Mobility scholars are increasingly turning to computational methods to analyze mobility tables. Most of these approaches start with the detection of mobility clusters, namely, sets of occupations within which the flow of workers is dense and across which the flow is sparse. Yet clustering is not the only way worker flows can be structured. This article shows how a degree-corrected stochastic blockmodel can detect patterns of mobility that are more general than clustering and consistent with the homogeneity criterion laid out by Goodman as well as the internal homogeneity thesis proposed by Breiger. Because of the intractable marginal likelihood of the model, parameters are estimated using a variational expectation maximization algorithm. Simulation results suggest the estimation algorithm successfully recovers (conditionally) stochastically equivalent mobility classes. Analysis of two real-world examples shows the model is able to detect meaningful mobility patterns, even in situations in which commonly used community detection algorithms fail.

Measuring social categories and phenomena in survey questionnaires is complicated in a dynamic and diverse society. The use of “other, describe”–style open-ended text boxes can address this issue, but researchers rarely use these data because they lack guidance on how to code open-text information and manage small samples for analyses. The authors offer a roadmap to key decisions regarding the use of “other, describe” answer options and data during three stages of research: data collection and instrument development, data cleaning, and data analysis. The authors then use two cases, the Perceptions of Housework Pilot Survey and the Perceptions of Discrimination Study, to illustrate how decision making about open-text responses to demographic survey questions unfolded. These cases highlight the complexities and trade-offs in decision making and their implications for knowledge production. The authors recommend meta-practices for flexibility, transparency, and reflexivity, as there is not one “right choice” when it comes to using “other, describe” data.

The network scale-up method (NSUM) is a cost-effective approach to estimating the size or prevalence of a group of people that is hard to reach through a standard survey. The basic NSUM involves two steps: estimating respondents’ degrees and estimating the prevalence of the hard-to-reach population of interest using respondents’ estimated degrees and the number of people they report knowing in the hard-to-reach group. Each of these two steps involves taking either an average of ratios or a ratio of averages. Using the ratio of averages for each step has so far been the most common approach. However, the authors present theoretical arguments that using the average of ratios at the second, prevalence-estimation step often has lower mean squared error when the random mixing assumption is violated, which seems likely in practice; this estimator was proposed early in NSUM development but has largely been unexplored and unused. Simulation results using an example network data set also support these findings. On the basis of this theoretical and empirical evidence, the authors suggest that future surveys that use a simple estimator may want to use this mixed estimator, and estimation methods based on this estimator may produce new improvements.

The authors argue that hitherto separate methodological conversations about abduction and comparison can be fruitfully brought together to generate novel, well-founded insights and retheorize an object of study in multiple-case qualitative inquiry. The authors call this abductive cross-case comparison and illustrate it by way of a collective study of how professional boundary work is changing under transnational conditions. In this study, the authors faced a common challenge in qualitative-comparative research: what to do when initial observations generate “surprises” that seem to confound the theoretical frameworks undergirding the comparison? To discuss how abductive inferences supported the authors’ response to this challenge, they explicate the acts of discovery and (re)conceptualization involved through various steps in a team-based research process. Building on the existing qualitative comparison literature, the authors suggest that such procedures fill a methodological gap and may hold great promise for overcoming obstacles in designing and implementing comparative research. Overall, the authors explicate and illustrate the method of abductive cross-case comparison, including their work as a research team. The aim of this article is thus to help sociologists implement better qualitative research that leverages a fuller potential of comparative designs to push beyond established knowledge and frameworks.

Researchers can use data visualization techniques to explore, analyze, and present data in new ways. Although quantitative data are visualized most often, recent innovations have brought attention to the potential benefits of visualizing qualitative data. In this article, the authors demonstrate one way researchers can use networks to analyze and present ethnographic interview data. The authors suggest that because many respondents know one another in ethnographic research, networks are a useful tool for analyzing the implications of respondents’ familiarity with one another. Moreover, respondents often share familiar cultural references that can be visualized. The authors show how visualizing respondents’ ties in conjunction with their shared cultural references sheds light on the different systems of meaning that respondents within a field site use to make sense of the social phenomena under investigation.

