Pub Date : 2023-06-08DOI: 10.1177/00491241231178275
Nicolas M. Legewie, Anne Nassauer
Video-based social science research is thriving. Across disciplines and topic areas, researchers use twenty-first century video data to gain novel insights into how social processes and events unfold on the ground. In recent years, “video data analysis” (VDA) has emerged as a methodological framework to facilitate this type of video-based research. The special issue “The Present and Future of Video-based Social Science Research: Innovations in Video Data Analysis” presents methodological innovations that speak to some of the most pressing debates around VDA. Contributions showcase the range of disciplines and research fields VDA is used in, from social interactions and collective behavior to neighborhoods, policing, and public health. This introductory article outlines two areas of growth in VDA methodology that the articles of this special issue speak to: taking advantage of scale and detail in VDA, and situating VDA in the canon of research methods.
{"title":"Current and Future Debates in Video Data Analysis","authors":"Nicolas M. Legewie, Anne Nassauer","doi":"10.1177/00491241231178275","DOIUrl":"https://doi.org/10.1177/00491241231178275","url":null,"abstract":"Video-based social science research is thriving. Across disciplines and topic areas, researchers use twenty-first century video data to gain novel insights into how social processes and events unfold on the ground. In recent years, “video data analysis” (VDA) has emerged as a methodological framework to facilitate this type of video-based research. The special issue “The Present and Future of Video-based Social Science Research: Innovations in Video Data Analysis” presents methodological innovations that speak to some of the most pressing debates around VDA. Contributions showcase the range of disciplines and research fields VDA is used in, from social interactions and collective behavior to neighborhoods, policing, and public health. This introductory article outlines two areas of growth in VDA methodology that the articles of this special issue speak to: taking advantage of scale and detail in VDA, and situating VDA in the canon of research methods.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"52 1","pages":"1107 - 1119"},"PeriodicalIF":6.3,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42172410","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 : 2023-05-30DOI: 10.1177/00491241231176851
Julian Schuessler, Peter Selb
Directed acyclic graphs (DAGs) are now a popular tool to inform causal inferences. We discuss how DAGs can also be used to encode theoretical assumptions about nonprobability samples and survey nonresponse and to determine whether population quantities including conditional distributions and regressions can be identified. We describe sources of bias and assumptions for eliminating it in various selection scenarios. We then introduce and analyze graphical representations of multiple selection stages in the data collection process, and highlight the strong assumptions implicit in using only design weights. Furthermore, we show that the common practice of selecting adjustment variables based on correlations with sample selection and outcome variables of interest is ill-justified and that nonresponse weighting when the interest is in causal inference may come at severe costs. Finally, we identify further areas for survey methodology research that can benefit from advances in causal graph theory.
{"title":"Graphical Causal Models for Survey Inference","authors":"Julian Schuessler, Peter Selb","doi":"10.1177/00491241231176851","DOIUrl":"https://doi.org/10.1177/00491241231176851","url":null,"abstract":"Directed acyclic graphs (DAGs) are now a popular tool to inform causal inferences. We discuss how DAGs can also be used to encode theoretical assumptions about nonprobability samples and survey nonresponse and to determine whether population quantities including conditional distributions and regressions can be identified. We describe sources of bias and assumptions for eliminating it in various selection scenarios. We then introduce and analyze graphical representations of multiple selection stages in the data collection process, and highlight the strong assumptions implicit in using only design weights. Furthermore, we show that the common practice of selecting adjustment variables based on correlations with sample selection and outcome variables of interest is ill-justified and that nonresponse weighting when the interest is in causal inference may come at severe costs. Finally, we identify further areas for survey methodology research that can benefit from advances in causal graph theory.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"294 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135479071","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 : 2023-05-29DOI: 10.1177/00491241231176850
Myoung-jae Lee, Goeun Lee, Jin-young Choi
A linear model is often used to find the effect of a binary treatment [Formula: see text] on a noncontinuous outcome [Formula: see text] with covariates [Formula: see text]. Particularly, a binary [Formula: see text] gives the popular “linear probability model (LPM),” but the linear model is untenable if [Formula: see text] contains a continuous regressor. This raises the question: what kind of treatment effect does the ordinary least squares estimator (OLS) to LPM estimate? This article shows that the OLS estimates a weighted average of the [Formula: see text]-conditional heterogeneous effect plus a bias. Under the condition that [Formula: see text] is equal to the linear projection of [Formula: see text] on [Formula: see text], the bias becomes zero, and the OLS estimates the “overlap-weighted average” of the [Formula: see text]-conditional effect. Although the condition does not hold in general, specifying the [Formula: see text]-part of the LPM such that the [Formula: see text]-part predicts [Formula: see text] well, not [Formula: see text], minimizes the bias counter-intuitively. This article also shows how to estimate the overlap-weighted average without the condition by using the “propensity-score residual” [Formula: see text]. An empirical analysis demonstrates our points.
