Pub Date : 2024-09-19DOI: 10.1177/00491241241277524
Joanne W. Golann, Lori Bougher, Richard Hall, Thomas J. Espenshade
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 are framed by our own efforts to create a secure and user-friendly database for the New Jersey Families Study, a two-week, in-home video study of 21 families with a 2- to 4-year-old child. In collecting over 11,470 hours of video data, the New Jersey Families Study is one of the very few large-scale video projects in the field of sociology. This project has provided us with a unique opportunity to explore video data management and data sharing techniques, particularly in light of a host of cutting-edge developments in data science.
数据共享和透明度在社会科学领域越来越普遍。在本文中，我们将概述在开发大型视频数据集以供研究人员共享时，在伦理、方法和技术方面需要考虑的问题和面临的挑战。我们将讨论数据安全、存储和访问以及数据记录、标记和转录等问题。我们的讨论以我们自己为新泽西家庭研究（New Jersey Families Study）创建一个安全且用户友好的数据库所做的努力为框架，该研究是对 21 个有一个 2-4 岁孩子的家庭进行的为期两周的家庭视频研究。新泽西家庭研究收集了超过 11,470 小时的视频数据，是社会学领域为数不多的大型视频项目之一。该项目为我们提供了一个探索视频数据管理和数据共享技术的难得机会，尤其是在数据科学取得一系列前沿发展的情况下。
{"title":"Sharing Big Video Data: Ethics, Methods, and Technology","authors":"Joanne W. Golann, Lori Bougher, Richard Hall, Thomas J. Espenshade","doi":"10.1177/00491241241277524","DOIUrl":"https://doi.org/10.1177/00491241241277524","url":null,"abstract":"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 are framed by our own efforts to create a secure and user-friendly database for the New Jersey Families Study, a two-week, in-home video study of 21 families with a 2- to 4-year-old child. In collecting over 11,470 hours of video data, the New Jersey Families Study is one of the very few large-scale video projects in the field of sociology. This project has provided us with a unique opportunity to explore video data management and data sharing techniques, particularly in light of a host of cutting-edge developments in data science.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142306403","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 : 2024-09-03DOI: 10.1177/00491241241268775
Tianyu Shen, Collin F. Payne, Maria Jahromi
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. This paper proposes a multiple multistate method (MMM) that situates the multistate model within the broader family of vector autoregressive models. Our approach allows the incorporation of the coevolution of multiple life course factors and provides a flexible yet simple way to model two or more time-varying variables with the multistate model. We demonstrate the MMM in two empirical applications, showing the flexibility of the approach to explore health expectancies with complex state spaces.
{"title":"Dynamics of Health Expectancy: An Introduction to the Multiple Multistate Method (MMM)","authors":"Tianyu Shen, Collin F. Payne, Maria Jahromi","doi":"10.1177/00491241241268775","DOIUrl":"https://doi.org/10.1177/00491241241268775","url":null,"abstract":"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. This paper proposes a multiple multistate method (MMM) that situates the multistate model within the broader family of vector autoregressive models. Our approach allows the incorporation of the coevolution of multiple life course factors and provides a flexible yet simple way to model two or more time-varying variables with the multistate model. We demonstrate the MMM in two empirical applications, showing the flexibility of the approach to explore health expectancies with complex state spaces.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142130631","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 : 2024-08-22DOI: 10.1177/00491241241268453
Miriam Hurtado Bodell, Måns Magnusson, Marc Keuschnigg
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 to identify associations in text based on word co-occurrences. The method estimates a concept’s shared interpretation (or framing) via its associations with other frequently co-occurring topics. In a case study, we extract longitudinal measures of media frames regarding immigration from a vast corpus of millions of Swedish newspaper articles from the period 1945–2019. We infer turning points that partition the immigration discourse into meaningful eras and locate Sweden’s era of multicultural ideals that coined its tolerant reputation.
{"title":"Seeded Topic Models in Digital Archives: Analyzing Interpretations of Immigration in Swedish Newspapers, 1945–2019","authors":"Miriam Hurtado Bodell, Måns Magnusson, Marc Keuschnigg","doi":"10.1177/00491241241268453","DOIUrl":"https://doi.org/10.1177/00491241241268453","url":null,"abstract":"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 to identify associations in text based on word co-occurrences. The method estimates a concept’s shared interpretation (or framing) via its associations with other frequently co-occurring topics. In a case study, we extract longitudinal measures of media frames regarding immigration from a vast corpus of millions of Swedish newspaper articles from the period 1945–2019. We infer turning points that partition the immigration discourse into meaningful eras and locate Sweden’s era of multicultural ideals that coined its tolerant reputation.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142042539","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 : 2024-08-16DOI: 10.1177/00491241241234866
Bernard J. Koch, Tim Sainburg, Pablo Geraldo Bastías, Song Jiang, Yizhou Sun, Jacob G. Foster
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 nonlinear, time-varying, or encoded in text, networks, and images. To maximize accessibility, we also introduce prerequisite concepts from causal inference and deep learning. The primer differs from other treatments of deep learning and causal inference in its sharp focus on observational causal estimation, its extended exposition of key algorithms, and its detailed tutorials for implementing, training, and selecting among deep estimators in TensorFlow 2 and PyTorch.