Sequence analysis is increasingly used in the social sciences for the holistic analysis of life-course and other longitudinal data. The usual approach is to construct sequences, calculate dissimilarities, group similar sequences with cluster analysis, and use cluster membership as a dependent or independent variable in a regression model. This approach may be problematic, as cluster memberships are assumed to be fixed known characteristics of the subjects in subsequent analyses. Furthermore, it is often more reasonable to assume that individual sequences are mixtures of multiple ideal types rather than equal members of some group. Failing to account for uncertain and mixed memberships may lead to wrong conclusions about the nature of the studied relationships. In this article, the authors bring forward and discuss the problems of the “traditional” use of sequence analysis clusters as variables and compare four approaches for creating explanatory variables from sequence dissimilarities using different types of data. The authors conduct simulation and empirical studies, demonstrating the importance of considering how sequences and outcomes are related and the need to adjust analyses accordingly. In many typical social science applications, the traditional approach is prone to result in wrong conclusions, and similarity-based approaches such as representativeness should be preferred.

Large-scale sample surveys are not designed to produce reliable estimates for small areas. Here, small area estimation methods can be applied to estimate population parameters of target variables to detailed geographic scales. Small area estimation for noncontinuous variables is a topic of great interest in the social sciences where such variables can be found. Generalized linear mixed models are widely adopted in the literature. Interestingly, the small area estimation literature shows that multivariate small area estimators, where correlations among outcome variables are taken into account, produce more efficient estimates than do the traditional univariate techniques. In this article, the author evaluate a multivariate small area estimator on the basis of a joint mixed model in which a small area proportion and mean of a continuous variable are estimated simultaneously. Using this method, the author “borrows strength” across response variables. The author carried out a design-based simulation study to evaluate the approach where the indicators object of study are the income and a monetary poverty (binary) indicator. The author found that the multivariate approach produces more efficient small area estimates than does the univariate modeling approach. The method can be extended to a large variety of indicators on the basis of social surveys.

Previous research has shown that a prolonged recall period is associated with lower data quality in time-diary research. In these studies, the recall period is roughly estimated on the basis of the period between the assigned diary day and the agreed collection day. Because this is so rudimentary, little is known about the duration of the mean recall period and its consequences for data quality. Recent advances in online methodology now allow a better investigation of the recall period using time stamps. Using a refined indicator, the authors examine the duration of the recall period, to what extent this duration is related to socioeconomic characteristics, and how a prolonged recall period affects data quality. The authors demonstrate that using online time-diary data collected from 8,535 teachers in Belgium, the mean recall period is less than 24 hr for most respondents, although respondents with many time constraints have extended recall periods. Additionally, a prolonged recall period indeed has negative consequences for data quality. Quality deterioration already arises several hours after an activity has been completed, much sooner than previous research has indicated.

Multistate life table methods are an important tool for producing easily understood measures of population health. Most contemporary uses of these methods involve sample data, thus requiring techniques for capturing uncertainty in estimates. In recent decades, several methods have been developed to do so. Among these methods, the Bayesian approach proposed by Lynch and Brown has several unique advantages. However, the approach is limited to estimating years to be spent in only two living states, such as "healthy" and "unhealthy." In this article, the authors extend this method to allow for large state spaces with "quasi-absorbing" states. The authors illustrate the new method and show its advantages using data from the Health and Retirement Study to investigate U.S. regional differences in years of remaining life to be spent with diabetes, chronic conditions, and disabilities. The method works well and yields rich output for reporting and subsequent analyses. The expanded method also should facilitate the use of multi-state life tables to address a wider array of social science research questions.

Modeling asymmetric relationships is an emerging subject of interest among sociologists. York and Light advanced a method to estimate asymmetric models with panel data, which was further developed by Allison. However, little attention has been given to the large- N, large- T case, wherein autoregression, slope heterogeneity, and cross-sectional dependence are important issues to consider. The authors fill this gap by conducting Monte Carlo experiments comparing the bias and power of the fixed-effects estimator to a set of heterogeneous panel estimators. The authors find that dynamic misspecification can produce substantial biases in the coefficients. Furthermore, even when the dynamics are correctly specified, the fixed-effects estimator will produce inconsistent and unstable estimates of the long-run effects in the presence of slope heterogeneity. The authors demonstrate these findings by testing for directional asymmetry in the economic development–CO2 emissions relationship, a key question in macro sociology, using data for 66 countries from 1971 to 2015. The authors conclude with a set of methodological recommendations on modeling directional asymmetry.