To what degree does parent occupation cause a child’s occupational attainment? We articulate this causal question in the potential outcomes framework. Empirically, we show that adjustment for only two confounding variables substantially reduces the estimated association between parent and child occupation in a U.S. cohort. Methodologically, we highlight complications that arise when the treatment variable

In a probability sampling survey, adaptive data collection strategies may be used to obtain a response set that minimizes nonresponse bias within budget constraints. Previous research has stratified the target population into subgroups defined by categories of auxiliary variables observed for the entire population, and tailored strategies to obtain similar response rates across subgroups. However,

Large language models (LLMs), through their exposure to massive collections of online text, learn to reproduce the perspectives and linguistic styles of diverse social and cultural groups. This capability suggests a powerful social scientific application—the simulation of empirically realistic, culturally situated human subjects. Synthesizing recent research in artificial intelligence and computational

How can one understand the spread of ideas across text data? This is a key measurement problem in sociological inquiry, from the study of how interest groups shape media discourse, to the spread of policy across institutions, to the diffusion of organizational structures and institution themselves. To study how ideas and narratives diffuse across text, we must first develop a method to identify whether

Large language models (LLMs) are quickly becoming ubiquitous, but their implications for social science research are not yet well understood. We ask whether LLMs can help code and analyse large-N qualitative data from open-ended interviews, with an application to transcripts of interviews with Rohingya refugees and their Bengali hosts in Bangladesh. We find that using LLMs to annotate and code text

Although there is growing social science research examining how generative AI models can be effectively and systematically applied to text-based tasks, whether and how these models can be used to analyze images remain open questions. In this article, we introduce a framework for analyzing images with generative multimodal models, which consists of three core tasks: curation, discovery, and measurement

This article proposes a fully nonparametric model to investigate the dynamics of intergenerational income mobility for discrete outcomes. In our model, an individual’s income class probabilities depend on parental income in a manner that accommodates nonlinearities and interactions among various individual and parental characteristics, including race, education, and parental age at childbearing, and

Randomized audits and correspondence studies are widely regarded as a “gold standard” for capturing discrimination and bias. However, gatekeepers (e.g., employers) are the analytic unit even though stated implications often center on group-level inequalities. Employing simple rules, we show that audits have the potential to uncover applicant-side inequalities and burdens beyond the gatekeeper biases

Generative artificial intelligence (AI) has the potential to revolutionize social science research. However, researchers face the difficult challenge of choosing a specific AI model, often without social science-specific guidance. To demonstrate the importance of this choice, we present an evaluation of the effect of alignment, or human-driven modification, on the ability of large language models (LLMs)

Over the past decade, social scientists have adapted computational methods for qualitative text analysis, with the hope that they can match the accuracy and reliability of hand coding. The emergence of GPT and open-source generative large language models (LLMs) has transformed this process by shifting from programming to engaging with models using natural language, potentially mimicking the in-depth

The growing popularity of generative artificial intelligence (AI) tools presents new challenges for data quality in online surveys and experiments. This study examines participants’ use of large language models to answer open-ended survey questions and describes empirical tendencies in human versus large language model (LLM)-generated text responses. In an original survey of research participants recruited

Generative artificial intelligence (AI) offers new capabilities for analyzing data, creating synthetic media, and simulating realistic social interactions. This essay introduces a special issue that examines how these and other affordances of generative AI can advance social science research. We discuss three core themes that appear across the contributed articles: rigorous measurement and validation

We examine variations in pay gap estimates and inferences associated with distinct conceptualizations of jobs and employment contexts under legal and comparable worth theories of pay bias. We find that job titles produce smaller estimates of within job pay gaps than job groups, but the inferential importance of job concepts differs across organizational, workplace, and job groups within workplace units

Large language models (LLMs) have tremendous potential for social science research as they are trained on vast amounts of text and can generalize to many tasks. We explore the use of LLMs for supervised text classification, specifically the application to stance detection, which involves detecting attitudes and opinions in texts. We examine the performance of these models across different architectures

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 nonrandom sample selection, we extend our model to generalize or transport disparity or to assess disparity after

Social networks literature has explored homophily, the tendency to associate with similar others, as a critical boundary-making process contributing to segregated networks along the lines of identities. Yet, social network research generally conceptualizes identities as sociodemographic categories and seldom considers the inherently continuous and heterogeneous nature of differences. Drawing upon the

Large language models (LLMs) provide cost-effective but possibly inaccurate predictions of human behavior. Despite growing evidence that predicted and observed behavior are often not interchangeable , there is limited guidance on using LLMs to obtain valid estimates of causal effects and other parameters. We argue that LLM predictions should be treated as potentially informative observations, while

Generative artificial intelligence (AI) has shown incredible leaps in performance across data of a variety of modalities including texts, images, audio, and videos. This affords social scientists the ability to annotate variables of interest from unstructured media. While rapidly improving, these methods are far from perfect and, as we show, even ignoring the small amounts of error in high accuracy

