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

Social scientists often present modeling results from categorical explanatory variables, such as gender, race, and marital status, as coefficients representing contrasts to a “reference” group. Although choosing the reference category may seem arbitrary, the authors argue that it is an intrinsically meaningful act that affects the interpretability of results. Reference category selection foregrounds

The author introduces methods for the decomposition analysis of multigroup segregation measured by the index of dissimilarity, the squared coefficient of variation, and Theil’s entropy measure. Using a new causal framework, the author takes a unified approach to the decomposition analysis by specifying conditions that must be satisfied to decompose segregation into unexplained and explained components

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

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

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

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

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

Studies of economic mobility summarize the distribution of offspring incomes for each level of parent income. Mitnik and Grusky (2020) highlight that the conventional intergenerational elasticity (IGE) targets the geometric mean and propose a parametric strategy for estimating the arithmetic mean. We decompose the IGE and their proposal into two choices: (1) the summary statistic for the conditional

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

Network concepts are often used to characterize the features of a social context. For example, past work has asked if individuals in more socially cohesive neighborhoods have better mental health outcomes. Despite the ubiquity of use, it is relatively rare for contextual studies to use the methods of network analysis. This is the case, in part, because network data are difficult to collect, requiring

The network autocorrelation model has been the workhorse for estimating and testing the strength of theories of social influence in a network. In many network studies, different types of social influence are present simultaneously and can be modeled using various connectivity matrices. Often, researchers have expectations about the order of strength of these different influence mechanisms. However

Retrospective life history designs are among the few practical approaches for collecting longitudinal network information from large populations, particularly in the context of relationships like sexual partnerships that cannot be measured via digital traces or documentary evidence. While all such designs afford the ability to “peer into the past” vis-à-vis the point of data collection, little is known

Random effects (RE) models have been widely used to study the contextual effects of structures such as neighborhoods or schools. The RE approach has recently been applied to age-period-cohort (APC) models that are unidentified because the predictors are exactly linearly dependent. However, research has not fully explained how the RE specification identifies these otherwise unidentified APC models.

The fact that the intergenerational income elasticity (IGE)—the workhorse measure of economic mobility—is defined in terms of the geometric mean of children’s income generates serious methodological problems. This has led to a call to replace it with the IGE of the expectation, which requires developing the methodological knowledge necessary to estimate the latter with short-run measures of income

The intergenerational elasticity (IGE) has been assumed to refer to the expectation of children’s income when in fact it pertains to the geometric mean of children’s income. We show that mobility analyses based on the conventional IGE have been widely misinterpreted, are subject to selection bias, and cannot disentangle the different channels for transmitting economic status across generations. The

This study provides a new test of time-use diary methodology, comparing diaries with a pair of objective criterion measures: wearable cameras and accelerometers. A volunteer sample of respondents (n = 148) completed conventional self-report paper time-use diaries using the standard UK Harmonised European Time Use Study (HETUS) instrument. On the diary day, respondents wore a camera that continuously

Sociological Methodology 2019 (volume 49) is dedicated to the eminent sociologist Stanley Lieberson, who passed away in 2018. For more than 50 years, Stan’s research and writing advanced the discipline’s substantive understanding of race and ethnic relations, culture (both language diversity and processes of cultural change), urban sociology, and social organization. Shaped by his demographic training

This is the fourth (and last) volume of Sociological Methodology (SM) at Pennsylvania State University under the editorship of Duane F. Alwin and the second for Ashton M. Verdery as Deputy Editor.1 Our editorial team, along with an additional cast of characters to be implicated in the following, has worked hard to maintain the high standards held by SM. As a testament to these claims, we should first

Zhang, Han, and Jennifer Pan. 2019. “CASM: A Deep-Learning Approach for Identifying Collective Action Events with Text and Image Data from Social Media.” Sociological Methodology, first published on July 19, 2019. (Original DOI: 10.1177/0081175019860244)

An essential feature common to all empirical social research is variability across units of analysis. Individuals differ not only in background characteristics but also in how they respond to a particular treatment, intervention, or stimulation. Moreover, individuals may self-select into treatment on the basis of anticipated treatment effects. To study heterogeneous treatment effects in the presence

