Pub Date : 2023-04-10DOI: 10.1177/00811750231160781
Jackelyn Hwang, Nikhil Naik
Analysis of neighborhood environments is important for understanding inequality. Few studies, however, use direct measures of the visible characteristics of neighborhood conditions, despite their theorized importance in shaping individual and community well-being, because collecting data on the physical conditions of places across neighborhoods and cities and over time has required extensive time and labor. The authors introduce systematic social observation at scale (SSO@S), a pipeline for using visual data, crowdsourcing, and computer vision to identify visible characteristics of neighborhoods at a large scale. The authors implement SSO@S on millions of street-level images across three physically distinct cities—Boston, Detroit, and Los Angeles—from 2007 to 2020 to identify trash across space and over time. The authors evaluate the extent to which this approach can be used to assist with systematic coding of street-level imagery through cross-validation and out-of-sample validation, class-activation mapping, and comparisons with other sources of observed neighborhood characteristics. The SSO@S approach produces estimates with high reliability that correlate with some expected demographic characteristics but not others, depending on the city. The authors conclude with an assessment of this approach for measuring visible characteristics of neighborhoods and the implications for methods and research.
{"title":"Systematic Social Observation at Scale: Using Crowdsourcing and Computer Vision to Measure Visible Neighborhood Conditions","authors":"Jackelyn Hwang, Nikhil Naik","doi":"10.1177/00811750231160781","DOIUrl":"https://doi.org/10.1177/00811750231160781","url":null,"abstract":"Analysis of neighborhood environments is important for understanding inequality. Few studies, however, use direct measures of the visible characteristics of neighborhood conditions, despite their theorized importance in shaping individual and community well-being, because collecting data on the physical conditions of places across neighborhoods and cities and over time has required extensive time and labor. The authors introduce systematic social observation at scale (SSO@S), a pipeline for using visual data, crowdsourcing, and computer vision to identify visible characteristics of neighborhoods at a large scale. The authors implement SSO@S on millions of street-level images across three physically distinct cities—Boston, Detroit, and Los Angeles—from 2007 to 2020 to identify trash across space and over time. The authors evaluate the extent to which this approach can be used to assist with systematic coding of street-level imagery through cross-validation and out-of-sample validation, class-activation mapping, and comparisons with other sources of observed neighborhood characteristics. The SSO@S approach produces estimates with high reliability that correlate with some expected demographic characteristics but not others, depending on the city. The authors conclude with an assessment of this approach for measuring visible characteristics of neighborhoods and the implications for methods and research.","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":"53 1","pages":"183 - 216"},"PeriodicalIF":3.0,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43079457","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 : 2023-03-28DOI: 10.1177/00811750231151949
E. Fosse, Christopher Winship
In a widely influential essay, Ryder argued that to understand social change, researchers should compare cohort careers, contrasting how different cohorts change over the life cycle with respect to some outcome. Ryder, however, provided few technical details on how to actually conduct a cohort analysis. In this article, the authors develop a framework for analyzing temporally structured data grounded in the construction, comparison, and decomposition of cohort careers. The authors begin by illustrating how one can analyze age-period-cohort (APC) data by constructing graphs of cohort careers. Although a useful starting point, the major problem with this approach is that the graphs are typically of sufficient complexity that it can be difficult, if not impossible, to discern the underlying trends and patterns in the data. To provide a more useful foundation for cohort analysis, the authors therefore introduce three distinct improvements over the purely graphical approach. First, they provide a mathematical definition of a cohort career, demonstrating how the underlying parameters of interest can be estimated using a reparameterized version of the conventional APC model. The authors call this the life cycle and social change (LC-SC) model. Second, they contrast the proposed model with two alternative three-factor APC models and all logically possible two-factor models, showing that none of these other models are adequate for fully representing Ryder’s ideas. Third, the authors present the article’s major accomplishment: using the LC-SC model, they show how a collection of cohort careers can be decomposed into just four basic components: a curve representing an overall intracohort trend (or life cycle change); a curve representing an overall intercohort trend (or social change); a set of common cross-period temporal fluctuations that permit variability across cohort careers; and, finally, a set of terms representing cell-specific heterogeneity (or, equivalently, interactions among age, period, and/or cohort). As the authors demonstrate, these parts can be reassembled into simpler versions of cohort careers, revealing underlying trends and patterns that may not be evident otherwise. The authors illustrate this approach by analyzing trends in political party strength in the General Social Survey.
