Pub Date : 2026-03-17DOI: 10.1177/00491241261430340
Lai Wei
This article introduces treatment effect on the association between outcomes (TEA), a new causal estimand that measures how a treatment influences the covariance between two post-treatment variables. TEA enables researchers to estimate how interventions affect associations that characterize social inequalities. I define TEA, provide identification results under standard causal inference assumptions, and outline estimation strategies including regression-imputation, weighting, and double machine learning estimators. I compare and contrast TEA with other common estimands in similar research settings, highlighting its unique use. I demonstrate the use of TEA through two applications: the effect of college completion on income gradient in health and the effect of college completion on issue alignment, using NLSY97 and GSS, respectively. By exploring how treatments modify associations between outcomes, TEA offers a valuable tool for sociological research on inequality, stratification, and public opinion, providing insights into the mechanisms sustaining social inequalities and informing policy interventions.
本文介绍了治疗效果对结果间关联（association of outcomes， TEA）的影响，TEA是一种新的因果估计，用于衡量治疗如何影响两个治疗后变量之间的协方差。TEA使研究人员能够估计干预措施如何影响表征社会不平等的关联。我定义了TEA，提供了标准因果推理假设下的识别结果，并概述了估计策略，包括回归-imputation、加权和双机器学习估计器。我将TEA与类似研究环境中的其他常见估计进行了比较和对比，突出了其独特的用途。我通过两个应用来证明TEA的使用：大学毕业对健康收入梯度的影响和大学毕业对问题对齐的影响，分别使用NLSY97和GSS。通过探索治疗如何改变结果之间的关联，TEA为不平等、分层和公众舆论的社会学研究提供了一个有价值的工具，为维持社会不平等的机制提供了见解，并为政策干预提供了信息。
{"title":"Treatment Effect on the Association Between Outcomes","authors":"Lai Wei","doi":"10.1177/00491241261430340","DOIUrl":"https://doi.org/10.1177/00491241261430340","url":null,"abstract":"This article introduces treatment effect on the association between outcomes (TEA), a new causal estimand that measures how a treatment influences the covariance between two post-treatment variables. TEA enables researchers to estimate how interventions affect associations that characterize social inequalities. I define TEA, provide identification results under standard causal inference assumptions, and outline estimation strategies including regression-imputation, weighting, and double machine learning estimators. I compare and contrast TEA with other common estimands in similar research settings, highlighting its unique use. I demonstrate the use of TEA through two applications: the effect of college completion on income gradient in health and the effect of college completion on issue alignment, using NLSY97 and GSS, respectively. By exploring how treatments modify associations between outcomes, TEA offers a valuable tool for sociological research on inequality, stratification, and public opinion, providing insights into the mechanisms sustaining social inequalities and informing policy interventions.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"5 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2026-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147465083","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}
Pub Date : 2026-03-11DOI: 10.1177/00491241261427726
Clara Vandeweerdt, Gregory Eady, Frederik Hjorth, Peter Thisted Dinesen
Identities are fundamental to our understanding of social and political behavior, but are challenging to measure and are rarely observed in real-world settings. We introduce a method for measuring the identity-relevant aspects of brief self-descriptions regularly used online (e.g., on social media). Our approach combines the benefits of word embeddings for finding related identity terms with the ability of clustering algorithms to aggregate terms into discrete categories. To illustrate our approach, we apply it to daily observations of bios from millions of US Twitter/X users. We present three applications of our approach with substantive findings. First, we track users’ social and political identities over time and find, among other things, that direct expressions of political affiliations are rare. Second, we map the identities that are most characteristic of each US state. Third, we show that users’ political identities are highly predictable based on non-political identity markers. With the growing availability of user self-descriptions on social media platforms and elsewhere, our approach enables researchers to map and analyze expressions of identity at scale.
