Catálogo de publicaciones - libros

This chapter proposes a novel sequential mixed-method design that brings together the strengths of sequence analysis (SA) and qualitative comparative analysis (QCA). Both methods rely on an epistemological framework that combines analytical and holistic elements: they share the notion of analytically approaching social phenomena without disregarding their complexity. We apply SA as a first step to analyze the unfolding of phenomena over time. Specifically, through discrepancy analysis of state sequences, one can identify the time points when trajectories (start to) diverge from each other. In a second, explanatory step, QCA is applied to investigate whether, at such crucial time points, some particular configurations of factors are logically sufficient for the occurrence of a given state (or outcome). We provide an illustrative application to women’s employment trajectories in divided Germany by using data from the Adults Cohort of the German National Educational Panel (NEPS). By preserving the analytical and holistic perspective on trajectories, our proposed design highlights the dynamic of socio-demographic factors sufficient for women to be in employment or education at critical turning points over the life course. This design takes on the challenge, summarized in Abbott’s call for a ‘processual sociology’, to investigate both the dynamics of social phenomena as lineages of successive events and the complexity of contextual characteristics of ‘present’ moments. We conclude with an overview of research fields where our framework holds the promise of being applied fruitfully, namely life-course research, social stratification studies, policy evaluation, and comparative politics.

Life course data often consists of multiple parallel sequences, one for each life domain of interest. Multichannel sequence analysis has been used for computing pairwise dissimilarities and finding clusters in this type of multichannel (or multidimensional) sequence data. Describing and visualizing such data is, however, often challenging. We propose an approach for compressing, interpreting, and visualizing the information within multichannel sequences by finding (1) groups of similar trajectories and (2) similar phases within trajectories belonging to the same group. For these tasks we combine multichannel sequence analysis and hidden Markov modelling. We illustrate this approach with an empirical application to life course data but the proposed approach can be useful in various longitudinal problems.

The main aim of this paper is to describe the use of the Markovian-based Hidden Mixture Transition Distribution (HMTD) model for the clustering of longitudinal sequences of continuous data. We especially discuss the use of covariates to improve the clustering process. The HMTD is compared to the well-known Growth Mixture Model (GMM) that is considered here as a gold standard. Both models are applied to a sample of  = 185 adolescents, who are repeatedly evaluated for Internet overuse using the Internet Addiction Test (IAT). The best solution provided by the HMTD model has four groups and it uses five covariates. This solution is related to the subjects’ level of emotional well-being, body mass index, gender, and education track, but shows no relation with age. Compared to a GMM clustering, the HMTD solution provides highly interpretable results with quite equilibrate cluster size, while GMM tends to identify very small clusters allowing for less generalization.

This paper discusses the usefulness of divisive property-based and fuzzy clustering for sequence analysis. Divisive property-based clustering provides well-defined clustering membership rules. Aside from significantly simplifying interpretations of clustering, it is also useful when one plans to use the same typology in other samples or studies. We further enrich the methods by proposing new sets of sequence features that can be automatically extracted and used in the procedure. We then discuss the use of fuzzy clustering, where sequences belong to each cluster with an estimated membership strength. This approach is particularly useful when some sequences are thought to lie between two (or more) sequence types (i.e., sequences) or when only a weak structure is found in the data. This paper also discusses several methods by which to visualize a fuzzy clustering solution, and analyzes them with regression-like approaches. It also introduces R code to run all the discussed analyses; all the proposed developments are made available in the WeightedClusterR package.

In what ways do dual-earner couples organize their workdays and how do they (de)synchronize their daily activities? Using a multichannel sequence analysis approach, the paper tackles these questions. We consider the couples’ division of work-family activities in holistic terms by setting it within the context of everyday life, that is, the overall temporal pattern of combination of His and Her multiple activities. Our multichannel sequence analysis approach is based on a Lexicographic Index that seeks to overcome some optimal matching limits of the sequence analysis. The case-study concerns how Italian dual-earner couples organize their daily activities (sleep, personal care, work, moving, housework, free time), during a typical Monday to Friday work day, 7.00 am to 10.00 pm. The analysis, carried out using the data from the 2008 Italian Census on Time Use (the last one available), involves 873 couples where both partners filled the given diaries on the very same day. All the analyses confirm the idea that dual-earner couples package their life time mainly in accordance with their jobs and eventual children management. Moreover, the analyses show that this time packaging changes in relation to the level of education, social class and the occupational sector of the couple.

We propose a new measure to quantify the quality of binary sequences that can be meaningfully interpreted as series of successes and failures. We operationalize the concept of positive and negative sequences by formulating general properties that a quality measure must adhere to, construct a measure that fulfills these requirements, and show that such measure can be modeled in a theoretically meaningful way. We apply such measure of sequence quality to data from the Household, Income, and Labour Dynamics in Australia (HILDA) Survey over the period 2001 to 2013, and model the evolution of employment career quality after the occurrence of an initial spell of unemployment, providing a direct test of unemployment “scarring” theories. We define states of unemployment and inactivity as failures and those of employment as successes to predict whether prior unemployment leads to descending spirals into inactivity and joblessness or whether patterns of full career recovery exist. Our findings lend support to scarring theories by demonstrating that, despite recovery trends, career disparities among previously unemployed workers persist long after their first unemployment experience. We conclude discussing implications of the findings and proposing directions for future extensions of the measure.

A vast body of research examined changing employment relations and the ensued employment precarity. However there is a lack of quantitative tools able to assess the extent and impact of precarity overtime and at the individual level. Using the index of complexity as a starting point, we aim to create an index of precarity accounting for the benefit or loss entailed by each transition. Including the nature of each transition and the unpredictability of the whole employment trajectory in the index allows researchers to grasp both the complexity and the quality of young people’s employment trajectories. Our contribution shows how the proposed index provides a synthetic measure for comparing the degree of precarity. Results from a school-to-work transition dataset confirm the usefulness of the index as a predictor of future negative labour market trajectories.