JCS Focus |《社会学方法论》最新目录及摘要

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社会学国际顶刊

Sociological Methodology

（《社会学方法论》）

最新目录及摘要

The qualitative interview has been a core technique in the sociological methods toolkit for generations. Interviews provide essential insights into how participants experience the world around them. New opportunities have emerged to adapt traditional in-depth interview techniques through the use of evolving technologies available to interview participants. This article describes the integration of ecological momentary assessment techniques to augment qualitative in-depth interviews focused on specific events, which we term event-centered interviewing. By incorporating photo data captured systematically through smartphone apps designed for ecological momentary assessment, event-centered interviews can extend the strengths of traditional qualitative interviews. We describe the processes and procedures for conducting event-centered interviews, and we highlight how the approach may create opportunities for qualitative analysis and minimize certain limitations of traditional in-depth interviews. We also highlight the positive participant responses to the approach from a pilot study. Although traditional in-depth interviews may remain at the core of qualitative sociological inquiry, event-centered interviewing may be especially useful for interviews about behavior and experiences that occur during specific events.

The authors introduce BERTNN (Bidirectional Encoder Representations from Transformers Neural Network), a novel methodology designed to expand affective lexicons, a critical component in sociological research. BERTNN estimates the affective meanings and their distribution for new concepts, bypassing the need for extensive surveys by leveraging their contextual usage in language. The cornerstone of BERTNN is the use of nuanced word embeddings from Bidirectional Encoder Representations from Transformers. BERTNN uniquely encodes words within the framework of synthesized social event sentences, preserving their meaning across actor-behavior-object positions. The model is fine-tuned on the basis of the implied sentiment changes, providing a more refined estimation of affective meanings. BERTNN outperforms previous approaches, setting a new standard in deriving multidimensional affective meanings for novel concepts. It efficiently replicates sentiment ratings that traditionally require extensive survey hours, demonstrating the power of automated modeling in sociological research. The expanded affective lexicons that can be produced with BERTNN cater to shifting cultural meanings and diverse subgroups, demonstrating the potential of computational linguistics to enrich the measurement tools in sociological research. This article underscores the novelty and significance of BERTNN in the broader context of sociological methodology.

Validation is at the heart of methodological discussions about topic modeling. The authors argue that validation based on human reading hinges on distinctive words and readers’ labeling of a topic, and it overlooks the probability of conflicting results from semantically similar models, such as regressions or other methods. This runs counter to the presumption that topic modeling can reveal features of documents that have some measurable association with social aspects outside the text. The authors develop a similar topic identifying procedure to verify that semantically similar solutions yield similar results in further analysis. The authors argue that future validations of topic modeling must consider such procedures.

In addition to overall dispersion, the distributional shape of economic status has attracted growing attention in the inequality literature. Economic polarization is a specific form of distributional change, characterized by a shrinking middle of the distribution and a growing top and bottom, with potentially important and unique social consequences. Building on relative distribution methods and drawing from the literature on job polarization, the authors develop an approach for analyzing economic polarization at the individual level. The method has three useful features. First, it offers intuitive and flexible measurement of economic polarization both between and within categories. Second, it helps disentangle two potential sources of economic polarization: compositional change, which involves changes to the allocation of workers across categories, and relative economic status change, which involves changes to the allocation of economic rewards between individuals. Third, it enables researchers to uncover and examine potential heterogeneity in economic polarization, for example, across occupations, geographic units, demographic and educational groups, and firms. The authors demonstrate the utility of this approach through two empirical applications: (1) an analysis of trends in wage polarization between and within occupations and (2) an examination of geographic variation in income polarization.

Previous studies have shown many instances where nonprobability surveys were not as accurate as probability surveys. However, because of their cost advantages, nonprobability surveys are widely used, and there is much debate over the appropriate settings for their use. To contribute to this debate, we evaluate the accuracy of nonprobability surveys by investigating the common claim that estimates of relationships are more robust to sample bias than means or proportions. We compare demographic, attitudinal, and behavioral variables across eight German probability and nonprobability surveys with demographic and political benchmarks from the microcensus and a high-quality, face-to-face survey. In the analyses, we compare three types of statistical inference: univariate estimates, bivariate Pearson’s r coefficients, and 24 different multiple regression models. The results indicate that in univariate comparisons, nonprobability surveys were clearly less accurate than probability surveys when compared with the population benchmarks. These differences in accuracy were smaller in the bivariate and the multivariate comparisons across surveys. In addition, the outcome of those comparisons largely depended on the variables included in the estimation. The observed sample differences are remarkable when considering that three nonprobability surveys were drawn from the same online panel. Adjusting the nonprobability surveys somewhat improved their accuracy.

Sociological approaches to digital and community-engaged research experienced significant innovation in recent years. This article examines developing and implementing a primarily virtual community-driven research (CDR) project with the National Survivors Union, the American national drug-users union, during the COVID-19 pandemic. Relationships between researchers and directly impacted people, such as people who use drugs, face many barriers. These issues were exacerbated during COVID-19 when in-person research decreased while drug-related harms increased. In response, this project modified the CDR model for drug-use research. The CDR model is particularly beneficial for studies with marginalized populations who may mistrust researchers. In CDR, impacted community members are fundamental project drivers. This project’s data are based on 29 months of weekly group meetings in National Survivors Union online spaces, group and individual text conversations, phone calls, and shared-document group work. The project co-developed methods for CDR with directly impacted people, including community-initiated research questions, low-threshold methods, collaborative writing strategies, coauthorship practices foregrounding directly impacted perspectives, and multiple dissemination forms. Modified CDR expands sociological methods for digital research, citizen science, and community-engaged research with vulnerable, criminalized groups. This approach may aid inclusive, innovative sociological scholarship and effective public health policy for reducing morbidity and mortality during multiple crises.