{"title":"Measuring Social and Political Identities in Social Media Self-Descriptions","authors":"Clara Vandeweerdt, Gregory Eady, Frederik Hjorth, Peter Thisted Dinesen","doi":"10.1177/00491241261427726","DOIUrl":"https://doi.org/10.1177/00491241261427726","url":null,"abstract":"Identities are fundamental to our understanding of social and political behavior, but are challenging to measure and are rarely observed in real-world settings. We introduce a method for measuring the identity-relevant aspects of brief self-descriptions regularly used online (e.g., on social media). Our approach combines the benefits of word embeddings for finding related identity terms with the ability of clustering algorithms to aggregate terms into discrete categories. To illustrate our approach, we apply it to daily observations of bios from millions of US Twitter/X users. We present three applications of our approach with substantive findings. First, we track users’ social and political identities over time and find, among other things, that direct expressions of political affiliations are rare. Second, we map the identities that are most characteristic of each US state. Third, we show that users’ political identities are highly predictable based on non-political identity markers. With the growing availability of user self-descriptions on social media platforms and elsewhere, our approach enables researchers to map and analyze expressions of identity at scale.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"11 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147393358","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}
Pub Date : 2026-03-11DOI: 10.1177/00491241261420812
Jaemin Lee, Yujie Li
Status is central to understanding collaborative behavior, yet it is often difficult to measure in cultural fields where perceived standings are only partially observable. This study develops a scalable supervised machine learning approach to infer directed deference in collaboration networks using a partially observed status hierarchy derived from a ritualized site of status conferral (a televised competition series). Drawing on a longitudinal “featuring” network of more than 3,000 South Korean hip-hop artists, we train a classifier to learn how differences in status-relevant characteristics map onto observed deference patterns and then use it to estimate preferential attachment across all collaboration dyads. The resulting measure aligns closely with external expert assessments of artists’ relative standing. Applying this metric to streaming performance data, we show that collaboration improves listener engagement and that its effect varies nonlinearly with status distance: artists benefit both from partnering with higher-status collaborators and from featuring emerging talents.
{"title":"A Machine Learning Approach to Preferential Attachment and Status Advantage in a Hip-Hop Collaboration Network","authors":"Jaemin Lee, Yujie Li","doi":"10.1177/00491241261420812","DOIUrl":"https://doi.org/10.1177/00491241261420812","url":null,"abstract":"Status is central to understanding collaborative behavior, yet it is often difficult to measure in cultural fields where perceived standings are only partially observable. This study develops a scalable supervised machine learning approach to infer directed deference in collaboration networks using a partially observed status hierarchy derived from a ritualized site of status conferral (a televised competition series). Drawing on a longitudinal “featuring” network of more than 3,000 South Korean hip-hop artists, we train a classifier to learn how differences in status-relevant characteristics map onto observed deference patterns and then use it to estimate preferential attachment across all collaboration dyads. The resulting measure aligns closely with external expert assessments of artists’ relative standing. Applying this metric to streaming performance data, we show that collaboration improves listener engagement and that its effect varies nonlinearly with status distance: artists benefit both from partnering with higher-status collaborators and from featuring emerging talents.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"267 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147393357","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}
Pub Date : 2026-03-02DOI: 10.1177/00491241261426862
Nicolas M. Legewie, Anne Nassauer, Simon Kühne
Large-N, inference-based approaches are gaining increasing prominence in video-based social science research across sociology, social psychology, political science, and other fields. However, existing methodological publications on video methods do not discuss sampling methodology and empirical video-based research often includes only cursory discussions of the issue. To address this gap, this article applies insights from sampling methodology to video-based social science research. We review how sampling has been addressed in video-based social science research, reflect on its specific challenges, and propose a decision-tree flowchart to help researchers identify appropriate sampling strategies and common pitfalls. We then illustrate how the flowchart can be used in three common video-based sampling scenarios. The article thereby contributes to establishing clear guidelines for sampling in video-based social research as a reference point and as a resource for current and future practitioners, as well as reviewers and readers of such studies.
