We present a conceptual model to measure disparity-the target study-where social groups may be similarly situated (i.e., balanced) on allowable covariates. Our model, based on a sampling design, does not intervene to assign social group membership or alter allowable covariates. To address non-random sample selection, we extend our model to generalize or transport disparity or to assess disparity after an intervention on eligibility-related variables that eliminates forms of collider-stratification. To avoid bias from differential timing of enrollment, we aggregate time-specific study results by balancing calendar time of enrollment across social groups. To provide a framework for emulating our model, we discuss study designs, data structures, and G-computation and weighting estimators. We compare our sampling-based model to prominent decomposition-based models used in healthcare and algorithmic fairness. We provide R code for all estimators and apply our methods to measure health system disparities in hypertension control using electronic medical records.

Empirical studies on individual behaviour often, implicitly or explicitly, assume a single type of decision rule. Other studies do not specify behavioural assumptions at all. We advance sociological research by introducing (random) regret minimization, which is related to loss aversion, into the sociological literature and by testing it against (random) utility maximization, which is the most prominent decision rule in sociological research on individual behaviour. With an application to neighbourhood choice, in a sample of four European cities, we combine stated choice experiment data and discrete choice modelling techniques and find a considerable degree of decision rule-heterogeneity, with a strong prevalence of regret minimization and hence loss aversion. We also provide indicative evidence that decision rules can affect expected neighbourhood demand at the macro level. Our approach allows identifying heterogeneity in decision rules, that is, the degree of regret/loss aversion, at the level of choice attributes such as the share of foreigners when comparing neighbourhoods, and can improve sociological practice related to linking theories and social research on decision-making.

Measuring meaning is a central problem in cultural sociology and word embeddings may offer powerful new tools to do so. But like any tool, they build on and exert theoretical assumptions. In this paper I theorize the ways in which word embeddings model three core premises of a structural linguistic theory of meaning: that meaning is coherent, relational, and may be analyzed as a static system. In certain ways, word embeddings are vulnerable to the enduring critiques of these premises. In other ways, word embeddings offer novel solutions to these critiques. More broadly, formalizing the study of meaning with word embeddings offers theoretical opportunities to clarify core concepts and debates in cultural sociology, such as the coherence of meaning. Just as network analysis specified the once vague notion of social relations, formalizing meaning with embeddings can push us to specify and reimagine meaning itself.

Grouped and right-censored (GRC) counts have been used in a wide range of attitudinal and behavioural surveys yet they cannot be readily analyzed or assessed by conventional statistical models. This study develops a unified regression framework for the design and optimality of GRC counts in surveys. To process infinitely many grouping schemes for the optimum design, we propose a new two-stage algorithm, the Fisher Information Maximizer (FIM), which utilizes estimates from generalized linear models to find a global optimal grouping scheme among all possible -group schemes. After we define, decompose, and calculate different types of regressor-specific design errors, our analyses from both simulation and empirical examples suggest that: 1) the optimum design of GRC counts is able to reduce the grouping error to zero, 2) the performance of modified Poisson estimators using GRC counts can be comparable to that of Poisson regression, and 3) the optimum design is usually able to achieve the same estimation efficiency with a smaller sample size.

Discrimination is associated with numerous psychological health outcomes over the life course. The nine-item Everyday Discrimination Scale (EDS) is one of the most widely used measures of discrimination; however, this nine-item measure may not be feasible in large-scale population health surveys where a shortened discrimination measure would be advantageous. The current study examined the construct validity of a combined two-item discrimination measure adapted from the EDS by Add Health (N = 14,839) as compared to the full nine-item EDS and a two-item EDS scale (parallel to the adapted combined measure) used in the National Survey of American Life (NSAL; N = 1,111) and National Latino and Asian American Study (NLAAS) studies (N = 1,055). Results identified convergence among the EDS scales, with high item-total correlations, convergent validity, and criterion validity for psychological outcomes, thus providing evidence for the construct validity of the two-item combined scale. Taken together, the findings provide support for using this reduced scale in studies where the full EDS scale is not available.

Disparities across race, gender, and class are important targets of descriptive research. But rather than only describe disparities, research would ideally inform interventions to close those gaps. The gap-closing estimand quantifies how much a gap (e.g. incomes by race) would close if we intervened to equalize a treatment (e.g. access to college). Drawing on causal decomposition analyses, this type of research question yields several benefits. First, gap-closing estimands place categories like race in a causal framework without making them play the role of the treatment (which is philosophically fraught for non-manipulable variables). Second, gap-closing estimands empower researchers to study disparities using new statistical and machine learning estimators designed for causal effects. Third, gap-closing estimands can directly inform policy: if we sampled from the population and actually changed treatment assignments, how much could we close gaps in outcomes? I provide open-source software (the R package gapclosing) to support these methods.

Responsive survey design is a technique aimed at improving the efficiency or quality of surveys by using incoming data from the field to make design changes. The technique was pioneered on large national surveys, but the tools can also be applied on the smaller-scale surveys most commonly used by sociologists. We demonstrate responsive survey design in a small-scale, list-based sample survey of students on the topic of sexual misconduct. We investigate the impact of individual incentive levels and a two-phase responsive design with changes to mode of contact as approaches for limiting the potential of nonresponse bias in data from such surveys. Our analyses demonstrate that a two-phase design introducing telephone and face-to-face reminders to complete the survey can produce stronger change in response rates and characteristics of those who respond than higher incentive levels. These findings offer tools for sociologists designing smaller-scale surveys of special populations or sensitive topics.

Unit nonresponse in panel data sets is often a source of bias. Why certain individuals attrite from longitudinal studies and how to minimize this phenomenon have been examined by researchers. Howev...

When using multiple imputation, users often want to know how many imputations they need. An old answer is that 2–10 imputations usually suffice, but this recommendation only addresses the efficienc...

People have been interested in social cohesion and its implications in different areas of social life from the classic sociology authors until today. Besides its multidimensional nature, this compl...