Sociological Methodology最新文献

{"title":"An Implausible Virtual Interview: Conversations with a Professional Research Subject","authors":"L. Owens","doi":"10.1177/00811750221106777","DOIUrl":"https://doi.org/10.1177/00811750221106777","url":null,"abstract":"The author explores interactions with one research subject who feigns credentials and invents stories in order to participate in social science research interviews online. The possibility of intentional deception among interviewees in virtually mediated fieldwork is a critical consideration in the context of the recent extensive pivot to online-based fieldwork during the need for social distancing associated with the coronavirus disease 2019 pandemic. Following this rapid shift in what is generally accepted as the “gold standard” for social science research interviews, widespread use of online-based interviewing methods will likely endure as equivalent to in-person methods. A methodological case study with implications for virtually mediated fieldwork, this article highlights some of the advantages and disadvantages of virtually mediated interviews and provides practical suggestions.","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":"52 1","pages":"121 - 140"},"PeriodicalIF":3.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49592113","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}

{"title":"Decomposing Ethnic Achievement Gaps across Multiple Levels of Analysis and for Multiple Ethnic Groups","authors":"Beatriz Gallo Cordoba, G. Leckie, W. Browne","doi":"10.1177/00811750221099503","DOIUrl":"https://doi.org/10.1177/00811750221099503","url":null,"abstract":"Ethnic achievement gaps are often explained in terms of student and school factors. The decomposition of these gaps into their within- and between-school components has therefore been applied as a strategy to quantify the overall influence of each set of factors. Three competing approaches have previously been proposed, but each is limited to the study of student-school decompositions of the gap between two ethnic groups (e.g., White and Black). The authors show that these approaches can be reformulated as mediation models facilitating new extensions to allow additional levels in the school system (e.g., classrooms, school districts, geographic areas) and multiple ethnic groups (e.g., White, Black, Hispanic, Asian). The authors illustrate these extensions using administrative data for high school students in Colombia and highlight the increased substantive insights and nuanced policy implications they afford.","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":"52 1","pages":"162 - 192"},"PeriodicalIF":3.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43907795","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}

{"title":"Lorenz Interpolation: A Method for Estimating Income Inequality from Grouped Income Data","authors":"Andrew Carr","doi":"10.1177/00811750221085586","DOIUrl":"https://doi.org/10.1177/00811750221085586","url":null,"abstract":"To understand how income inequality affects individuals and communities, researchers must have accurate measures of income inequality at lower geographic levels, such as counties, school districts, and census tracts. Studies of income inequality, however, are constrained by the tabular format in which censuses publish income data. In this article, the author proposes a new method, Lorenz interpolation, for estimating income inequality from binned income data. Using public microsample data from the American Community Survey (ACS), the author shows that Lorenz interpolation produces more accurate and reliable income inequality estimates than do alternative estimation methods. Then, using restricted ACS income data obtained through a Federal Statistical Research Data Center, the author evaluates the accuracy of Lorenz interpolation at the census tract and school district levels. Lorenz interpolation produces reliable school district–level estimates, but the method produces less reliable estimates for some income inequality measures at the tract level. These findings indicate that researchers should refrain from estimating tract-level income inequality measures from tabular data. They also show that aggregating tract income distributions to higher geographic levels can produce valid estimates of income inequality.","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":"52 1","pages":"141 - 161"},"PeriodicalIF":3.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41707373","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}

