—peculiar sequences that might be interesting from a substan-
tive and theoretical point of view. This analysis could be very useful in applications
where—under the assumption of within homogeneity—clusters are used as outcome
or explanatory variables in regressions. We demonstrate the added value of our pro-
posal in a motivating application from life-course socio-demography, focusing on
Italian women’s employment trajectories and on their link with their mothers’ par-
ticipation in the labour market across geographical areas.
Tipologia IRIS:
01 - Articolo su periodico
Keywords:
Cluster analysis; Flagged index plot; Index plot; Sequence analysis; Visualisation
Elenco autori:
R. Piccarreta, E. Struffolino
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