{"title":"The Anatomy of Cohort Analysis: Decomposing Comparative Cohort Careers","authors":"E. Fosse, Christopher Winship","doi":"10.1177/00811750231151949","DOIUrl":"https://doi.org/10.1177/00811750231151949","url":null,"abstract":"In a widely influential essay, Ryder argued that to understand social change, researchers should compare cohort careers, contrasting how different cohorts change over the life cycle with respect to some outcome. Ryder, however, provided few technical details on how to actually conduct a cohort analysis. In this article, the authors develop a framework for analyzing temporally structured data grounded in the construction, comparison, and decomposition of cohort careers. The authors begin by illustrating how one can analyze age-period-cohort (APC) data by constructing graphs of cohort careers. Although a useful starting point, the major problem with this approach is that the graphs are typically of sufficient complexity that it can be difficult, if not impossible, to discern the underlying trends and patterns in the data. To provide a more useful foundation for cohort analysis, the authors therefore introduce three distinct improvements over the purely graphical approach. First, they provide a mathematical definition of a cohort career, demonstrating how the underlying parameters of interest can be estimated using a reparameterized version of the conventional APC model. The authors call this the life cycle and social change (LC-SC) model. Second, they contrast the proposed model with two alternative three-factor APC models and all logically possible two-factor models, showing that none of these other models are adequate for fully representing Ryder’s ideas. Third, the authors present the article’s major accomplishment: using the LC-SC model, they show how a collection of cohort careers can be decomposed into just four basic components: a curve representing an overall intracohort trend (or life cycle change); a curve representing an overall intercohort trend (or social change); a set of common cross-period temporal fluctuations that permit variability across cohort careers; and, finally, a set of terms representing cell-specific heterogeneity (or, equivalently, interactions among age, period, and/or cohort). As the authors demonstrate, these parts can be reassembled into simpler versions of cohort careers, revealing underlying trends and patterns that may not be evident otherwise. The authors illustrate this approach by analyzing trends in political party strength in the General Social Survey.","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":"53 1","pages":"217 - 268"},"PeriodicalIF":3.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46970228","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 : 2023-01-13DOI: 10.1177/00811750221147525
Taylor Lewis, Joseph McMichael, Charlotte Looby
Most addresses on modern address-based sampling frames derived from the U.S. Postal Service’s Computerized Delivery Sequence file have a one-to-one relationship with a household. Some addresses, however, are associated with multiple households. These addresses are referred to as drop points, and the households therein are referred to as drop point units (DPUs). DPUs pose a challenge for self-administered surveys because no apartment number or unit designation is available, making it impossible to send targeted correspondence. The authors evaluate a method for substituting sampled DPUs with similar non-DPUs, which was implemented in the 2021 Healthy Chicago Survey alongside a concurrent survey of the originally sampled DPUs. Comparing aggregate distributions of DPUs and the non-DPU substitutes, the authors observe certain differences with respect to age, employment status, marital status, and housing tenure but no substantive differences in key health outcomes measured by the survey.