{"title":"Clinical severity of, and effectiveness of mRNA vaccines against, covid-19 from omicron, delta, and alpha SARS-CoV-2 variants in the United States: prospective observational study.","authors":"Adam S Lauring, Mark W Tenforde, James D Chappell, Manjusha Gaglani, Adit A Ginde, Tresa McNeal, Shekhar Ghamande, David J Douin, H Keipp Talbot, Jonathan D Casey, Nicholas M Mohr, Anne Zepeski, Nathan I Shapiro, Kevin W Gibbs, D Clark Files, David N Hager, Arber Shehu, Matthew E Prekker, Heidi L Erickson, Matthew C Exline, Michelle N Gong, Amira Mohamed, Nicholas J Johnson, Vasisht Srinivasan, Jay S Steingrub, Ithan D Peltan, Samuel M Brown, Emily T Martin, Arnold S Monto, Akram Khan, Catherine L Hough, Laurence W Busse, Caitlin C Ten Lohuis, Abhijit Duggal, Jennifer G Wilson, Alexandra June Gordon, Nida Qadir, Steven Y Chang, Christopher Mallow, Carolina Rivas, Hilary M Babcock, Jennie H Kwon, Natasha Halasa, Carlos G Grijalva, Todd W Rice, William B Stubblefield, Adrienne Baughman, Kelsey N Womack, Jillian P Rhoads, Christopher J Lindsell, Kimberly W Hart, Yuwei Zhu, Katherine Adams, Stephanie J Schrag, Samantha M Olson, Miwako Kobayashi, Jennifer R Verani, Manish M Patel, Wesley H Self","doi":"10.1136/bmj-2021-069761","DOIUrl":"10.1136/bmj-2021-069761","url":null,"abstract":"<p><strong>Objectives: </strong>To characterize the clinical severity of covid-19 associated with the alpha, delta, and omicron SARS-CoV-2 variants among adults admitted to hospital and to compare the effectiveness of mRNA vaccines to prevent hospital admissions related to each variant.</p><p><strong>Design: </strong>Case-control study.</p><p><strong>Setting: </strong>21 hospitals across the United States.</p><p><strong>Participants: </strong>11 690 adults (≥18 years) admitted to hospital: 5728 with covid-19 (cases) and 5962 without covid-19 (controls). Patients were classified into SARS-CoV-2 variant groups based on viral whole genome sequencing, and, if sequencing did not reveal a lineage, by the predominant circulating variant at the time of hospital admission: alpha (11 March to 3 July 2021), delta (4 July to 25 December 2021), and omicron (26 December 2021 to 14 January 2022).</p><p><strong>Main outcome measures: </strong>Vaccine effectiveness calculated using a test negative design for mRNA vaccines to prevent covid-19 related hospital admissions by each variant (alpha, delta, omicron). Among patients admitted to hospital with covid-19, disease severity on the World Health Organization's clinical progression scale was compared among variants using proportional odds regression.</p><p><strong>Results: </strong>Effectiveness of the mRNA vaccines to prevent covid-19 associated hospital admissions was 85% (95% confidence interval 82% to 88%) for two vaccine doses against the alpha variant, 85% (83% to 87%) for two doses against the delta variant, 94% (92% to 95%) for three doses against the delta variant, 65% (51% to 75%) for two doses against the omicron variant; and 86% (77% to 91%) for three doses against the omicron variant. In-hospital mortality was 7.6% (81/1060) for alpha, 12.2% (461/3788) for delta, and 7.1% (40/565) for omicron. Among unvaccinated patients with covid-19 admitted to hospital, severity on the WHO clinical progression scale was higher for the delta versus alpha variant (adjusted proportional odds ratio 1.28, 95% confidence interval 1.11 to 1.46), and lower for the omicron versus delta variant (0.61, 0.49 to 0.77). Compared with unvaccinated patients, severity was lower for vaccinated patients for each variant, including alpha (adjusted proportional odds ratio 0.33, 0.23 to 0.49), delta (0.44, 0.37 to 0.51), and omicron (0.61, 0.44 to 0.85).</p><p><strong>Conclusions: </strong>mRNA vaccines were found to be highly effective in preventing covid-19 associated hospital admissions related to the alpha, delta, and omicron variants, but three vaccine doses were required to achieve protection against omicron similar to the protection that two doses provided against the delta and alpha variants. Among adults admitted to hospital with covid-19, the omicron variant was associated with less severe disease than the delta variant but still resulted in substantial morbidity and mortality. Vaccinated patients admitted to hospital with cov","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":"43 1","pages":"e069761"},"PeriodicalIF":0.0,"publicationDate":"2022-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8905308/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86913270","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}

