groupes a individuelles bis

Classical demography was grounded on the hypotheses of population homogeneity and of independence between studied events. Civil status data did not permit to verify these hypotheses, which only detailed surveys permit to remove.

Towards a complete event history analysis The growing number of detailed surveys undertaken in the 1980s that used probabilistic and statistical tools permitted the demographers to pass from aggregate data (number of individuals that met an event in a given year) to individual data (an individual with a number of known characteristics meets different events, at a given time and location, during his whole life). This leads to an explanatory approach, which brings out the intricacies of facts, the dependence between the different phenomena and detects through time how diverse individual characteristics may have influenced his behaviour.

Such an approach permits also to remove the hypothesis of homogeneity of the studied population. We assume here that the behaviours are not innate, but may change during the individual’s life through the personal experiences and successive attainments. The use of parametric and semiparametric methods, which generalise the regression method used in studies that do not introduce the time dimension, permit the introduction of time dependent characteristics in a consistent way.

Such an approach permits to remove the hypothesis of independence and the introduction of the effect of other events, not by excluding the individuals who knew them as proposed by the classical longitudinal analysis. It displays clearly their influence on the studied processes. The introduction of a nonparametric analysis of interactions between phenomena, generalises the analysis of one phenomenon, which was used by demographers until then.

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Introduction of space into demography
From the study of groups to the study of individual event histories
A contextual and multilevel analysis
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