Sociological Methods & Research最新文献_第6页

Social Rigidity Across and Within Generations: A Predictive Approach 跨代和代内的社会刚性：一种预测方法

IF 6.3 2区社会学

Sociological Methods & Research Pub Date : 2025-06-19 DOI: 10.1177/00491241251347984

Haowen Zheng, Siwei Cheng

{"title":"Social Rigidity Across and Within Generations: A Predictive Approach","authors":"Haowen Zheng, Siwei Cheng","doi":"10.1177/00491241251347984","DOIUrl":"https://doi.org/10.1177/00491241251347984","url":null,"abstract":"How well can individuals’ parental background and previous life experiences predict their mid-life socioeconomic status (SES) attainment? This question is central to stratification research, as a strong power of earlier experiences in predicting later-life outcomes signals substantial intra- or intergenerational status persistence, or put simply, social rigidity. Running machine learning models on panel data to predict outcomes that include hourly wage, total income, family income, and occupational status, we find that a large number (around 4,000) of predictors commonly used in the stratification literature improves the prediction of one’s life chances in middle to late adulthood by about 10 percent to 50 percent, compared with a null model that uses a simple mean of the outcome variable. The level of predictability depends on the specific outcome being analyzed, with labor market indicators like wages and occupational prestige being more predictable than broader socioeconomic measures such as overall personal and family income. Grouping a comprehensive list of predictors into four unique sets that cover family background, childhood and adolescence development, early labor market experiences, and early adulthood family formation, we find that including income, employment status, and occupational characteristics at early career significantly improves models’ prediction accuracy for mid-life SES attainment. We also illustrate the application of the predictive models to examine heterogeneity in predictability by race and gender and identify important variables through this data-driven exercise.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"51 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144319669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Causal Effect of Parent Occupation on Child Occupation: A Multivalued Treatment with Positivity Constraints 父母职业对子女职业的因果影响：一个具有正性约束的多值处理

IF 6.3 2区社会学

Sociological Methods & Research Pub Date : 2025-06-02 DOI: 10.1177/00491241251338412

Ian Lundberg, Daniel Molitor, Jennie E. Brand

引用次数: 0

Simulating Subjects: The Promise and Peril of Artificial Intelligence Stand-Ins for Social Agents and Interactions 模拟对象：人工智能代替社会代理和互动的希望与危险

IF 6.3 2区社会学

Sociological Methods & Research Pub Date : 2025-06-02 DOI: 10.1177/00491241251337316

Austin C. Kozlowski, James Evans

{"title":"Simulating Subjects: The Promise and Peril of Artificial Intelligence Stand-Ins for Social Agents and Interactions","authors":"Austin C. Kozlowski, James Evans","doi":"10.1177/00491241251337316","DOIUrl":"https://doi.org/10.1177/00491241251337316","url":null,"abstract":"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 social science, we outline a methodological foundation for simulating human subjects and their social interactions. We then identify six characteristics of current models that are likely to impair the realistic simulation of human subjects: bias, uniformity, atemporality, disembodiment, linguistic cultures, and alien intelligence. For each of these areas, we discuss promising approaches for overcoming their associated shortcomings. Given the rate of change of these models, we advocate for an ongoing methodological program for the simulation of human subjects that keeps pace with rapid technical progress, and caution that validation against human subjects data remains essential to ensure simulation accuracy.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"113 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144210944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Quantifying Narrative Similarity Across Languages 量化不同语言之间的叙事相似性

IF 6.3 2区社会学

Sociological Methods & Research Pub Date : 2025-06-02 DOI: 10.1177/00491241251340080

Hannah Waight, Solomon Messing, Anton Shirikov, Margaret E. Roberts, Jonathan Nagler, Jason Greenfield, Megan A. Brown, Kevin Aslett, Joshua A. Tucker

{"title":"Quantifying Narrative Similarity Across Languages","authors":"Hannah Waight, Solomon Messing, Anton Shirikov, Margaret E. Roberts, Jonathan Nagler, Jason Greenfield, Megan A. Brown, Kevin Aslett, Joshua A. Tucker","doi":"10.1177/00491241251340080","DOIUrl":"https://doi.org/10.1177/00491241251340080","url":null,"abstract":"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 texts share the same information and narratives, rather than the same broad themes or exact features. We propose a novel approach to measure this quantity of interest, which we call “narrative similarity,” by using large language models to distill texts to their core ideas and then compare the similarity of claims rather than of words, phrases, or sentences. The result is an estimand much closer to narrative similarity than what is possible with past relevant alternatives, including exact text reuse, which returns lexically similar documents; topic modeling, which returns topically similar documents; or an array of alternative approaches. We devise an approach to providing out-of-sample measures of performance (precision, recall, F1) and show that our approach outperforms relevant alternatives by a large margin. We apply our approach to an important case study: The spread of Russian claims about the development of a Ukrainian bioweapons program in U.S. mainstream and fringe news websites. While we focus on news in this application, our approach can be applied more broadly to the study of propaganda, misinformation, diffusion of policy and cultural objects, among other topics.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"62 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144210939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Optimal Stratification Method for Addressing Nonresponse Bias in Bayesian Adaptive Survey Design 贝叶斯自适应调查设计中一种解决无反应偏差的最优分层方法