{"title":"Asking about the Worst First: An Examination of Contextual Effects in Factorial Vignettes","authors":"Amelie Pedneault, Dale W. Willits","doi":"10.1177/00811750211071129","DOIUrl":"https://doi.org/10.1177/00811750211071129","url":null,"abstract":"Contextual effects refer to the process by which responses given to survey questions can be affected by question order. Generally, contextual effects harm data measurement validity by introducing bias and increasing measurement error; the risk is that responses to a survey’s later questions are partly affected not only by the substance of the question but also by the preceding questions. Two opposite effects are possible: a carryover effect refers to the assimilation of later questions into those previously asked, and a backfire effect refers to the contrasting of earlier and later questions. In the case where a stereotype is activated in earlier questions of a survey, the previous literature suggests a carryover effect is more likely. The present study tests whether this is also the case in factorial vignette research by examining the influence of first presenting a vignette that corresponds more closely to a stereotypical view of sexual abuse. Results indicate a backfire effect, pointing to the distinctively different way in which vignette scenarios activate stereotypes compared to general survey questions. The results also highlight the need for researchers to control for contextual ordering effects when modeling factorial vignette data.","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":"52 1","pages":"103 - 118"},"PeriodicalIF":3.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42651125","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}

{"title":"Surveying Spontaneous Mass Protests: Mixed-mode Sampling and Field Methods","authors":"S. Yuen, Gary Tang, Francis L. F. Lee, Edmund W. Cheng","doi":"10.1177/00811750211071130","DOIUrl":"https://doi.org/10.1177/00811750211071130","url":null,"abstract":"Protest survey is a standard tool for scholars to understand protests. However, although protest survey methods are well established, the occurrence of spontaneous and leaderless protests has created new challenges for researchers. Not only do their unpredictable occurrences hinder planning, their fluidity also creates problems in obtaining representative samples. This article addresses these challenges based on our research during Hong Kong’s Anti-Extradition Law Amendment Bill Movement. We propose a mixed-mode sampling method combining face-to-face survey and smartphone-based online survey (onsite and post hoc), which can maximize sample sizes while ensuring representativeness in a cost-effective manner. Test results indicate that key variables from the survey modes are not statistically different in a consistent manner, except for age. Our findings show mixed-mode sampling can better capture protesters’ characteristics in contemporary protests and is replicable in other contexts.","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":"52 1","pages":"75 - 102"},"PeriodicalIF":3.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49169680","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}

{"title":"Language Models in Sociological Research: An Application to Classifying Large Administrative Data and Measuring Religiosity","authors":"Jeffrey L. Jensen, Daniel Karell, Cole Tanigawa-Lau, Nizar Habash, Mai Oudah, Dhia Fairus Shofia Fani","doi":"10.1177/00811750211053370","DOIUrl":"https://doi.org/10.1177/00811750211053370","url":null,"abstract":"Computational methods have become widespread in the social sciences, but probabilistic language models remain relatively underused. We introduce language models to a general social science readership. First, we offer an accessible explanation of language models, detailing how they estimate the probability of a piece of language, such as a word or sentence, on the basis of the linguistic context. Second, we apply language models in an illustrative analysis to demonstrate the mechanics of using these models in social science research. The example application uses language models to classify names in a large administrative database; the classifications are then used to measure a sociologically important phenomenon: the spatial variation of religiosity. This application highlights several advantages of language models, including their effectiveness in classifying text that contains variation around the base structures, as is often the case with localized naming conventions and dialects. We conclude by discussing language models’ potential to contribute to sociological research beyond classification through their ability to generate language.","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":"52 1","pages":"30 - 52"},"PeriodicalIF":3.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48332990","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}

