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…

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…

Social mobility scholars have long been interested in estimating the effect of intergenerational mobility, typically measured by differences in the socioeconomic status between parents and offspring, on later-life outcomes of offspring. In a 2022 article "Heterogeneous Effects of Intergenerational Social Mobility: An Improved Method and New…

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…

Randomized response techniques (RRTs) are useful survey tools for estimating the prevalence of sensitive issues, such as the prevalence of doping in elite sports. One type of RRT, the unrelated question model (UQM), has become widely used because of its psychological acceptability for study participants and its favorable statistical properties.…

This article details the challenges we faced in collecting sensitive information in an ethnically and religiously divided community that has experienced recent violence. The discussion is based on a summer 2016 survey experiment we conducted in Jos, Nigeria, to gather information regarding residents' perceptions of local communal violence. We…

While conflict event data sets are increasingly used in contemporary conflict research, important concerns persist regarding the quality of the collected data. Such concerns are not necessarily new. Yet, because the methodological debate and evidence on potential errors remains scattered across different subdisciplines of social sciences, there is…

The historical turn in social science has prompted scholars to engage with the work of historians on a large scale. Here, social scientists face two standard problems of selection bias: confirmation bias and convenience sampling. So far, the record of dealing with these problems has been poor, and little has been done to specify how social…

It is widely believed that regression models for binary responses are problematic if we want to compare estimated coefficients from models for different groups or with different explanatory variables. This concern has two forms. The first arises if the binary model is treated as an estimate of a model for an unobserved continuous response and the…

This article is developed from the contents from one of the chapters the researcher has written as part of her PhD thesis. It discusses various methodological challenges the researcher had to face during the phase of data collection in the prisons of Kerala, India, and the strategies adopted to overcome these challenges. This article is intended…

This article addresses the problem of estimating the proportion Pi[subscript S] of the population belonging to a sensitive group using optional randomized response technique in stratified sampling based on Mangat model that has proportional and Neyman allocation and larger gain in efficiency. Numerically, it is found that the suggested model is…