A Look at How the New York Times, the Wall Street Journal, the Washington Post, and BuzzFeed Compare.” The Atlantic. Retrieved June 17, 2019. https:// www.theatlantic.com/technology/archive/2016/05/how-many-stories-do-newspaperspublish-per-day/483845/. Soule, Sarah, and Jennifer Earl. 2005. “A Movement Society Evaluated: Collective Protest in the United States, 1960–1986.” Mobilization: An International

Protest Event and Political Discourse Approaches.” Mobilization 4(2):203–21. Kriesi, Hanspeter, Swen Hutter, and Abel Bojar. 2019. “Contentious Episode Analysis.” Mobilization 24(3). Kriesi, Hanspeter, Ruud Koopmans, Jan Willem Duyvendak, and Marco Giugni. 1995. New Social Movements in Western Europe. Minneapolis: University of Minnesota Press. McAdam, Doug. 1982. Political Process and the Development

We thank the editorial board for the opportunity to discuss our methodological contribution in a symposium dialogue as well as the two commentators for their inspiring and challenging comments. We are especially delighted that the commentators agree on the relevance of analyzing the dynamics of manifest and latent meanings in big data using different textmining tools in general and for map analysis

Goldenstein and Poschmann (hereafter GP, this volume, pp. 83–131) offer a novel approach, or combination of approaches, for tracking multiple layers of “meaning” in a collection of texts over time. The core theoretical argument is sound: Any given text conveys multiple layers of meaning simultaneously, the relationships across those layers may evolve dynamically over time in any given corpus, and different

In multilevel data, cross-classified data structures are common. For example, this occurs when individuals move to different regions in longitudinal data or students go to different secondary schools than their primary school peers. In both cases, the data structure is no longer fully nested. Estimating cross-classified multilevel models is computationally intensive, so researchers have used several

The case-time-control design is a tool to control for measured, time-varying covariates that increase montonically in time within each subject while also controlling for all unmeasured covariates that are constant within each subject across time. Until recently, the design was restricted to data with only two timepoints and a single binary covariate, or data with a binary exposure. Sjölander (2017)

Many research questions involve comparing predictions or effects across multiple models. For example, it may be of interest whether an independent variable’s effect changes after adding variables to a model. Or, it could be important to compare a variable’s effect on different outcomes or across different types of models. When doing this, marginal effects are a useful method for quantifying effects

Social scientists have recently started discussing the utilization of text-mining tools as being fruitful for scaling inductively grounded close reading. We aim to progress in this direction and provide a contemporary contribution to the literature. By focusing on map analysis, we demonstrate the potential of text-mining tools for text analysis that approaches inductive but still formal in-depth analysis

The dynamics of individual behavior are related to the dynamics of the social structures in which individuals are embedded. This implies that in order to study social mechanisms such as social selection or peer influence, we need to model the evolution of social networks and the attributes of network actors as interdependent processes. The stochastic actor-oriented model is a statistical approach to

Social networks represent two different facets of social life: (1) stable paths for diffusion, or the spread of something through a connected population, and (2) random draws from an underlying social space, which indicate the relative positions of the people in the network to one another. The dual nature of networks creates a challenge: if the observed network ties are a single random draw, is it

Multilevel models with persons nested in countries are increasingly popular in cross-country research. Recently, social scientists have started to analyze data with a three-level structure: persons at level 1, nested in year-specific country samples at level 2, nested in countries at level 3. By using a country fixed-effects estimator, or an alternative equivalent specification in a random-effects

This comment reconsiders advice offered by Bruch and Mare regarding sampling choice sets in conditional logistic regression models of residential mobility. Contradicting Bruch and Mare’s advice, past econometric research shows that no statistical correction is needed when using simple random sampling of unchosen alternatives to pare down respondents’ choice sets. Using data on stated residential preferences