{"title":"A Primer on Deep Learning for Causal Inference","authors":"Bernard J. Koch, Tim Sainburg, Pablo Geraldo Bastías, Song Jiang, Yizhou Sun, Jacob G. Foster","doi":"10.1177/00491241241234866","DOIUrl":"https://doi.org/10.1177/00491241241234866","url":null,"abstract":"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 nonlinear, time-varying, or encoded in text, networks, and images. To maximize accessibility, we also introduce prerequisite concepts from causal inference and deep learning. The primer differs from other treatments of deep learning and causal inference in its sharp focus on observational causal estimation, its extended exposition of key algorithms, and its detailed tutorials for implementing, training, and selecting among deep estimators in TensorFlow 2 and PyTorch.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141994354","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 : 2024-08-09DOI: 10.1177/00491241241268770
Erin Macke, Claire Daviss, Emma Williams-Baron
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 can be used to analyze digital outcomes of theoretical and empirical interest, while reducing the risk of social desirability bias. We demonstrate the feasibility and utility of collecting digital trace data during online survey experiments through two original studies. In both, participants evaluated interactive digital resumes designed to track participants’ clicks, mouse movements, and time spent on the resumes. This novel approach allowed us to better understand participants’ search for information and cognitive processing in hiring decisions. There is immense, untapped potential value in collecting digital trace data during online survey experiments and using it to address important sociological research questions.
{"title":"Untapped Potential: Designed Digital Trace Data in Online Survey Experiments","authors":"Erin Macke, Claire Daviss, Emma Williams-Baron","doi":"10.1177/00491241241268770","DOIUrl":"https://doi.org/10.1177/00491241241268770","url":null,"abstract":"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 can be used to analyze digital outcomes of theoretical and empirical interest, while reducing the risk of social desirability bias. We demonstrate the feasibility and utility of collecting digital trace data during online survey experiments through two original studies. In both, participants evaluated interactive digital resumes designed to track participants’ clicks, mouse movements, and time spent on the resumes. This novel approach allowed us to better understand participants’ search for information and cognitive processing in hiring decisions. There is immense, untapped potential value in collecting digital trace data during online survey experiments and using it to address important sociological research questions.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141910238","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 : 2024-08-09DOI: 10.1177/00491241241235900
Chris Felton, Brandon M. Stewart
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 three methodological problems IV faces: (i) identification bias, an asymptotic bias from assumption violations; (ii) estimation bias, a finite-sample bias that persists even when assumptions hold; and (iii) type-M error, the exaggeration of effect size given statistical significance. In each case, we emphasize how weak instruments exacerbate these problems and make results sensitive to minor violations of assumptions. We survey IV papers from top sociology journals, finding that assumptions often go unstated and robust uncertainty measures are rarely used. We provide a practical checklist to show how IV, despite its fragility, can still be useful when handled with care.
工具变量（IV）分析是从观察数据中得出因果推论的一种强大但脆弱的工具。在治疗与结果之间可能存在未测量混杂因素的情况下，社会学家越来越多地采用这种策略。本文以社会学应用为重点，回顾了 IV 所需的假设以及违反这些假设的后果。我们强调了 IV 所面临的三个方法问题：(i) 识别偏差，即违反假设产生的渐近偏差；(ii) 估计偏差，即即使假设成立也会持续存在的有限样本偏差；(iii) M 型误差，即在统计显著性条件下夸大效应大小。在每种情况下，我们都会强调弱工具会如何加剧这些问题，并使结果对微小的违反假设的情况变得敏感。我们调查了顶级社会学期刊中的 IV 篇论文，发现这些论文往往没有说明假设，也很少使用稳健的不确定性测量方法。我们提供了一份实用的核对表，说明尽管 IV 很脆弱，但只要小心处理，它仍然是有用的。
{"title":"Handle with Care: A Sociologist’s Guide to Causal Inference with Instrumental Variables","authors":"Chris Felton, Brandon M. Stewart","doi":"10.1177/00491241241235900","DOIUrl":"https://doi.org/10.1177/00491241241235900","url":null,"abstract":"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 three methodological problems IV faces: (i) identification bias, an asymptotic bias from assumption violations; (ii) estimation bias, a finite-sample bias that persists even when assumptions hold; and (iii) type-M error, the exaggeration of effect size given statistical significance. In each case, we emphasize how weak instruments exacerbate these problems and make results sensitive to minor violations of assumptions. We survey IV papers from top sociology journals, finding that assumptions often go unstated and robust uncertainty measures are rarely used. We provide a practical checklist to show how IV, despite its fragility, can still be useful when handled with care.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141910241","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 : 2024-08-02DOI: 10.1177/00491241241266279
Jiwon Lee
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. Because the two methods take alternative positions on the appropriate cohort-effect estimands, the comparison underscores the importance of well-grounded conceptual foundations in APC analysis.