{"title":"Evaluating Substitution as a Strategy for Handling U.S. Postal Service Drop Points in Self-Administered Address-Based Sampling Frame Surveys","authors":"Taylor Lewis, Joseph McMichael, Charlotte Looby","doi":"10.1177/00811750221147525","DOIUrl":"https://doi.org/10.1177/00811750221147525","url":null,"abstract":"Most addresses on modern address-based sampling frames derived from the U.S. Postal Service’s Computerized Delivery Sequence file have a one-to-one relationship with a household. Some addresses, however, are associated with multiple households. These addresses are referred to as drop points, and the households therein are referred to as drop point units (DPUs). DPUs pose a challenge for self-administered surveys because no apartment number or unit designation is available, making it impossible to send targeted correspondence. The authors evaluate a method for substituting sampled DPUs with similar non-DPUs, which was implemented in the 2021 Healthy Chicago Survey alongside a concurrent survey of the originally sampled DPUs. Comparing aggregate distributions of DPUs and the non-DPU substitutes, the authors observe certain differences with respect to age, employment status, marital status, and housing tenure but no substantive differences in key health outcomes measured by the survey.","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":"53 1","pages":"158 - 175"},"PeriodicalIF":3.0,"publicationDate":"2023-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42127373","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 : 2022-10-22DOI: 10.1177/00811750221125799
Nandana Sengupta, Madeleine Udell, N. Srebro, James Evans
Social science approaches to missing values predict avoided, unrequested, or lost information from dense data sets, typically surveys. The authors propose a matrix factorization approach to missing data imputation that (1) identifies underlying factors to model similarities across respondents and responses and (2) regularizes across factors to reduce their overinfluence for optimal data reconstruction. This approach may enable social scientists to draw new conclusions from sparse data sets with a large number of features, for example, historical or archival sources, online surveys with high attrition rates, or data sets created from Web scraping, which confound traditional imputation techniques. The authors introduce matrix factorization techniques and detail their probabilistic interpretation, and they demonstrate these techniques’ consistency with Rubin’s multiple imputation framework. The authors show via simulations using artificial data and data from real-world subsets of the General Social Survey and National Longitudinal Study of Youth cases for which matrix factorization techniques may be preferred. These findings recommend the use of matrix factorization for data reconstruction in several settings, particularly when data are Boolean and categorical and when large proportions of the data are missing.
{"title":"Sparse Data Reconstruction, Missing Value and Multiple Imputation through Matrix Factorization","authors":"Nandana Sengupta, Madeleine Udell, N. Srebro, James Evans","doi":"10.1177/00811750221125799","DOIUrl":"https://doi.org/10.1177/00811750221125799","url":null,"abstract":"Social science approaches to missing values predict avoided, unrequested, or lost information from dense data sets, typically surveys. The authors propose a matrix factorization approach to missing data imputation that (1) identifies underlying factors to model similarities across respondents and responses and (2) regularizes across factors to reduce their overinfluence for optimal data reconstruction. This approach may enable social scientists to draw new conclusions from sparse data sets with a large number of features, for example, historical or archival sources, online surveys with high attrition rates, or data sets created from Web scraping, which confound traditional imputation techniques. The authors introduce matrix factorization techniques and detail their probabilistic interpretation, and they demonstrate these techniques’ consistency with Rubin’s multiple imputation framework. The authors show via simulations using artificial data and data from real-world subsets of the General Social Survey and National Longitudinal Study of Youth cases for which matrix factorization techniques may be preferred. These findings recommend the use of matrix factorization for data reconstruction in several settings, particularly when data are Boolean and categorical and when large proportions of the data are missing.","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":"53 1","pages":"72 - 114"},"PeriodicalIF":3.0,"publicationDate":"2022-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48838062","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 : 2022-10-15DOI: 10.1177/00811750221128790
W. TenHouten, L. Schussel, Maria Gritsch, C. D. Kaplan
Because all aspects of social life have a mental component, sociology’s focus is not society alone but mind and society. Insofar as mind is an emergent level of brainwork, the description and measurement of mindwork amidst social interaction can be accomplished by neurometric measurement methodology. The authors’ topic, hyperscanning, involves the simultaneous recording of either hemodynamic or neuroelectric measurement of brain activity in two (or more) interacting individuals. The authors consider two hyperscanning methods, functional magnetic resonance imaging and electroencephalography (EEG). Although functional magnetic resonance imaging provides excellent spatial resolution of brain-region activation, the temporal resolution of EEG is unmatched. EEG’s low spatial resolution has been overcome by low-resolution electromagnetic tomography. Hyperscanning studies show that interpersonal coordination of action includes mutual entrainment or synchronization of neural dynamics, flow of information between brains, and causal effects of one brain upon another with respect to social-signaling processes involving fairness, reciprocity, trust, competition, cooperation, and leadership.
