Pub Date : 2020-05-29DOI: 10.1177/0081175020913899
D. Dittrich, R. Leenders, J. Mulder
The network autocorrelation model has been the workhorse for estimating and testing the strength of theories of social influence in a network. In many network studies, different types of social influence are present simultaneously and can be modeled using various connectivity matrices. Often, researchers have expectations about the order of strength of these different influence mechanisms. However, currently available methods cannot be applied to test a specific order of social influence in a network. In this article, the authors first present flexible Bayesian techniques for estimating network autocorrelation models with multiple network autocorrelation parameters. Second, they develop new Bayes factors that allow researchers to test hypotheses with order constraints on the network autocorrelation parameters in a direct manner. Concomitantly, the authors give efficient algorithms for sampling from the posterior distributions and for computing the Bayes factors. Simulation results suggest that frequentist properties of Bayesian estimators on the basis of noninformative priors for the network autocorrelation parameters are overall slightly superior to those based on maximum likelihood estimation. Furthermore, when testing statistical hypotheses, the Bayes factors show consistent behavior with evidence for a true data-generating hypothesis increasing with the sample size. Finally, the authors illustrate their methods using a data set from economic growth theory.
{"title":"Network Autocorrelation Modeling: Bayesian Techniques for Estimating and Testing Multiple Network Autocorrelations","authors":"D. Dittrich, R. Leenders, J. Mulder","doi":"10.1177/0081175020913899","DOIUrl":"https://doi.org/10.1177/0081175020913899","url":null,"abstract":"The network autocorrelation model has been the workhorse for estimating and testing the strength of theories of social influence in a network. In many network studies, different types of social influence are present simultaneously and can be modeled using various connectivity matrices. Often, researchers have expectations about the order of strength of these different influence mechanisms. However, currently available methods cannot be applied to test a specific order of social influence in a network. In this article, the authors first present flexible Bayesian techniques for estimating network autocorrelation models with multiple network autocorrelation parameters. Second, they develop new Bayes factors that allow researchers to test hypotheses with order constraints on the network autocorrelation parameters in a direct manner. Concomitantly, the authors give efficient algorithms for sampling from the posterior distributions and for computing the Bayes factors. Simulation results suggest that frequentist properties of Bayesian estimators on the basis of noninformative priors for the network autocorrelation parameters are overall slightly superior to those based on maximum likelihood estimation. Furthermore, when testing statistical hypotheses, the Bayes factors show consistent behavior with evidence for a true data-generating hypothesis increasing with the sample size. Finally, the authors illustrate their methods using a data set from economic growth theory.","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2020-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0081175020913899","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44028170","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 : 2020-05-17DOI: 10.1177/0081175020967392
James P. Murphy
Like other quantitative social scientists, network researchers benefit from pooling information from multiple observed variables to infer underlying (latent) attributes or social processes. Appropriate network data for this task is increasingly available. The inherent dependencies in relational data, however, pose unique challenges. This is especially true for the ascendant tasks of cross-network comparisons and multilevel network analysis. The author draws on item response theory and multilevel (mixed effects) modeling to propose a methodological approach that accounts for these dependencies and allows the analyst to model variation of latent dyadic traits across relations, actors, and groups precisely and parsimoniously. Examples demonstrate the approach’s utility for three important research areas: tie strength in adolescent friendships, group differences in how discussing personal problems relates to tie strength, and the analysis of multiple relations.
