Sociological Methodology最新文献

Individuals do not respond uniformly to treatments, such as events or interventions. Sociologists routinely partition samples into subgroups to explore how the effects of treatments vary by selected covariates, such as race and gender, on the basis of theoretical priors. Data-driven discoveries are also routine, yet the analyses by which sociologists typically go about them are often problematic and seldom move us beyond our biases to explore new meaningful subgroups. Emerging machine learning methods based on decision trees allow researchers to explore sources of variation that they may not have previously considered or envisaged. In this article, the authors use tree-based machine learning, that is, causal trees, to recursively partition the sample to uncover sources of effect heterogeneity. Assessing a central topic in social inequality, college effects on wages, the authors compare what is learned from covariate and propensity score-based partitioning approaches with recursive partitioning based on causal trees. Decision trees, although superseded by forests for estimation, can be used to uncover subpopulations responsive to treatments. Using observational data, the authors expand on the existing causal tree literature by applying leaf-specific effect estimation strategies to adjust for observed confounding, including inverse propensity weighting, nearest neighbor matching, and doubly robust causal forests. We also assess localized balance metrics and sensitivity analyses to address the possibility of differential imbalance and unobserved confounding. The authors encourage researchers to follow similar data exploration practices in their work on variation in sociological effects and offer a straightforward framework by which to do so.

In this article, the author proposes a methodology for the validation of sequence analysis typologies on the basis of parametric bootstraps following the framework proposed by Hennig and Lin (2015). The method works by comparing the cluster quality of an observed typology with the quality obtained by clustering similar but nonclustered data. The author proposes several models to test the different structuring aspects of the sequences important in life-course research, namely, sequencing, timing, and duration. This strategy allows identifying the key structural aspects captured by the observed typology. The usefulness of the proposed methodology is illustrated through an analysis of professional and coresidence trajectories in Switzerland. The proposed methodology is available in the WeightedCluster R library.

Most social mobility studies take a two-generation perspective, in which intergenerational relationships are represented by the association between parents' and offspring's socioeconomic status. This approach, albeit widely adopted in the literature, has serious limitations when more than two generations of families are considered. In particular, it ignores the role of families' demographic behaviors in moderating mobility outcomes and the joint role of mobility and demography in shaping long-run family and population processes. This paper provides a demographic approach to the study of multigenerational social mobility, incorporating demographic mechanisms of births, deaths, and mating into statistical models of social mobility. Compared to previous mobility models for estimating the probability of offspring's mobility conditional on parent's social class, the proposed joint demography-mobility model treats the number of offspring in various social classes as the outcome of interest. This new approach shows the extent to which demographic processes may amplify or dampen the effects of family socioeconomic positions due to the direction and strength of the interaction between mobility and differentials in demographic behaviors. I illustrate various demographic methods for studying multigenerational mobility with empirical examples using the IPUMS linked historical U.S. census representative samples (1850 to 1930), the Panel Study of Income Dynamics (1968 to 2015), and simulation data that show other possible scenarios resulting from demography-mobility interactions.