{"title":"What Goes Up Might Not Come Down: Modeling Directional Asymmetry with Large-N, Large-T Data","authors":"Ryan P. Thombs, Xiaorui Huang, Jared Berry Fitzgerald","doi":"10.1177/00811750211046307","DOIUrl":"https://doi.org/10.1177/00811750211046307","url":null,"abstract":"Modeling asymmetric relationships is an emerging subject of interest among sociologists. York and Light advanced a method to estimate asymmetric models with panel data, which was further developed by Allison. However, little attention has been given to the large-N, large-T case, wherein autoregression, slope heterogeneity, and cross-sectional dependence are important issues to consider. The authors fill this gap by conducting Monte Carlo experiments comparing the bias and power of the fixed-effects estimator to a set of heterogeneous panel estimators. The authors find that dynamic misspecification can produce substantial biases in the coefficients. Furthermore, even when the dynamics are correctly specified, the fixed-effects estimator will produce inconsistent and unstable estimates of the long-run effects in the presence of slope heterogeneity. The authors demonstrate these findings by testing for directional asymmetry in the economic development–CO2 emissions relationship, a key question in macro sociology, using data for 66 countries from 1971 to 2015. The authors conclude with a set of methodological recommendations on modeling directional asymmetry.","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":"52 1","pages":"1 - 29"},"PeriodicalIF":3.0,"publicationDate":"2021-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42559507","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}

{"title":"From sequences to variables – Rethinking the relationship between sequences and outcomes","authors":"S. Helske, Jouni Helske, Guilherme Kenji Chihaya","doi":"10.31235/osf.io/srxag","DOIUrl":"https://doi.org/10.31235/osf.io/srxag","url":null,"abstract":"Sequence analysis (SA) has gained increasing interest in social sciences for theholistic analysis of life course and other longitudinal data. The usual approach isto construct sequences, calculate dissimilarities, group similar sequences with clusteranalysis, and use cluster membership as a dependent or independent variable in a linear or nonlinear regression model.This approach may be problematic as the cluster memberships are assumed to befixed known characteristics of the subjects in subsequent analysis. Furthermore, often it is more reasonable to assume that individual sequences are mixtures of multiple ideal types rather than equal members of some group. Failing to account for these issues may lead to wrong conclusions about the nature of the studied relationships.In this paper, we bring forward and discuss the problems of the \"traditional\" useof SA clusters and compare four approaches for different types of data. We conduct a simulation study and an empirical study, demonstrating the importance of considering how sequences and outcomes are related and the need to adjust the analysis accordingly. In many typical social science applications, the traditional approach is prone to result in wrong conclusions and so-called position-dependent approaches such as representativeness should be preferred.","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":"1 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45045791","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}

{"title":"Uncovering Sociological Effect Heterogeneity Using Tree-Based Machine Learning.","authors":"Jennie E Brand, Jiahui Xu, Bernard Koch, Pablo Geraldo","doi":"10.1177/0081175021993503","DOIUrl":"10.1177/0081175021993503","url":null,"abstract":"<p><p>Individuals do not respond uniformly to treatments, such as events or interventions. Sociologists routinely partition samples into subgroups to explore how the effects of treatments vary by selected covariates, such as race and gender, on the basis of theoretical priors. Data-driven discoveries are also routine, yet the analyses by which sociologists typically go about them are often problematic and seldom move us beyond our biases to explore new meaningful subgroups. Emerging machine learning methods based on decision trees allow researchers to explore sources of variation that they may not have previously considered or envisaged. In this article, the authors use tree-based machine learning, that is, causal trees, to recursively partition the sample to uncover sources of effect heterogeneity. Assessing a central topic in social inequality, college effects on wages, the authors compare what is learned from covariate and propensity score-based partitioning approaches with recursive partitioning based on causal trees. Decision trees, although superseded by forests for estimation, can be used to uncover subpopulations responsive to treatments. Using observational data, the authors expand on the existing causal tree literature by applying leaf-specific effect estimation strategies to adjust for observed confounding, including inverse propensity weighting, nearest neighbor matching, and doubly robust causal forests. We also assess localized balance metrics and sensitivity analyses to address the possibility of differential imbalance and unobserved confounding. The authors encourage researchers to follow similar data exploration practices in their work on variation in sociological effects and offer a straightforward framework by which to do so.</p>","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":"51 2","pages":"189-223"},"PeriodicalIF":2.4,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9897445/pdf/nihms-1849062.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10652104","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}