IF 6.3 2区社会学

Sociological Methods & Research Pub Date : 2025-06-02 DOI: 10.1177/00491241251345463

Yongchao Ma, Nino Mushkudiani, Barry Schouten

{"title":"An Optimal Stratification Method for Addressing Nonresponse Bias in Bayesian Adaptive Survey Design","authors":"Yongchao Ma, Nino Mushkudiani, Barry Schouten","doi":"10.1177/00491241251345463","DOIUrl":"https://doi.org/10.1177/00491241251345463","url":null,"abstract":"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, if the auxiliary variables are weakly correlated with the target survey variables, optimizing data collection for these subgroups may not reduce nonresponse bias and may actually increase the variance of survey estimates. In this paper, we propose a stratification method to identify subgroups by: (1) predicting values of target survey variables from auxiliary variables, and (2) forming subgroups with different response propensities based on the predicted values of target survey variables. By tailoring different data collection strategies to these subgroups, we can obtain a response set with less variation in response propensities across subgroups that are directly relevant to the target survey variables. Given this rationale, we also propose to measure nonresponse bias by the coefficient of variation of response propensities estimated from the predicted target survey variables. A case study using the Dutch Health Survey shows that the proposed stratification method generally produces less variation in response propensities with respect to the predicted target survey variables compared to traditional methods, thereby leading to a response set that better resembles the population.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"51 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144210943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generative Multimodal Models for Social Science: An Application with Satellite and Streetscape Imagery 社会科学的生成多模态模型：卫星和街景图像的应用

IF 6.3 2区社会学

Sociological Methods & Research Pub Date : 2025-05-27 DOI: 10.1177/00491241251339673

Tina Law, Elizabeth Roberto

引用次数: 0

Using Large Language Models for Qualitative Analysis can Introduce Serious Bias 使用大型语言模型进行定性分析可能会引入严重的偏差

IF 6.3 2区社会学

Sociological Methods & Research Pub Date : 2025-05-27 DOI: 10.1177/00491241251338246

Julian Ashwin, Aditya Chhabra, Vijayendra Rao

引用次数: 0

Accounting for Individual-Specific Heterogeneity in Intergenerational Income Mobility 代际收入流动中个体特异性异质性的核算

IF 6.3 2区社会学

Sociological Methods & Research Pub Date : 2025-05-22 DOI: 10.1177/00491241251339654

Yoosoon Chang, Steven N. Durlauf, Bo Hu, Joon Y. Park

引用次数: 0

The Unrealized Potential of Audits: Applicant-Side Inequalities in Effort, Opportunities, and Certainty 未实现的审计潜力：申请人在努力、机会和确定性方面的不平等

IF 6.3 2区社会学

Sociological Methods & Research Pub Date : 2025-05-22 DOI: 10.1177/00491241251338240

Mike Vuolo, Sadé L. Lindsay, Vincent J. Roscigno, Shawn D. Bushway

{"title":"The Unrealized Potential of Audits: Applicant-Side Inequalities in Effort, Opportunities, and Certainty","authors":"Mike Vuolo, Sadé L. Lindsay, Vincent J. Roscigno, Shawn D. Bushway","doi":"10.1177/00491241251338240","DOIUrl":"https://doi.org/10.1177/00491241251338240","url":null,"abstract":"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 standardly reported. Specifically, applicants from groups facing lower callback rates must submit more applications to ensure an eventual callback, have fewer opportunities to choose from, and face higher uncertainty regarding how many applications to submit. These results reflect several sequential and cumulative stratification processes “real-world” applicants face that warrant attention in conventional audit reporting. Our approach can be straightforwardly applied and, we show, is particularly pertinent for employment relative to other institutional domains (e.g., education, religion). We discuss the methodological and theoretical relevance of our suggested extensions and the implications for the study of inequality, discrimination, and social closure.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"57 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144113542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Updating “The Future of Coding”: Qualitative Coding with Generative Large Language Models 更新“编码的未来”：用生成式大型语言模型进行定性编码

IF 6.3 2区社会学

Sociological Methods & Research Pub Date : 2025-05-21 DOI: 10.1177/00491241251339188

Nga Than, Leanne Fan, Tina Law, Laura K. Nelson, Leslie McCall

引用次数: 0