Sociological Methodology

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, although 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 article 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 with 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 because of the direction and strength of the interaction between mobility and differentials in demographic behaviors. The author illustrates various demographic methods for studying multigenerational mobility with empirical examples using the IPUMS linked historical U.S. census representative samples (1850–1930), the Panel Study of Income Dynamics (1968–2015), and simulation data that show other possible scenarios resulting from demography-mobility interactions.

How can we statistically assess differences in groups of life-course trajectories? The authors address a long-standing inadequacy of social sequence analysis by proposing an adaption of the Bayesian information criterion (BIC) and the likelihood-ratio test (LRT) for assessing differences in groups of sequence data. Unlike previous methods, this adaption provides a useful measure for degrees of difference, that is, the substantive significance, and the statistical significance of differences between predefined groups of life-course trajectories. The authors present a simulation study and an empirical application on whether employment life-courses converged after reunification in the former East Germany and West Germany, using data for six birth-cohort groups ages 15 to 40 years from the German National Education Panel Study. The new methods allow the authors to show that convergence of employment life-courses around reunification was stronger for men than for women and that it was most pronounced in terms of the duration of employment states but weaker for their order and timing in the life-course. Convergence of East German and West German women’s employment lives set in earlier and reflects a secular trend toward a more gender-egalitarian division of labor in West Germany that is unrelated to reunification. The simulation study and the substantive application demonstrate the usefulness of the proposed BIC and LRT methods for assessing group differences in sequence data.

In this article, a modeling strategy is proposed that accounts for heterogeneity in nominal responses that is typically ignored when using common multinomial logit models. Heterogeneity can arise from unobserved variance heterogeneity, but it may also represent uncertainty in choosing from alternatives or, more generally, result from varying coefficients determined by effect modifiers. It is demonstrated that the bias in parameter estimation in multinomial logit models can be substantial if heterogeneity is present but ignored. The modeling strategy avoids biased estimates and allows researchers to investigate which variables determine uncertainty in choice behavior. Several applications demonstrate the usefulness of the model.

Like other quantitative social scientists, network researchers benefit from pooling information from multiple observed variables to infer underlying (latent) attributes or social processes. Appropriate network data for this task is increasingly available. The inherent dependencies in relational data, however, pose unique challenges. This is especially true for the ascendant tasks of cross-network comparisons and multilevel network analysis. The author draws on item response theory and multilevel (mixed effects) modeling to propose a methodological approach that accounts for these dependencies and allows the analyst to model variation of latent dyadic traits across relations, actors, and groups precisely and parsimoniously. Examples demonstrate the approach’s utility for three important research areas: tie strength in adolescent friendships, group differences in how discussing personal problems relates to tie strength, and the analysis of multiple relations.

Many refinements of statistical design have been offered to solve the replication problem identified by the Open Science Collaboration and Camerer and colleagues. There are, however, two distinct kinds of experimentation: Fisher design and theory designed. Therefore, there are two kinds of replication. Only for the Fisher design does replication reproduce conditions of prior experiments in order to compare new with prior results, and only there has a replication problem been demonstrated. In contrast, replications for theory-designed experiments test experimental results against theoretical predictions, and only for theory-designed experiments can replication be extended broadly across the scope of a theory. The authors analyze the logic of the two types of experiments as well as hybrids that mix qualities of both.

This article presents an innovative approach to improve the power of questionnaires by combining them with weekly diaries. The aim is to show how one can calibrate information collected from questionnaires, which provide a distribution that is in general biased, with diary data, which are more accurate but cannot provide a distribution across a range of frequencies. These problems become even more pronounced when the object of analysis is a specific issue, such as religious practice, the focus of this study. The suggested user-friendly model uses the more accurate diary data to adjust the distribution produced by the standard questions and enables researchers to obviate the problems of the two data collection methods. To present a practical application, the Time Budget Survey, conducted at five-year intervals between 1975 and 2005 in the Netherlands, is used.