Draper, David. 1995. “Assessment and Propagation of Model Uncertainty.” Journal of the Royal Statistical Society, Series B 57:45–97. Freedman, David A. 1983. “A Note on Screening Regression Equations.” American Statistician 37:152–55. Leamer, Edward E. 1983. “Let’s Take the Con out of Econometrics.” American Economic Review 73:31–43. Raftery, Adrian E. 1996. “Approximate Bayes Factors and Accounting

Robert M. O’Brien is a professor emeritus at the University of Oregon. He specializes in criminology and quantitative methods. Within criminology, he focuses on the methods used to gather criminological data, on the analysis of crime rates, and on the task of extricating the effects of ages, periods, and cohorts on crime rates. His most recent publication on that topic, “Homicide Arrest Rate Trends

1. The Bayesian information criterion (BIC) has been proposed as a way to carry out Bayesian hypothesis testing when there are no clear expectations. However, the BIC rests on a particular prior distribution, for which there is rarely any justification. See Raftery (1995) on the case for the BIC and Weakliem (1999) for a critique. 2. The assumption that the sample is of the same size is important.

In recent years, there has been increasing attention focused on the spatial dimensions of residential segregation—from the spatial arrangement of segregated neighborhoods to the geographic scale or relative size of segregated areas. However, the methods used to measure segregation do not incorporate features of the built environment, such as the road connectivity between locations or the physical barriers

Autoregressive latent trajectory (ALT) models combine features of latent growth curve models and autoregressive models into a single modeling framework. The development of ALT models has focused primarily on models with linear growth components, but some social processes follow nonlinear trajectories. Although it is straightforward to extend ALT models to allow for some forms of nonlinear trajectories

Treatment interference (i.e., one unit’s potential outcomes depend on other units’ treatment) is prevalent in social settings. Ignoring treatment interference can lead to biased estimates of treatment effects and incorrect statistical inferences. Some recent studies have started to incorporate treatment interference into causal inference. But treatment interference is often assumed to follow a simple

Randomized experiments on a network often involve interference between connected units, namely, a situation in which an individual’s treatment can affect the response of another individual. Current approaches to deal with interference, in theory and in practice, often make restrictive assumptions on its structure—for instance, assuming that interference is local—even when using otherwise nonparametric

False positive findings are a growing problem in many research literatures. We argue that excessive false positives often stem from model uncertainty. There are many plausible ways of specifying a regression model, but researchers typically report only a few preferred estimates. This raises the concern that such research reveals only a small fraction of the possible results and may easily lead to nonrobust

Conventional model selection evaluates models on their ability to represent data accurately, ignoring their dependence on theoretical and methodological assumptions. Drawing on the concept of underdetermination from the philosophy of science, the author argues that uncritical use of methodological assumptions can pose a problem for effective inference. By ignoring the plausibility of assumptions, existing

Researchers studying income inequality, economic segregation, and other subjects must often rely on grouped data—that is, data in which thousands or millions of observations have been reduced to counts of units by specified income brackets. The distribution of households within the brackets is unknown, and highest incomes are often included in an open-ended top bracket, such as “$200,000 and above

In recent studies, latent class tree (LCT) modeling has been proposed as a convenient alternative to standard latent class (LC) analysis. Instead of using an estimation method in which all classes are formed simultaneously given the specified number of classes, in LCT analysis a hierarchical structure of mutually linked classes is obtained by sequentially splitting classes into two subclasses. The

Survey researchers often ask a series of attitudinal questions with a common question stem and response options, known as battery questions. Interviewers have substantial latitude in deciding how to administer these items, including whether to reread the common question stem on items after the first one or to probe respondents’ answers. Despite the ubiquity of use of these items, there is virtually

The relationship between processes and time-varying covariates is of central theoretical interest in addressing many social science research questions. On the one hand, event history analysis (EHA) has been the chosen method to study these kinds of relationships when the outcomes can be meaningfully specified as simple instantaneous events or transitions. On the other hand, sequence analysis (SA) has