Generative artificial intelligence (AI) is increasingly presented as a potential substitute for humans, including as research subjects. However, there is no scientific consensus on how closely these in silico clones can emulate survey respondents. While some defend the use of these “synthetic users,” others point toward social biases in the responses provided by large language models (LLMs). In this

The study of mobility effects is an important subject of study in sociology. Empirical investigations of individual mobility effects, however, have been hindered by one fundamental limitation, the unidentifiability of mobility effects when origin and destination are held constant. Given this fundamental limitation, we propose to reconceptualize mobility effects from the micro- to macro-level. Instead

Modern computational text classification methods have brought social scientists tantalizingly close to the goal of unlocking vast insights buried in text data—from centuries of historical documents to streams of social media posts. Yet three barriers still stand in the way: the tedious labor of manual text annotation, the technical complexity that keeps these tools out of reach for many researchers

Although the literature on cultural holes has expanded considerably in recent years, there is no concrete measure in that literature to locate cultural holes brokers. This article develops a conceptual framework grounded in social network theory and cultural sociology to propose a specific solution to fill this measurement gap. Agent-based computational experiments are leveraged to develop a theoretical

Social science theories often postulate systems of causal relationships among variables, which are commonly represented using directed acyclic graphs (DAGs). As non-parametric causal models, DAGs require no assumptions about the functional form of the hypothesized relationships. Nevertheless, to simplify empirical evaluation, researchers typically invoke such assumptions anyway, even though they are

According to the interventionist framework of actual causality, causal claims in history are ultimately claims about special types of functional dependencies between variables, which consist not only of actual events but also of corresponding counterfactual states of affairs. Instead of advocating the methodological use of counterfactuals tout court, we propose specific circumstances in historical

In this article, we develop a mixed-methods design that combines Bayesian regression with Bayesian process tracing. A fully Bayesian multimethod design allows one to include empirical knowledge at each stage of the analysis and to coherently transfer information from the quantitative to the qualitative analysis, and vice versa. We present a complete mixed-methods workflow explaining how this is accomplished

The study addresses the effects of piloting methods on the cross-cultural comparability and reliability of the measurement of gender and age stereotypes. We conducted a summative evaluation of expert reviews, cognitive pretests and web probing. We first piloted a gender role, an ageism, and a children stereotypes instrument in German and American English. We then randomly assigned the original and

Occupation is a construct prone to classification mismatches by coders and description inconsistency by respondents. We explore whether mismatches in occupational coding have recently increased, what factors are associated with the rise in mismatches, and how the rise affects estimates of intragenerational occupational mobility. Utilizing the 1991–2020 Annual Social and Economic Supplement of the Current

Organizational researchers use a variety of methods to obtain sampling frames. The utility of these methods, however, is constrained by access restrictions, limited coverage, prohibitive costs, and cumbersome formats. This article presents a new method for generating organizational sampling frames that is cost-effective, uses publicly available data, and can produce sampling frames for many geographic

Self-administered surveys may be administered with a single mode or mixed data collection modes. How mixing modes of data collection affects survey costs is not well understood. We examine whether cost structures differ for mail-only versus web+mail mixed-mode surveys, what design features are associated with costs, and whether survey costs are associated with response rates. Using administrative survey

Data sharing and transparency are becoming more common across the social sciences. In this article, we provide an overview of ethical, methodological, and technological considerations and challenges when developing large video-based datasets intended to be shared across researchers. We cover data security, storage, and access as well as data documentation, tagging, and transcription. Our discussions

Many studies have compared individual measures of health expectancy across older populations by time-invariant characteristics. However, very few have included time-varying variables when calculating health expectancy. Even among older adults, socioeconomic and demographic characteristics are likely to change over the life course, and these changes may have substantial implications for health outcomes

Sociologists are discussing the need for more formal ways to extract meaning from digital text archives. We focus attention on the seeded topic model, a semi-supervised extension to the standard topic model that allows sociological knowledge to be infused into the computational learning of meaning structures. Seed words help crystallize topics around known concepts, while utilizing topic models’ functionality

This primer systematizes the emerging literature on causal inference using deep neural networks under the potential outcomes framework. It provides an intuitive introduction to building and optimizing custom deep learning models and shows how to adapt them to estimate/predict heterogeneous treatment effects. It also discusses ongoing work to extend causal inference to settings where confounding is

Researchers have developed many uses for digital trace data, yet most online survey experiments continue to rely on attitudinal rather than behavioral measures. We argue that researchers can collect digital trace data during online survey experiments with relative ease, at modest costs, and to substantial benefit. Because digital trace data unobtrusively measure survey participants’ behaviors, they