{"title":"Sampling in Video-Based Social Sciences","authors":"Nicolas M. Legewie, Anne Nassauer, Simon Kühne","doi":"10.1177/00491241261426862","DOIUrl":"https://doi.org/10.1177/00491241261426862","url":null,"abstract":"Large-N, inference-based approaches are gaining increasing prominence in video-based social science research across sociology, social psychology, political science, and other fields. However, existing methodological publications on video methods do not discuss sampling methodology and empirical video-based research often includes only cursory discussions of the issue. To address this gap, this article applies insights from sampling methodology to video-based social science research. We review how sampling has been addressed in video-based social science research, reflect on its specific challenges, and propose a decision-tree flowchart to help researchers identify appropriate sampling strategies and common pitfalls. We then illustrate how the flowchart can be used in three common video-based sampling scenarios. The article thereby contributes to establishing clear guidelines for sampling in video-based social research as a reference point and as a resource for current and future practitioners, as well as reviewers and readers of such studies.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"55 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147358800","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}
Pub Date : 2026-02-23DOI: 10.1177/00491241261420810
Kerstin Ostermann
Studying the relationship between neighborhoods and individual-level outcomes such as crime, labor market success, or intergenerational mobility has a long history in the social sciences. As local processes like gentrification constantly change neighborhoods’ composition and spatial expansion, time-constant one-size-fits-all neighborhood measures fail to capture important local dynamics. This article presents a flexible and data-driven approach for efficiently estimating overlapping and arbitrarily shaped neighborhoods with time-dynamic boundaries. Constructed in a two-stage clustering design, the first stage identifies homogeneous groups within a city, while the second stage clusters homogeneous groups by spatial proximity. In an analysis of 86 million person-year observations from 76 German cities, the paper shows that a larger spatial expansion of affluent neighborhoods negatively correlates with city crime cases, while higher neighborhood fragmentation and heterogeneity correlate positively with crime rates. The findings stress the importance of flexible neighborhood estimation techniques and the necessity to view neighborhoods as nonconstant entities.
{"title":"Beyond Proximity: Investigating Crime With Organic Neighborhoods and a Two-Stage Unsupervised Learning Approach","authors":"Kerstin Ostermann","doi":"10.1177/00491241261420810","DOIUrl":"https://doi.org/10.1177/00491241261420810","url":null,"abstract":"Studying the relationship between neighborhoods and individual-level outcomes such as crime, labor market success, or intergenerational mobility has a long history in the social sciences. As local processes like gentrification constantly change neighborhoods’ composition and spatial expansion, time-constant one-size-fits-all neighborhood measures fail to capture important local dynamics. This article presents a flexible and data-driven approach for efficiently estimating overlapping and arbitrarily shaped neighborhoods with time-dynamic boundaries. Constructed in a two-stage clustering design, the first stage identifies homogeneous groups within a city, while the second stage clusters homogeneous groups by spatial proximity. In an analysis of 86 million person-year observations from 76 German cities, the paper shows that a larger spatial expansion of affluent neighborhoods negatively correlates with city crime cases, while higher neighborhood fragmentation and heterogeneity correlate positively with crime rates. The findings stress the importance of flexible neighborhood estimation techniques and the necessity to view neighborhoods as nonconstant entities.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"3 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147274311","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}
Pub Date : 2026-02-12DOI: 10.1177/00491241251396789
Jiazhou Liang, Jolomi Tosanwumi, Ethan Fosse, Daniel Silver, Scott Sanner
Analyzing social change requires detecting patterns of continuity and difference over time. While time-series clustering offers a valuable approach, existing techniques are often limited by assuming fixed cluster definitions and static assignments of entities to clusters. To address these limitations, we introduce a unified framework of temporal clustering methods that allows for both dynamic cluster definitions and the transition of entities between clusters, generalizing and extending previous work. We also provide new algorithms for this dynamic clustering that optimize global objectives, with optional constraints on the transitions of entities across clusters. This framework expands the methodological toolkit for analyzing social change, and we provide guidelines for its application. We illustrate our approach with three case studies: polarization of social and political attitudes across U.S. states; cross-national cultural change; and the evolution of neighborhood business patterns. We conclude with directions for further research.
