Life Course Data In Demography And Social Sciences: Statistical And Data-Mining Approaches

Abstract

This paper has essentially a methodological purpose. In a first section, we shortly explain why demographers have been relatively reluctant to implement the life course paradigm and methods, while the quantitative focus and the concepts of demographic analysis a priori favored such implementation. A real intellectual crisis has been needed before demographers integrated the necessity to face up the challenge of shifting "from structure to process, from macro to micro, from analysis to synthesis, from certainty to uncertainty" (Willekens, 1999, p. 26). This retrospective look also shows impressive progresses to promote a real interdisciplinarity in population studies, knotting the ties between demography and the social sciences. However, we also note that the success of multivariate causal analyses has been so rapid that some pitfalls are not always avoided. In Section 2, we focus on statistical methods for studying transitions. First, readers mind is refreshed about regression like models, and then we consider the issue of population heterogeneity. We show how it could affect results interpretation, and illustrate the interest of robust estimates and of the notion of shared frailty to deal with it. We also present Markovian modeling. Though less popular than regression event history models, Markovian models are specifically well suited for studying successive transitions between states observed at periodic time. In Sections 3, we promote some tools from the developing field of data mining, with special attention on the mining of frequent sequences and induction trees. These highly flexible heuristic tools can, among others, handle trajectories. Hence, they may prove very useful to face the deficit of knowledge on trajectories we observe between standard demographic analysis and causal research.

RE-EM trees: a data mining approach for longitudinal and clustered data

“…Ritschard et al (2008) discussed data mining applications in the somewhat-related topic of event histories (although in that context the event responses are categorical rather than numerical). Ritschard and Oris (2005) applied classification trees to such data, taking lagged response values as potential predictors, but still not treating the response variable as inherently multidimensional. Other papers have considered data mining methods other than trees for longitudinal data.…”

Principles and Prospects of the Life Course Paradigm

“…Such models are known as multilevel (or mixed effects or hierarchical) models (e.g. Courgeau, 2000;Snijders, 2002;Ritschard and Oris, 2005).…”

Sequence Analysis and Transition to Adulthood: An Exploration of the Access to Reproduction in Nineteenth-Century East Belgium

“…However, this global pattern was created by an impressive diversity of trajectories, a diversity that illustrates the importance of individual agency in a highly constrained context for those who decided to stay in the villages. The completely unexpected discovery of a group, both on the male and the female sides, able to triumph over the norm of a proper age at marriage-moreover, in a demographic regime where marriage played such a crucial role-proves the performance of the data-mining techniques (Ritschard and Oris 2005). We were so structured by the literature and previous results that we simply never looked at those people until they appeared in group 3 of Figs.…”