Likelihood Estimation for Social Network Dynamics.” Annals of Applied Statistics 4(2):567–88. Snijders, Tom A. B., and Mark Pickup. 2016. “Stochastic Actor-oriented Models for Network Dynamics.” Pp. 221–47 in Oxford Handbook of Political Networks, edited by Jennifer N. Victor, Mark Lubell, and Alexander H. Montgomery. Oxford, UK: Oxford University Press. Stadtfeld, Christoph, and Andreas Geyer-Schulz

The history of content in social science is the history of fad, fashion, and momentary preoccupations, but the history of research methods is one of cumulative developments which have enabled us to ask increasingly precise and sophisticated questions about human behavior. In this sense, I believe progress in social science is mostly in the ability to ask questions, not in the ability to foresee the

In Trond Petersen’s “Multiplicative Models for Continuous Dependent Variables: Estimation on Unlogged versus Logged Form” (this volume, pp. 113–164), the following phenomenon is noted: With the arithmetic and geometric means of a nonnegative quantitative variable measured for two groups (say, groups 1 and 2), it is possible for the arithmetic mean ( A 1 ) for group 1 to be larger than the arithmetic

Dynamic Event Processes in Social Networks. Karlsruhe, Germany: KIT Scientific. Tranmer, M., C. S. Marcum, F. B. Morton, D. P. Croft, and S. R. de Kort. 2015. “Using the Relational Event Model (REM) to Investigate the Temporal Dynamics of Animal Social Networks.” Animal Behaviour 101:99–105. Vu, D., A. Lomi, D. Mascia, and F. Pallotti. 2017. “Relational Event Models for Longitudinal Network Data with

In regression analysis with a continuous and positive dependent variable, a multiplicative relationship between the unlogged dependent variable and the independent variables is often specified. It can then be estimated on its unlogged or logged form. The two procedures may yield major differences in estimates, even opposite signs. The reason is that estimation on the unlogged form yields coefficients

The authors investigate the accuracy of young women’s retrospective reporting on their first substantial employment in three major, nationally representative U.S. surveys, examining hypotheses that longer recall duration, employment histories with lower salience and higher complexity, and an absence of “anchoring” biographical details will adversely affect reporting accuracy. The authors compare retrospective

The authors propose using categorical analysis-of-distance biplots to visualize the posterior classifications arising from a latent class (LC) model. Using this multivariate plot, it is possible to visualize in two (or three) dimensions the profile of multiple LCs, specifically both the within- and between-class variation, and the overlap or separation of the classes together with the class weights

Rank-order relational data, in which each actor ranks other actors according to some criterion, often arise from sociometric measurements of judgment or preference. The authors propose a general framework for representing such data, define a class of exponential-family models for rank-order relational structure, and derive sufficient statistics for interdependent ordinal judgments that do not require

Respondent-driven sampling (RDS) is a popular method for sampling hard-to-survey populations that leverages social network connections through peer recruitment. Although RDS is most frequently applied to estimate the prevalence of infections and risk behaviors of interest to public health, such as HIV/AIDS or condom use, it is rarely used to draw inferences about the structural properties of social

Important questions in the social sciences are concerned with the circumstances under which individuals, organizations, or states mutually agree to form social network ties. Examples of these coordination ties are found in such diverse domains as scientific collaboration, international treaties, and romantic relationships and marriage. This article introduces dynamic network actor models (DyNAM) for

Individuals differ in how they respond to a particular treatment or exposure, and social scientists are often interested in understanding how treatment effects are moderated by observed characteristics of individuals. Effect moderation occurs when individual covariates dampen or amplify the effect of some exposure. This article focuses on estimating moderated causal effects in longitudinal settings

Although substantive studies on segregation, such as residential or school segregation by race and occupational segregation by gender, are many in sociology, the analytical methodology is almost exclusively focused on measurement issues. The author introduces a set of two statistical models for the decomposition analysis of segregation. These models can be regarded as a tool to analyze whether one

A popular way to reduce confounding in observational studies is to use each study participant as his or her own control. This is possible when both the exposure and the outcome are time varying and have been measured at several time points for each individual. The case-time-control method is a special case, which, under certain assumptions, allows the analyst to control for confounding by time-varying