{"title":"Mapping Social Change: A Unified Framework for Temporal Clustering","authors":"Jiazhou Liang, Jolomi Tosanwumi, Ethan Fosse, Daniel Silver, Scott Sanner","doi":"10.1177/00491241251396789","DOIUrl":"https://doi.org/10.1177/00491241251396789","url":null,"abstract":"Analyzing social change requires detecting patterns of continuity and difference over time. While time-series clustering offers a valuable approach, existing techniques are often limited by assuming fixed cluster definitions and static assignments of entities to clusters. To address these limitations, we introduce a unified framework of temporal clustering methods that allows for both dynamic cluster definitions and the transition of entities between clusters, generalizing and extending previous work. We also provide new algorithms for this dynamic clustering that optimize global objectives, with optional constraints on the transitions of entities across clusters. This framework expands the methodological toolkit for analyzing social change, and we provide guidelines for its application. We illustrate our approach with three case studies: polarization of social and political attitudes across U.S. states; cross-national cultural change; and the evolution of neighborhood business patterns. We conclude with directions for further research.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"92 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2026-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146169753","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}
Pub Date : 2026-02-08DOI: 10.1177/00491241251412360
Diana Dávila Gordillo, Joan C. Timoneda, Sebastián Vallejo Vera
Extant work has identified two discursive forms of racism: overt and covert. While both forms have received attention in scholarly work, research on covert racism has been limited. Its subtle and context-specific nature has made it difficult to systematically identify covert racism in text, especially in large corpora. In this article, we first propose a theoretically driven and generalizable process to identify and classify covert and overt racism in text. This process allows researchers to construct coding schemes and build labeled datasets. We use the resulting dataset to train XLM-RoBERTa, a cross-lingual large language model (LLM) for supervised classification with a cutting-edge contextual understanding of text. We show that XLM-R and XLM-R-Racismo, our pretrained model, outperform other state-of-the-art approaches in classifying racism in large corpora. We illustrate our approach using a corpus of tweets relating to the Ecuadorian indígena community between 2018 and 2021.
{"title":"Machines Do See Color: Using LLMs to Classify Overt and Covert Racism in Text","authors":"Diana Dávila Gordillo, Joan C. Timoneda, Sebastián Vallejo Vera","doi":"10.1177/00491241251412360","DOIUrl":"https://doi.org/10.1177/00491241251412360","url":null,"abstract":"Extant work has identified two discursive forms of racism: overt and covert. While both forms have received attention in scholarly work, research on covert racism has been limited. Its subtle and context-specific nature has made it difficult to systematically identify covert racism in text, especially in large corpora. In this article, we first propose a theoretically driven and generalizable process to identify and classify covert and overt racism in text. This process allows researchers to construct coding schemes and build labeled datasets. We use the resulting dataset to train XLM-RoBERTa, a cross-lingual large language model (LLM) for supervised classification with a cutting-edge contextual understanding of text. We show that XLM-R and XLM-R-Racismo, our pretrained model, outperform other state-of-the-art approaches in classifying racism in large corpora. We illustrate our approach using a corpus of tweets relating to the Ecuadorian <jats:italic toggle=\"yes\">indígena</jats:italic> community between 2018 and 2021.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"22 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2026-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146138609","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}
Pub Date : 2026-01-22DOI: 10.1177/00491241251414876
Eric W. Schoon
Researchers routinely face suspicion during fieldwork. This article presents findings from interviews with 34 ethnographers who were suspected of being spies while conducting fieldwork in Turkey. I find that the way the ethnographers experienced and responded to this suspicion depended on whether they reported being questioned about whether they were spies versus accused of spying. Questioning was interpreted as sense-making, and researchers reported several common strategies for addressing the suspicions they faced. Accusations, in contrast, were associated with threats and motivated the researchers to mitigate risks to themselves and their interlocutors. Engaging with scholarship on social cognition, high-risk fieldwork, and reflexivity, I discuss how my findings offer practical insights for navigating suspicion and risk during fieldwork—even in seemingly low-risk environments—and I make the case that interrogating how researchers react to suspicion can help them clarify their positionality and aid reflexivity.