线性模型通常用于发现二元处理[公式:见文]对具有协变量[公式:见文]的不连续结果[公式:见文]的影响。特别地，二元[公式:见文本]给出了流行的“线性概率模型(LPM)”，但是如果[公式:见文本]包含连续回归量，线性模型就站不住脚。这就提出了一个问题:普通最小二乘估计器(OLS)对LPM估计了什么样的治疗效果?本文表明，OLS估计了[公式:见文本]-条件异质性效应加上偏差的加权平均值。当[Formula: see text]等于[Formula: see text]在[Formula: see text]上的线性投影时，偏差变为零，OLS估计[Formula: see text]-条件效应的“重叠加权平均值”。虽然这个条件通常不成立，指定[公式:见文本]- LPM的一部分，使得[公式:见文本]-部分预测[公式:见文本]，而不是[公式:见文本]，可以反直觉地最小化偏差。本文还展示了如何使用“倾向-分数残差”来估计没有条件的重叠加权平均值[公式:见文本]。实证分析证明了我们的观点。
{"title":"Linear Probability Model Revisited: Why It Works and How It Should Be Specified","authors":"Myoung-jae Lee, Goeun Lee, Jin-young Choi","doi":"10.1177/00491241231176850","DOIUrl":"https://doi.org/10.1177/00491241231176850","url":null,"abstract":"A linear model is often used to find the effect of a binary treatment [Formula: see text] on a noncontinuous outcome [Formula: see text] with covariates [Formula: see text]. Particularly, a binary [Formula: see text] gives the popular “linear probability model (LPM),” but the linear model is untenable if [Formula: see text] contains a continuous regressor. This raises the question: what kind of treatment effect does the ordinary least squares estimator (OLS) to LPM estimate? This article shows that the OLS estimates a weighted average of the [Formula: see text]-conditional heterogeneous effect plus a bias. Under the condition that [Formula: see text] is equal to the linear projection of [Formula: see text] on [Formula: see text], the bias becomes zero, and the OLS estimates the “overlap-weighted average” of the [Formula: see text]-conditional effect. Although the condition does not hold in general, specifying the [Formula: see text]-part of the LPM such that the [Formula: see text]-part predicts [Formula: see text] well, not [Formula: see text], minimizes the bias counter-intuitively. This article also shows how to estimate the overlap-weighted average without the condition by using the “propensity-score residual” [Formula: see text]. An empirical analysis demonstrates our points.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"203 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135791840","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 : 2023-05-15DOI: 10.1177/00491241231171945
Jackelyn Hwang, Nima Dahir, Mayuka Sarukkai, Gabby Wright
Visual data have dramatically increased in quantity in the digital age, presenting new opportunities for social science research. However, the extensive time and labor costs to process and analyze these data with existing approaches limit their use. Computer vision methods hold promise but often require large and nonexistent training data to identify sociologically relevant variables. We present a cost-efficient method for curating training data that utilizes simple tasks and pairwise comparisons to interpret and analyze visual data at scale using computer vision. We apply our approach to the detection of trash levels across space and over time in millions of street-level images in three physically distinct US cities. By comparing to ratings produced in a controlled setting and utilizing computational methods, we demonstrate generally high reliability in the method and identify sources that limit it. Altogether, this approach expands how visual data can be used at a large scale in sociology.
