Do Different Approaches in Population Science Lead to Divergent or Convergent Models?

Courgeau, Daniel

doi:10.1007/978-3-319-95420-2_2

Cited by 10 publications

(11 citation statements)

References 53 publications

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“…Another most important step to take is to combine sequence analysis with event history analysis. Recent research has begun to use this combination due to life course scholars' increasing awareness that the two methods must not compete, but rather complement each other in making up for their individual drawbacks (Bras, Liefbroer & Elzinga 2010;Courgeau 2016;Svensson, Lundholm, De Luna & Malmberg 2015). We support this direction in research and intend to contribute to it by, for example, identifying whether there are any significant associations between premature mortality risks and the specific life sequences found in this study.…”

Section: Concluding Discussionmentioning

confidence: 62%

“…The ability to conduct sequence analysis on a past population with disabilities from about 150-200 years ago makes our investigation novel in its approach, as quantitative measures are underused in recent social history and studies concerned with disability issues. Although quantitative methods dominate in the field of historical demography, sequence analysis has been of comparatively limited use, given that it is a method only recently developed and perhaps because it primarily shows descriptive outcomes (Abbott 1995;Abbott & Tsay 2000;Courgeau 2016;Dribe, Manfredini & Oris 2014;Kok 2007;Ritschard & Oris 2006;Schumacher, Matthijs & Moreels, 2013). Within this field, the most popular way to conduct life course research is to employ event history analysis in different ways, focusing on the outcome of one single event and its significant determinants.…”

Section: Introductionmentioning

confidence: 99%

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Sequence Analysis of How Disability Influenced Life Trajectories in a Past Population from the Nineteenth-Century Sundsvall Region, Sweden

Vikström¹,

Haage²,

Lundevaller³

2017

hlcs

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Historically, little is known about whether and to what extent disabled people found work and formed families. To fill this gap, this study analyses the life course trajectories of both disabled and non-disabled individuals, between the ages of 15 and 33, from the Sundsvall region in Sweden during the nineteenth century. Having access to micro-data that report disabilities in a population of 8,874 individuals from the parish registers digitised by the Demographic Data Base, Umeå University, we employ sequence analysis on a series of events that are expected to occur in life of young adults: getting a job, marrying and becoming a parent, while also taking into account out-migration and death. Through this method we obtain a holistic picture of the life course of disabled people. Main findings show that their trajectories did not include work or family to the same extent as those of non-disabled people. Secondary findings concerning migration and mortality indicate that the disabled rarely out-migrated from the region, and they suffered from premature deaths. To our knowledge this is the first study to employ sequence analysis on a substantially large number of cases to provide demographic evidence of how disability shaped human trajectories in the past during an extended period of life. Accordingly, we detail our motivation for this method, describe our analytical approach, and discuss the advantages and disadvantages associated with sequence analysis for our case study.

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Section: Concluding Discussionmentioning

confidence: 62%

Section: Introductionmentioning

confidence: 99%

Sequence Analysis of How Disability Influenced Life Trajectories in a Past Population from the Nineteenth-Century Sundsvall Region, Sweden

Vikström¹,

Haage²,

Lundevaller³

2017

hlcs

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“…These alternatives include feature-based and fuzzy clustering (Studer 2018) and non-dissimilarity-based methods such as those based on network representations of sequences (Cornwell 2018;Hamberger 2018), and Markov-based models (Helske et al 2018;Taushanov and Berchtold 2018). Alongside the two general papers (Courgeau 2018;Eerola 2018) in Part I, five papers demonstrate the benefit of combining SA with other methods to grasp the dynamics that drive the trajectories. SA is combined with survival models (Malin and Wise 2018;Lundevaller et al 2018;Rossignon et al 2018), with QCA (Borgna and Struffolino 2018), and with hidden Markov models (Helske et al 2018).…”

Section: Resultsmentioning

confidence: 99%

Sequence Analysis: Where Are We, Where Are We Going?

Ritschard

Studer

2018

Life Course Research and Social Policies

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“…In a broader perspective, one could also consider other aspects of time, for example`natural' time such as the cycle of the seasons and its relation with agricultural production (see Section 4), or the seasonal cycle of religious rules and practices and its impact on the occurrence of marriages and conceptions (Matsuo and Matthijs, 2018 Sequence analysis can be used in this case to examine, for example, the sequential patterns of housing trajectories (Mikolai and Kulu, 2019). Courgeau (2018) has thoroughly discussed the pros and cons of this approach, in comparison with event-history analysis and network analysis.…”

Section: Timementioning

confidence: 99%

Time and causality in the social sciences

et al. 2021

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This article deals with the role of time in causal models in the social sciences. The aim is to underline the importance of time-sensitive causal models, in contrast to time-free models. The relation between time and causality is important, though a complex one, as the debates in the philosophy of science show. In particular, an outstanding issue is whether one can derive causal ordering from time ordering. The article examines how time is taken into account in demography and in economics as examples of social sciences in which considerations about time may diverge. We then present structural causal modeling as a modeling strategy that, while not essentially based on temporal information, can incorporate it in a more or less explicit way. In particular, we argue that temporal information is useful to the extent that it is placed in a correct causal structure, thus further corroborating the causal mechanism or generative process explaining the phenomenon under consideration. Despite the fact that the causal ordering of variables is more relevant than the temporal order for explanatory purposes, in establishing causal ordering the researcher should nevertheless take into account the time-patterns of causes and effects, as these are often episodes rather than single events. For this reason in particular, it is time to put time at the core of our causal models.

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Do Different Approaches in Population Science Lead to Divergent or Convergent Models?

Cited by 10 publications

References 53 publications

Sequence Analysis of How Disability Influenced Life Trajectories in a Past Population from the Nineteenth-Century Sundsvall Region, Sweden

Sequence Analysis of How Disability Influenced Life Trajectories in a Past Population from the Nineteenth-Century Sundsvall Region, Sweden

Sequence Analysis: Where Are We, Where Are We Going?

Time and causality in the social sciences

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