{"title":"Suspicion During Fieldwork: Lessons From Ethnographers Suspected of Espionage","authors":"Eric W. Schoon","doi":"10.1177/00491241251414876","DOIUrl":"https://doi.org/10.1177/00491241251414876","url":null,"abstract":"Researchers routinely face suspicion during fieldwork. This article presents findings from interviews with 34 ethnographers who were suspected of being spies while conducting fieldwork in Turkey. I find that the way the ethnographers experienced and responded to this suspicion depended on whether they reported being <jats:italic toggle=\"yes\">questioned</jats:italic> about whether they were spies versus <jats:italic toggle=\"yes\">accused</jats:italic> of spying. Questioning was interpreted as sense-making, and researchers reported several common strategies for addressing the suspicions they faced. Accusations, in contrast, were associated with threats and motivated the researchers to mitigate risks to themselves and their interlocutors. Engaging with scholarship on social cognition, high-risk fieldwork, and reflexivity, I discuss how my findings offer practical insights for navigating suspicion and risk during fieldwork—even in seemingly low-risk environments—and I make the case that interrogating how researchers react to suspicion can help them clarify their positionality and aid reflexivity.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"275 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146021869","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}
Pub Date : 2025-12-17DOI: 10.1177/00491241251405869
Christopher Bratt
Can aggregated composite scores be used to compare countries or other groups despite measurement non-invariance? We propose a pragmatic approach, emphasizing that measurement invariance is valuable but not strictly necessary for all such comparisons. For descriptive analyses of group differences, composite scores may outperform factor-analytic approaches, because they are more intuitive and can capture multiple dimensions. Using data from the European Social Survey (39 countries, 11 measurement occasions, 546,954 respondents), we examined social and political trust. Composite scores aggregated to the country level were practically indistinguishable from countries’ factor scores based on approximate measurement invariance testing. We conclude that composite scores can suffice for simple group comparisons, though their suitability depends on the data. They can, however, underestimate uncertainty, producing overly narrow confidence intervals. We further show that measurement invariance does not guarantee measurement equivalence. Finally, we highlight how researchers can leverage data even if measurement invariance fails.
{"title":"Benefits of a Pragmatic Approach: Rethinking Measurement Invariance and Composite Scores in Cross-Cultural Research","authors":"Christopher Bratt","doi":"10.1177/00491241251405869","DOIUrl":"https://doi.org/10.1177/00491241251405869","url":null,"abstract":"Can aggregated composite scores be used to compare countries or other groups despite measurement non-invariance? We propose a pragmatic approach, emphasizing that measurement invariance is valuable but not strictly necessary for all such comparisons. For descriptive analyses of group differences, composite scores may outperform factor-analytic approaches, because they are more intuitive and can capture multiple dimensions. Using data from the European Social Survey (39 countries, 11 measurement occasions, 546,954 respondents), we examined social and political trust. Composite scores aggregated to the country level were practically indistinguishable from countries’ factor scores based on approximate measurement invariance testing. We conclude that composite scores can suffice for simple group comparisons, though their suitability depends on the data. They can, however, underestimate uncertainty, producing overly narrow confidence intervals. We further show that measurement invariance does not guarantee measurement equivalence. Finally, we highlight how researchers can leverage data even if measurement invariance fails.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"163 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145770615","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}
Pub Date : 2025-12-10DOI: 10.1177/00491241251403078
Ingrid Mauerer, Gerhard Tutz
We present an item response model for ordinal public opinion data to understand individual-level variation in attitudes as a function of covariates. The approach allows investigating how individuals (or population subgroups) differ in substantive stances and attitude strength. It is a two-dimensional partial credit model that incorporates covariates linked to attitude direction and strength into the basic model. We exemplify the types of substantive insights into heterogeneity that can be obtained from the approach but not from existing models with two applications: attitudes toward gender equality (European Values Study) and the evaluation of presidential candidates (American National Election Study).
{"title":"An Ordinal Item Response Model for Understanding Attitudes","authors":"Ingrid Mauerer, Gerhard Tutz","doi":"10.1177/00491241251403078","DOIUrl":"https://doi.org/10.1177/00491241251403078","url":null,"abstract":"We present an item response model for ordinal public opinion data to understand individual-level variation in attitudes as a function of covariates. The approach allows investigating how individuals (or population subgroups) differ in substantive stances and attitude strength. It is a two-dimensional partial credit model that incorporates covariates linked to attitude direction and strength into the basic model. We exemplify the types of substantive insights into heterogeneity that can be obtained from the approach but not from existing models with two applications: attitudes toward gender equality (European Values Study) and the evaluation of presidential candidates (American National Election Study).","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"1 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145717535","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}
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