{"title":"Hyperscanning and the Future of Neurosociology","authors":"W. TenHouten, L. Schussel, Maria Gritsch, C. D. Kaplan","doi":"10.1177/00811750221128790","DOIUrl":"https://doi.org/10.1177/00811750221128790","url":null,"abstract":"Because all aspects of social life have a mental component, sociology’s focus is not society alone but mind and society. Insofar as mind is an emergent level of brainwork, the description and measurement of mindwork amidst social interaction can be accomplished by neurometric measurement methodology. The authors’ topic, hyperscanning, involves the simultaneous recording of either hemodynamic or neuroelectric measurement of brain activity in two (or more) interacting individuals. The authors consider two hyperscanning methods, functional magnetic resonance imaging and electroencephalography (EEG). Although functional magnetic resonance imaging provides excellent spatial resolution of brain-region activation, the temporal resolution of EEG is unmatched. EEG’s low spatial resolution has been overcome by low-resolution electromagnetic tomography. Hyperscanning studies show that interpersonal coordination of action includes mutual entrainment or synchronization of neural dynamics, flow of information between brains, and causal effects of one brain upon another with respect to social-signaling processes involving fairness, reciprocity, trust, competition, cooperation, and leadership.","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":"53 1","pages":"139 - 157"},"PeriodicalIF":3.0,"publicationDate":"2022-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44291541","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 : 2022-10-05DOI: 10.1177/00811750221126499
Petrus te Braak, T. P. van Tienoven, Joeri Minnen, I. Glorieux
Previous research has shown that a prolonged recall period is associated with lower data quality in time-diary research. In these studies, the recall period is roughly estimated on the basis of the period between the assigned diary day and the agreed collection day. Because this is so rudimentary, little is known about the duration of the mean recall period and its consequences for data quality. Recent advances in online methodology now allow a better investigation of the recall period using time stamps. Using a refined indicator, the authors examine the duration of the recall period, to what extent this duration is related to socioeconomic characteristics, and how a prolonged recall period affects data quality. The authors demonstrate that using online time-diary data collected from 8,535 teachers in Belgium, the mean recall period is less than 24 hr for most respondents, although respondents with many time constraints have extended recall periods. Additionally, a prolonged recall period indeed has negative consequences for data quality. Quality deterioration already arises several hours after an activity has been completed, much sooner than previous research has indicated.
{"title":"Data Quality and Recall Bias in Time-Diary Research: The Effects of Prolonged Recall Periods in Self-Administered Online Time-Use Surveys","authors":"Petrus te Braak, T. P. van Tienoven, Joeri Minnen, I. Glorieux","doi":"10.1177/00811750221126499","DOIUrl":"https://doi.org/10.1177/00811750221126499","url":null,"abstract":"Previous research has shown that a prolonged recall period is associated with lower data quality in time-diary research. In these studies, the recall period is roughly estimated on the basis of the period between the assigned diary day and the agreed collection day. Because this is so rudimentary, little is known about the duration of the mean recall period and its consequences for data quality. Recent advances in online methodology now allow a better investigation of the recall period using time stamps. Using a refined indicator, the authors examine the duration of the recall period, to what extent this duration is related to socioeconomic characteristics, and how a prolonged recall period affects data quality. The authors demonstrate that using online time-diary data collected from 8,535 teachers in Belgium, the mean recall period is less than 24 hr for most respondents, although respondents with many time constraints have extended recall periods. Additionally, a prolonged recall period indeed has negative consequences for data quality. Quality deterioration already arises several hours after an activity has been completed, much sooner than previous research has indicated.","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":"53 1","pages":"115 - 138"},"PeriodicalIF":3.0,"publicationDate":"2022-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45524136","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 : 2022-08-01DOI: 10.1177/00811750221112398
Scott M Lynch, Emma Zang
Multistate life table methods are an important tool for producing easily understood measures of population health. Most contemporary uses of these methods involve sample data, thus requiring techniques for capturing uncertainty in estimates. In recent decades, several methods have been developed to do so. Among these methods, the Bayesian approach proposed by Lynch and Brown has several unique advantages. However, the approach is limited to estimating years to be spent in only two living states, such as "healthy" and "unhealthy." In this article, the authors extend this method to allow for large state spaces with "quasi-absorbing" states. The authors illustrate the new method and show its advantages using data from the Health and Retirement Study to investigate U.S. regional differences in years of remaining life to be spent with diabetes, chronic conditions, and disabilities. The method works well and yields rich output for reporting and subsequent analyses. The expanded method also should facilitate the use of multi-state life tables to address a wider array of social science research questions.