Instrumental variables (IV) analysis is a powerful, but fragile, tool for drawing causal inferences from observational data. Sociologists increasingly turn to this strategy in settings where unmeasured confounding between the treatment and outcome is likely. This paper reviews the assumptions required for IV and the consequences of violating them, focusing on sociological applications. We highlight

This article uses the example of voter turnout in US presidential elections to compare two new methods for age, period, and cohort (APC) analysis: the APC interaction model and the APC bounding analysis. While discussing the formal, conceptual, and interpretive differences between the two methods, the analysis demonstrates how both methods can be used to generate distinct but complementary findings

Causal decomposition analysis aims to identify risk factors (referred to as “mediators”) that contribute to social disparities in an outcome. Despite promising developments in causal decomposition analysis, current methods are limited to addressing a time-fixed mediator and outcome only, which has restricted our understanding of the causal mechanisms underlying social disparities. In particular, existing

Trust is a foundational concept of contemporary sociological theory. Still, empirical research on trust relies on a relatively small set of measures. These are increasingly debated, potentially undermining large swathes of empirical evidence. Drawing on a combination of open-ended probing data, supervised machine learning, and a U.S. representative quota sample, our study compares the validity of standard

In this paper, we investigate the conditions under which data imbalances, a common data characteristic that occurs when factor values are unevenly distributed, are problematic for the performance of Coincidence Analysis (CNA). We further examine how such imbalances relate to fragmentation and noise in data. We show that even extreme data imbalances, when not combined with fragmentation or noise, do

Distances derived from word embeddings can measure a range of gradational relations—similarity, hierarchy, entailment, and stereotype—and can be used at the document- and author-level in ways that overcome some of the limitations of weighted dictionary methods. We provide a comprehensive introduction to using word embeddings for relation induction, and demonstrate how such techniques can complement

The promise of process tracing methods is that they can help us better understand how things work in real-world cases. Despite the many advances in the past two decades, we contend that existing accounts result in either under-socialised accounts in which the moves made by actors are studied while neglecting the social dimension of action, or over-socialised accounts that are so focused on social context

Processes that unfold over individuals’ life courses are often associated with inequalities later in life. The literature lacks methodological approaches to analyze inequalities in outcomes between groups, for example, between women and men, in a life-course-sensitive manner. We propose a combination of methods—of sequence analysis, which enables us to study the multidimensional complexity of life

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

Visual data have dramatically increased in quantity in the digital age, presenting new opportunities for social science research. However, the extensive time and labor costs to process and analyze ...

Background:On August 31, 2017, Ohio passed legislation that regulates how opioids can be prescribed postoperatively. Studies have shown that such legislation is successful in reducing the morphine ...

Dynamic cerebral autoregulation (dCA) is a key mechanism that regulates cerebral blood flow (CBF) in response to transient changes in blood pressure (BP). Impairment of dCA could increase vulnerabi...

We studied 30 healthy volunteers (60 arms), categorized into three age groups with equal numbers to verify if a 22 MHz compared with a 15 MHz ultrasound transducer has additional value for studying...

Asit Biswas and Shubh Brat Sarkar (Eds.), Dalit Poems, Songs and Dialogues from Bengal in English Translation (Kolkata: Ababil Books, 2019), 266 pp., ₹495, ISBN: 978-8-1939-3923-9 (Paperback).

Juned Shaikh, Outcaste Bombay: City Making and the Politics of the Poor. Orient BlackSwan Private Ltd, 2021, 227 pp., ₹995.

In the wake of the methodological developments that aim to render qualitative comparative analysis (QCA) “time sensitive”, I propose a new procedure for carrying out QCA longitudinally. More specif...

The rise of social media has opened countless opportunities to explore social science questions with new data and methods. However, research on socioeconomic inequality remains constrained by limit...

Multilevel models are often used to account for the hierarchical structure of social data and the inherent dependencies to produce estimates of regression coefficients, variance components associat...

In the United States and elsewhere, risk assessment algorithms are being used to help inform criminal justice decision-makers. A common intent is to forecast an offender’s “future dangerousness.” S...

The use of inter-rater reliability (IRR) methods may provide an opportunity to improve the transparency and consistency of qualitative case study data analysis in terms of the rigor of how codes an...

Video data analysis (VDA) represents an important methodological framework for contemporary research approaches to the myriad of footage available from cameras, devices, and phones. Footage from po...

Video data offer important insights into social processes because they enable direct observation of real-life social interaction. Though such data have become abundant and increasingly accessible, ...

Policy evaluation studies, which assess the effect of an intervention, face statistical challenges: in real-world settings treatments are not randomly assigned and the analysis might be complicated...

Obtaining reliable and timely estimates of migration flows is critical for advancing the migration theory and guiding policy decisions, but it remains a challenge. Digital data provide granular inf...

Discrete-time survival analysis (DTSA) models are a popular way of modeling events in the social sciences. However, the analysis of discrete-time survival data is challenged by missing data in one ...