{"title":"Curating Training Data for Reliable Large-Scale Visual Data Analysis: Lessons from Identifying Trash in Street View Imagery","authors":"Jackelyn Hwang, Nima Dahir, Mayuka Sarukkai, Gabby Wright","doi":"10.1177/00491241231171945","DOIUrl":"https://doi.org/10.1177/00491241231171945","url":null,"abstract":"Visual data have dramatically increased in quantity in the digital age, presenting new opportunities for social science research. However, the extensive time and labor costs to process and analyze these data with existing approaches limit their use. Computer vision methods hold promise but often require large and nonexistent training data to identify sociologically relevant variables. We present a cost-efficient method for curating training data that utilizes simple tasks and pairwise comparisons to interpret and analyze visual data at scale using computer vision. We apply our approach to the detection of trash levels across space and over time in millions of street-level images in three physically distinct US cities. By comparing to ratings produced in a controlled setting and utilizing computational methods, we demonstrate generally high reliability in the method and identify sources that limit it. Altogether, this approach expands how visual data can be used at a large scale in sociology.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"52 1","pages":"1155 - 1200"},"PeriodicalIF":6.3,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41859082","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 : 2023-04-16DOI: 10.1177/00491241231168665
Yuanmo He, Milena Tsvetkova
The rise of social media has opened countless opportunities to explore social science questions with new data and methods. However, research on socioeconomic inequality remains constrained by limit...
{"title":"A Method for Estimating Individual Socioeconomic Status of Twitter Users","authors":"Yuanmo He, Milena Tsvetkova","doi":"10.1177/00491241231168665","DOIUrl":"https://doi.org/10.1177/00491241231168665","url":null,"abstract":"The rise of social media has opened countless opportunities to explore social science questions with new data and methods. However, research on socioeconomic inequality remains constrained by limit...","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"51 27","pages":""},"PeriodicalIF":6.3,"publicationDate":"2023-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50167294","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 : 2023-03-15DOI: 10.1177/00491241231156972
Thomas Suesse, David Steel, Mark Tranmer
Multilevel models are often used to account for the hierarchical structure of social data and the inherent dependencies to produce estimates of regression coefficients, variance components associat...
多层模型通常用于解释社会数据的层次结构和固有的依赖关系，以产生回归系数，相关的方差成分…
{"title":"The Effects of Omitting Components in a Multilevel Model With Social Network Effects","authors":"Thomas Suesse, David Steel, Mark Tranmer","doi":"10.1177/00491241231156972","DOIUrl":"https://doi.org/10.1177/00491241231156972","url":null,"abstract":"Multilevel models are often used to account for the hierarchical structure of social data and the inherent dependencies to produce estimates of regression coefficients, variance components associat...","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"43 4","pages":""},"PeriodicalIF":6.3,"publicationDate":"2023-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50167456","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 : 2023-03-13DOI: 10.1177/00491241231155883
Richard A. Berk, Arun Kumar Kuchibhotla, Eric Tchetgen Tchetgen
In the United States and elsewhere, risk assessment algorithms are being used to help inform criminal justice decision-makers. A common intent is to forecast an offender’s “future dangerousness.” S...