{"title":"Bayesian Multistate Life Table Methods for Large and Complex State Spaces: Development and Illustration of a New Method.","authors":"Scott M Lynch, Emma Zang","doi":"10.1177/00811750221112398","DOIUrl":"https://doi.org/10.1177/00811750221112398","url":null,"abstract":"<p><p>Multistate life table methods are an important tool for producing easily understood measures of population health. Most contemporary uses of these methods involve sample data, thus requiring techniques for capturing uncertainty in estimates. In recent decades, several methods have been developed to do so. Among these methods, the Bayesian approach proposed by Lynch and Brown has several unique advantages. However, the approach is limited to estimating years to be spent in only two living states, such as \"healthy\" and \"unhealthy.\" In this article, the authors extend this method to allow for large state spaces with \"quasi-absorbing\" states. The authors illustrate the new method and show its advantages using data from the Health and Retirement Study to investigate U.S. regional differences in years of remaining life to be spent with diabetes, chronic conditions, and disabilities. The method works well and yields rich output for reporting and subsequent analyses. The expanded method also should facilitate the use of multi-state life tables to address a wider array of social science research questions.</p>","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":"52 2","pages":"254-286"},"PeriodicalIF":3.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10241463/pdf/nihms-1852062.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10293115","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}
Pub Date : 2022-07-29DOI: 10.1177/00811750221114857
K. Yamaguchi
This article introduces a new causal analytic method for survival analysis that retains the framework of Rubin’s causal model as an alternative to the marginal structural model (MSM). The major limitation of the MSM is a systematic bias in the effects of past treatments when the method is applied to the hazard rate analysis of nonrepeatable events in the presence of unobserved heterogeneity. This systematic bias is demonstrated in the article. The method introduced here assumes a semiparametric conditional-incidence-rate model and provides consistent estimates of the effects of present and past treatments on the conditional cumulative-incidence rate in the analysis of nonrepeatable events in the presence of unobserved heterogeneity. Unlike the MSM, which requires a sequential and cumulative use of the inverse-probability-of-treatment weighting many times for data with many time points, the new method uses the inverse-probability-of-treatment weighing only twice sequentially for estimation of the present and past treatment effects at each time of entry into treatment, and not cumulatively across different treatment entry times. Analysis of the conditional-incidence rate can also provide a more efficient parameter estimate for the treatment effect than the hazard rate model in cases where a majority of sample persons experience the event and thereby cease to be members of the risk set of the hazard rate during the period of observation. An application to an analysis of sexual initiation demonstrates that leaving home promotes sexual initiation, especially premarital sexual initiation, because it greatly increases the rate of premarital sexual initiation during the year after leaving home.
