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Sequence analysis as a tool for family demography
No full textIn this chapter we describe the development of sequence analysis (SA) techniques to investigate the process of family formation and dissolution. Family structure has changed substantially in past decades, and family trajectories are more heterogeneous than they used to be in the past. The age at first marriage has increased in many, if not all, western societies; cohabitation has become a very standard stage in people’s family formation; divorce rates rose considerably since the 1970s, but their growth slowed down and even halted in some countries; and stepfamilies have become more and more common. All these trends imply not only changes in the timing of events, but also changes in the sequencing and the duration of events. Many aspects of family trajectories have been analyzed individually, without taking into account the interrelation among different events. However, it is necessary to look at the process of union formation and the subsequent family pathways from a holistic point of view. Sequence analysis is therefore the appropriate tool to analyze family histories, taking into account the timing, sequencing, and duration of events. In this chapter we discuss the way in which sequence analysis has been used so far in family demography, and illustrate the most relevant developments and innovative procedures relative to this technique. In the second part of the chapter we use data from the European Social Survey to illustrate an empirical application of sequence analysis and describe family trajectories across European countries
Family Trajectories and Health:A Life Course Perspective
Full text link<p>This paper investigates the role of family trajectory, i.e., the whole sequence of family events during the life course of early adults in shaping their health outcomes. Union formation and childbearing are jointly considered, since the two life domains are highly connected and their intersections may have an effect on health outcomes. Data come from wave I and wave IV of the National Longitudinal Study of Adolescent Health (Add Health) in the United States. The paper is divided in two parts. The first part focuses on family transitions and investigates if changes in timing (when events happen), quantum (what and how many transitions), and ordering (in what order), have an effect on the health of young women. In the second part, life course trajectories are classified into six groups representing different ideal-types of family trajectories and the association of these trajectories with health outcomes is explored. Results suggest that family trajectories play an important role on different health outcomes. Controlling for selection and background characteristics, precocious and "non-normative" transitions are associated with lower self-reported health and higher propensity of smoking and drinking.</p>
Classifying life course trajectories: a comparison of latent class and sequence analysis
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Educational attainment and allostatic load in later life: Evidence using genetic markers
No full textA Sequence-Analysis Approach to the Study of the Transition to Adulthood in Low- and Middle-Income Countries
Full text linkThis study investigates whether young people in low- and middle-income countries (LMICs) have experienced processes of destandardization of the life course similar to those observed in high-income societies. We provide two contributions to the relevant literature. First, we use data from 263 Demographic and Health Surveys (DHS) across 69 LMICs, offering the richest comparative account to date of women's transition to adulthood (TTA) patterns in the developing world. In so doing, we adopt sequence analysis and shift the focus from individual life-course events—namely first sexual intercourse, first union, and first birth—to a visually appealing approach that allows us to describe interrelations among events. By focusing on the analysis of trajectories rather than the occurrence of single events, the study provides an in-depth focus on the timing of events, time intervals between events, and how experiencing (or not) one event might have consequences for subsequent markers in the TTA in cross-national comparative perspective. Second, we identify clusters of TTA and explore their changes across cohorts by region and household location of residence (rural vs. urban). We document significant differences by macro-regions, yet relative stability across cohorts. We interpret the latter as suggestive of cultural specificities that make the TTA resistant to change and slow to converge across regions, if converging at all. Also, we find that much of the difference across cluster typologies ensues from variation related to when the transition begins (early vs. late), rather than from the duration between events, which tends to be uniformly quick across three out of four clusters
The sociogenomics of polygenic scores of reproductive behavior and their relationship to other fertility traits
No full textThe relationship between cognitive decline and a genetic predictor of educational attainment
No full textGenetic and environmental factors both make substantial contributions to the heterogeneity in individuals' levels of cognitive ability. Many studies have examined the relationship between educational attainment and cognitive performance and its rate of change. Yet there remains a gap in knowledge regarding whether the effect of genetic predictors on individual differences in cognition becomes more or less prominent over the life course. In this analysis of over 5000 older adults from the Health and Retirement Study (HRS) in the U.S., we measured the change in performance on global cognition, episodic memory, attention & concentration, and mental status over 14 years. Growth curve models are used to evaluate the association between a polygenic risk score for education (education PGS) and cognitive change. Using the most recent education PGS, we find that individuals with higher scores perform better across all measures of cognition in later life. Education PGS is associated with a faster decline in episodic memory in old age. The relationships are robust even after controlling for phenotypic educational attainment, and are unlikely to be driven by mortality bias. Future research should consider genetic effects when examining non-genetic factors in cognitive decline. Our findings represent a need to understand the mechanisms between genetic endowment of educational attainment and cognitive decline from a biological angl
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