{"title":"Age, Period, and Cohort Analysis With Bounding and Interactions","authors":"Jiwon Lee","doi":"10.1177/00491241241266279","DOIUrl":"https://doi.org/10.1177/00491241241266279","url":null,"abstract":"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. Because the two methods take alternative positions on the appropriate cohort-effect estimands, the comparison underscores the importance of well-grounded conceptual foundations in APC analysis.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141880349","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 : 2024-07-26DOI: 10.1177/00491241241264562
Soojin Park, Namhwa Lee, Rafael Quintana
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 approaches largely overlook individual characteristics when designing (hypothetical) interventions to reduce disparities. To address this issue, we extend current longitudinal mediation approaches to the context of disparities research. Specifically, we develop a novel decomposition analysis method that addresses individual characteristics by (a) using optimal dynamic treatment regimes (DTRs) and (b) conditioning on a selective set of individual characteristics. Incorporating optimal DTRs into the design of interventions can be used to strike a balance between equity (reducing disparities) and excellence (improving individuals’ outcomes). We illustrate the proposed method using the High School Longitudinal Study data.
{"title":"Causal Decomposition Analysis With Time-Varying Mediators: Designing Individualized Interventions to Reduce Social Disparities","authors":"Soojin Park, Namhwa Lee, Rafael Quintana","doi":"10.1177/00491241241264562","DOIUrl":"https://doi.org/10.1177/00491241241264562","url":null,"abstract":"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 approaches largely overlook individual characteristics when designing (hypothetical) interventions to reduce disparities. To address this issue, we extend current longitudinal mediation approaches to the context of disparities research. Specifically, we develop a novel decomposition analysis method that addresses individual characteristics by (a) using optimal dynamic treatment regimes (DTRs) and (b) conditioning on a selective set of individual characteristics. Incorporating optimal DTRs into the design of interventions can be used to strike a balance between equity (reducing disparities) and excellence (improving individuals’ outcomes). We illustrate the proposed method using the High School Longitudinal Study data.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141768471","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 : 2024-06-20DOI: 10.1177/00491241241263701
{"title":"Corrigendum to “Individual Components of Three Inequality Measures for Analyzing Shapes of Inequality”","authors":"","doi":"10.1177/00491241241263701","DOIUrl":"https://doi.org/10.1177/00491241241263701","url":null,"abstract":"","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141448566","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 : 2024-05-01Epub Date: 2022-02-07DOI: 10.1177/00491241211067512
Aprile D Benner, Shanting Chen, Celeste C Fernandez, Mark D Hayward
Discrimination is associated with numerous psychological health outcomes over the life course. The nine-item Everyday Discrimination Scale (EDS) is one of the most widely used measures of discrimination; however, this nine-item measure may not be feasible in large-scale population health surveys where a shortened discrimination measure would be advantageous. The current study examined the construct validity of a combined two-item discrimination measure adapted from the EDS by Add Health (N = 14,839) as compared to the full nine-item EDS and a two-item EDS scale (parallel to the adapted combined measure) used in the National Survey of American Life (NSAL; N = 1,111) and National Latino and Asian American Study (NLAAS) studies (N = 1,055). Results identified convergence among the EDS scales, with high item-total correlations, convergent validity, and criterion validity for psychological outcomes, thus providing evidence for the construct validity of the two-item combined scale. Taken together, the findings provide support for using this reduced scale in studies where the full EDS scale is not available.
{"title":"The Potential for Using a Shortened Version of the Everyday Discrimination Scale in Population Research with Young Adults: A Construct Validation Investigation.","authors":"Aprile D Benner, Shanting Chen, Celeste C Fernandez, Mark D Hayward","doi":"10.1177/00491241211067512","DOIUrl":"10.1177/00491241211067512","url":null,"abstract":"<p><p>Discrimination is associated with numerous psychological health outcomes over the life course. The nine-item Everyday Discrimination Scale (EDS) is one of the most widely used measures of discrimination; however, this nine-item measure may not be feasible in large-scale population health surveys where a shortened discrimination measure would be advantageous. The current study examined the construct validity of a combined two-item discrimination measure adapted from the EDS by Add Health (<i>N</i> = 14,839) as compared to the full nine-item EDS and a two-item EDS scale (parallel to the adapted combined measure) used in the National Survey of American Life (NSAL; <i>N</i> = 1,111) and National Latino and Asian American Study (NLAAS) studies (<i>N</i> = 1,055). Results identified convergence among the EDS scales, with high item-total correlations, convergent validity, and criterion validity for psychological outcomes, thus providing evidence for the construct validity of the two-item combined scale. Taken together, the findings provide support for using this reduced scale in studies where the full EDS scale is not available.</p>","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11136476/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41461461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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