{"title":"Improving Fairness in Criminal Justice Algorithmic Risk Assessments Using Optimal Transport and Conformal Prediction Sets","authors":"Richard A. Berk, Arun Kumar Kuchibhotla, Eric Tchetgen Tchetgen","doi":"10.1177/00491241231155883","DOIUrl":"https://doi.org/10.1177/00491241231155883","url":null,"abstract":"In the United States and elsewhere, risk assessment algorithms are being used to help inform criminal justice decision-makers. A common intent is to forecast an offender’s “future dangerousness.” S...","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"42 6","pages":""},"PeriodicalIF":6.3,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50167459","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 : 2023-02-20DOI: 10.1177/00491241231156968
John D. McCluskey, Craig D. Uchida
Video data analysis (VDA) represents an important methodological framework for contemporary research approaches to the myriad of footage available from cameras, devices, and phones. Footage from police body-worn cameras (BWCs) is anticipated to be a widely available platform for social science researchers to scrutinize the interactions between police and citizens. We examine issues of validity and reliability as related to BWCs in the context of VDA, based on an assessment of the quality of audio and video obtained from that platform. Second, we compare the coding of BWC footage obtained from a sample of police-citizen encounters to coding of the same events by on-scene coders using an instrument adapted from in-person systematic social observations (SSOs). Findings show that there are substantial and systematic audio and video gaps present in BWC footage as a source of data for social science investigation that likely impact the reliability of measures. Despite these problems, BWC data have substantial capacity for judging sequential developments, causal ordering, and the duration of events. Thus, the technology should open theoretical frames that are too cumbersome for in-person observation. Theoretical development with VDA in mind is suggested as an important pathway for future researchers in terms of framing data collection from BWCs and also suggesting areas where triangulation is essential.
{"title":"Video Data Analysis and Police Body-Worn Camera Footage","authors":"John D. McCluskey, Craig D. Uchida","doi":"10.1177/00491241231156968","DOIUrl":"https://doi.org/10.1177/00491241231156968","url":null,"abstract":"Video data analysis (VDA) represents an important methodological framework for contemporary research approaches to the myriad of footage available from cameras, devices, and phones. Footage from police body-worn cameras (BWCs) is anticipated to be a widely available platform for social science researchers to scrutinize the interactions between police and citizens. We examine issues of validity and reliability as related to BWCs in the context of VDA, based on an assessment of the quality of audio and video obtained from that platform. Second, we compare the coding of BWC footage obtained from a sample of police-citizen encounters to coding of the same events by on-scene coders using an instrument adapted from in-person systematic social observations (SSOs). Findings show that there are substantial and systematic audio and video gaps present in BWC footage as a source of data for social science investigation that likely impact the reliability of measures. Despite these problems, BWC data have substantial capacity for judging sequential developments, causal ordering, and the duration of events. Thus, the technology should open theoretical frames that are too cumbersome for in-person observation. Theoretical development with VDA in mind is suggested as an important pathway for future researchers in terms of framing data collection from BWCs and also suggesting areas where triangulation is essential.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"52 1","pages":"1120 - 1154"},"PeriodicalIF":6.3,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42377767","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 : 2023-02-14DOI: 10.1177/00491241221147495
Yoav Goldstein, Nicolas M. Legewie, Doron Shiffer-Sebba
Video data offer important insights into social processes because they enable direct observation of real-life social interaction. Though such data have become abundant and increasingly accessible, they pose challenges to scalability and measurement. Computer vision (CV), i.e., software-based automated analysis of visual material, can help address these challenges, but existing CV tools are not sufficiently tailored to analyze social interactions. We describe our novel approach, “3D social research” (3DSR), which uses CV and 3D camera footage to study kinesics and proxemics, two core elements of social interaction. Using eight videos of a scripted interaction and five real-life street scene videos, we demonstrate how 3DSR expands sociologists’ analytical toolkit by facilitating a range of scalable and precise measurements. We specifically emphasize 3DSR's potential for analyzing physical distance, movement in space, and movement rate – important aspects of kinesics and proxemics in interactions. We also assess data reliability when using 3DSR.