{"title":"A New RCM Approach to Survival Analysis: The Conditional-Incidence-Rate Model","authors":"K. Yamaguchi","doi":"10.1177/00811750221114857","DOIUrl":"https://doi.org/10.1177/00811750221114857","url":null,"abstract":"This article introduces a new causal analytic method for survival analysis that retains the framework of Rubin’s causal model as an alternative to the marginal structural model (MSM). The major limitation of the MSM is a systematic bias in the effects of past treatments when the method is applied to the hazard rate analysis of nonrepeatable events in the presence of unobserved heterogeneity. This systematic bias is demonstrated in the article. The method introduced here assumes a semiparametric conditional-incidence-rate model and provides consistent estimates of the effects of present and past treatments on the conditional cumulative-incidence rate in the analysis of nonrepeatable events in the presence of unobserved heterogeneity. Unlike the MSM, which requires a sequential and cumulative use of the inverse-probability-of-treatment weighting many times for data with many time points, the new method uses the inverse-probability-of-treatment weighing only twice sequentially for estimation of the present and past treatment effects at each time of entry into treatment, and not cumulatively across different treatment entry times. Analysis of the conditional-incidence rate can also provide a more efficient parameter estimate for the treatment effect than the hazard rate model in cases where a majority of sample persons experience the event and thereby cease to be members of the risk set of the hazard rate during the period of observation. An application to an analysis of sexual initiation demonstrates that leaving home promotes sexual initiation, especially premarital sexual initiation, because it greatly increases the rate of premarital sexual initiation during the year after leaving home.","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":"53 1","pages":"42 - 71"},"PeriodicalIF":3.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41740093","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 : 2022-07-05DOI: 10.1177/00811750221109568
D. Feehan, Vo Hai Son, A. Abdul-Quader
Researchers increasingly use aggregate relational data to learn about the size and distribution of survey respondents’ weak-tie personal networks. Aggregate relational data are collected by asking questions about respondents’ connectedness to many different groups (e.g., “How many teachers do you know?”). This approach can be powerful, but to use aggregate relational data, researchers must locate external information about the size of each group from a census or administrative records (e.g., the number of teachers in the population). This need for external information makes aggregate relational data difficult or impossible to collect in many settings. Here, the authors show that relatively simple modifications can overcome this need for external data, significantly increasing the flexibility of the method and weakening key assumptions required by the associated estimators. The key idea is to estimate the size of these groups from the sample of survey respondents, rather than relying on external sources of information. These methods are appropriate for using a sample survey to study the size and distribution of weak-tie network connections. They can also be used as part of the network scale-up method to estimate the size of hidden populations. The authors illustrate this approach with two empirical studies: a large simulation study and original household survey data collected in Hanoi, Vietnam.
{"title":"Survey Methods for Estimating the Size of Weak-Tie Personal Networks","authors":"D. Feehan, Vo Hai Son, A. Abdul-Quader","doi":"10.1177/00811750221109568","DOIUrl":"https://doi.org/10.1177/00811750221109568","url":null,"abstract":"Researchers increasingly use aggregate relational data to learn about the size and distribution of survey respondents’ weak-tie personal networks. Aggregate relational data are collected by asking questions about respondents’ connectedness to many different groups (e.g., “How many teachers do you know?”). This approach can be powerful, but to use aggregate relational data, researchers must locate external information about the size of each group from a census or administrative records (e.g., the number of teachers in the population). This need for external information makes aggregate relational data difficult or impossible to collect in many settings. Here, the authors show that relatively simple modifications can overcome this need for external data, significantly increasing the flexibility of the method and weakening key assumptions required by the associated estimators. The key idea is to estimate the size of these groups from the sample of survey respondents, rather than relying on external sources of information. These methods are appropriate for using a sample survey to study the size and distribution of weak-tie network connections. They can also be used as part of the network scale-up method to estimate the size of hidden populations. The authors illustrate this approach with two empirical studies: a large simulation study and original household survey data collected in Hanoi, Vietnam.","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":"52 1","pages":"193 - 219"},"PeriodicalIF":3.0,"publicationDate":"2022-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45024226","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 : 2022-06-22DOI: 10.1177/00811750221106777
L. Owens
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.
{"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}
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