{"title":"3D Social Research: Analysis of Social Interaction Using Computer Vision","authors":"Yoav Goldstein, Nicolas M. Legewie, Doron Shiffer-Sebba","doi":"10.1177/00491241221147495","DOIUrl":"https://doi.org/10.1177/00491241221147495","url":null,"abstract":"Video data offer important insights into social processes because they enable direct observation of real-life social interaction. Though such data have become abundant and increasingly accessible, they pose challenges to scalability and measurement. Computer vision (CV), i.e., software-based automated analysis of visual material, can help address these challenges, but existing CV tools are not sufficiently tailored to analyze social interactions. We describe our novel approach, “3D social research” (3DSR), which uses CV and 3D camera footage to study kinesics and proxemics, two core elements of social interaction. Using eight videos of a scripted interaction and five real-life street scene videos, we demonstrate how 3DSR expands sociologists’ analytical toolkit by facilitating a range of scalable and precise measurements. We specifically emphasize 3DSR's potential for analyzing physical distance, movement in space, and movement rate – important aspects of kinesics and proxemics in interactions. We also assess data reliability when using 3DSR.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"52 1","pages":"1201 - 1238"},"PeriodicalIF":6.3,"publicationDate":"2023-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45724889","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 : 2023-02-03DOI: 10.1177/00491241221140431
Iddo Tavory
Qualitative research is deceptively approachable. With no high-end statistics or computational methods, outsiders and novices alike often feel that they can judge such research “cold,” having neither thought much about it, much less practiced it. After all, they can read the text and understand it, especially when qualitative researchers often take pains to make their prose readable. This has unfortunate results: It creates a lot of random noise in evaluation, but it also means that evaluators will tend to revert to their implicit habits of evaluation —either based on prior theoretical and political commitments, or developed through work with very different methods—when they sit on recruitment, funding, or award committees. At the heart of Small and Calarco’s Qualitative Literacy there is thus a seemingly simple question: How do we know good qualitative research when we see it? How can we tell when it isn’t? When we teach and read quantitative research, we have a more-or-less agreed upon sense of the way methods should be used and evidence should be supported. While there is never complete agreement, reviews of quantitative work tend to converge around a statistically-defined shared set of standards. Qualitative research is a different beast. While qualitative researchers usually detect good research when they see it, they seem to have a harder time turning this implicit knowledge of craft into a set of guidelines. If the impetus of the book already makes it worthwhile, the key move it makes is as important: rather than gravitating towards quantitative standards and attempting to make qualitative research as close as possible to quantitative reasoning, Small and Calarco (much as Small did in his How many cases do I need?) are adamant that the standards are both rigorous, and quite different. Book Review Symposium: Qualitative Literacy
{"title":"Deceptively Approachable: Translating Standards in Qualitative Research","authors":"Iddo Tavory","doi":"10.1177/00491241221140431","DOIUrl":"https://doi.org/10.1177/00491241221140431","url":null,"abstract":"Qualitative research is deceptively approachable. With no high-end statistics or computational methods, outsiders and novices alike often feel that they can judge such research “cold,” having neither thought much about it, much less practiced it. After all, they can read the text and understand it, especially when qualitative researchers often take pains to make their prose readable. This has unfortunate results: It creates a lot of random noise in evaluation, but it also means that evaluators will tend to revert to their implicit habits of evaluation —either based on prior theoretical and political commitments, or developed through work with very different methods—when they sit on recruitment, funding, or award committees. At the heart of Small and Calarco’s Qualitative Literacy there is thus a seemingly simple question: How do we know good qualitative research when we see it? How can we tell when it isn’t? When we teach and read quantitative research, we have a more-or-less agreed upon sense of the way methods should be used and evidence should be supported. While there is never complete agreement, reviews of quantitative work tend to converge around a statistically-defined shared set of standards. Qualitative research is a different beast. While qualitative researchers usually detect good research when they see it, they seem to have a harder time turning this implicit knowledge of craft into a set of guidelines. If the impetus of the book already makes it worthwhile, the key move it makes is as important: rather than gravitating towards quantitative standards and attempting to make qualitative research as close as possible to quantitative reasoning, Small and Calarco (much as Small did in his How many cases do I need?) are adamant that the standards are both rigorous, and quite different. Book Review Symposium: Qualitative Literacy","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"12 1","pages":"1043 - 1047"},"PeriodicalIF":6.3,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87786054","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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