{"title":"Explanatory Item Response Models for Dyadic Data from Multiple Groups","authors":"James P. Murphy","doi":"10.1177/0081175020967392","DOIUrl":"https://doi.org/10.1177/0081175020967392","url":null,"abstract":"Like other quantitative social scientists, network researchers benefit from pooling information from multiple observed variables to infer underlying (latent) attributes or social processes. Appropriate network data for this task is increasingly available. The inherent dependencies in relational data, however, pose unique challenges. This is especially true for the ascendant tasks of cross-network comparisons and multilevel network analysis. The author draws on item response theory and multilevel (mixed effects) modeling to propose a methodological approach that accounts for these dependencies and allows the analyst to model variation of latent dyadic traits across relations, actors, and groups precisely and parsimoniously. Examples demonstrate the approach’s utility for three important research areas: tie strength in adolescent friendships, group differences in how discussing personal problems relates to tie strength, and the analysis of multiple relations.","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2020-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0081175020967392","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44337340","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 : 2020-04-13DOI: 10.1177/00811750211057572
Nicholas Graetz, Kevin Ummel, Daniel Aldana Cohen
Quantitative sociologists and social policymakers are increasingly interested in local context. Some city-specific studies have developed new primary data collection efforts to analyze inequality at the neighborhood level, but methods from spatial microsimulation have yet to be broadly used in sociology to take better advantage of existing public data sets. The American Community Survey (ACS) is the largest household survey in the United States and indispensable for detailed analysis of specific places and populations. The authors propose a technique, tree-based spatial microsimulation, to produce “small-area” (census-tract) estimates of any person- or household-level phenomenon that can be derived from ACS microdata variables. The approach is straightforward and computationally efficient, based only on publicly available data, and it provides more reliable estimates than do prevailing methods of microsimulation. The authors demonstrate the technique’s capabilities by producing tract-level estimates, stratified by race/ethnicity, of (1) the proportion of people in the census-tract population who have children and work in an essential occupation and (2) the proportion of people in the census-tract population living below the federal poverty threshold and in a household that spends greater than 50 percent of monthly income on rent or owner costs. These examples are relevant to understanding the sociospatial inequalities dramatized by the coronavirus disease 2019 pandemic. The authors discuss potential extensions of the technique to derive small-area estimates of variables observed in surveys other than the ACS.
{"title":"Small-Area Analyses Using Public American Community Survey Data: A Tree-Based Spatial Microsimulation Technique","authors":"Nicholas Graetz, Kevin Ummel, Daniel Aldana Cohen","doi":"10.1177/00811750211057572","DOIUrl":"https://doi.org/10.1177/00811750211057572","url":null,"abstract":"Quantitative sociologists and social policymakers are increasingly interested in local context. Some city-specific studies have developed new primary data collection efforts to analyze inequality at the neighborhood level, but methods from spatial microsimulation have yet to be broadly used in sociology to take better advantage of existing public data sets. The American Community Survey (ACS) is the largest household survey in the United States and indispensable for detailed analysis of specific places and populations. The authors propose a technique, tree-based spatial microsimulation, to produce “small-area” (census-tract) estimates of any person- or household-level phenomenon that can be derived from ACS microdata variables. The approach is straightforward and computationally efficient, based only on publicly available data, and it provides more reliable estimates than do prevailing methods of microsimulation. The authors demonstrate the technique’s capabilities by producing tract-level estimates, stratified by race/ethnicity, of (1) the proportion of people in the census-tract population who have children and work in an essential occupation and (2) the proportion of people in the census-tract population living below the federal poverty threshold and in a household that spends greater than 50 percent of monthly income on rent or owner costs. These examples are relevant to understanding the sociospatial inequalities dramatized by the coronavirus disease 2019 pandemic. The authors discuss potential extensions of the technique to derive small-area estimates of variables observed in surveys other than the ACS.","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2020-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45590985","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 : 2020-02-26DOI: 10.1177/0081175020905624
Yue Yu, Emily Smith, C. Butts
Retrospective life history designs are among the few practical approaches for collecting longitudinal network information from large populations, particularly in the context of relationships like sexual partnerships that cannot be measured via digital traces or documentary evidence. While all such designs afford the ability to “peer into the past” vis-à-vis the point of data collection, little is known about the impact of the specific design parameters on the time horizon over which such information is useful. In this article, we investigate the effect of two different survey designs on retrospective network imputation: (1) intervalN, where subjects are asked to provide information on all partners within the past N time units; and (2) lastK, where subjects are asked to provide information about their K most recent partners. We simulate a “ground truth” sexual partnership network using a published model of Krivitsky (2012), and we then sample this data using the two retrospective designs under various choices of N and K . We examine the accumulation of missingness as a function of time prior to interview, and we investigate the impact of this missingness on model-based imputation of the state of the network at prior time points via conditional ERGM prediction. We quantitatively show that—even setting aside problems of alter identification and informant accuracy—choice of survey design and parameters used can drastically change the amount of missingness in the dataset. These differences in missingness have a large impact on the quality of retrospective parameter estimation and network imputation, including important effects on properties related to disease transmission.
{"title":"Retrospective Network Imputation from Life History Data: The Impact of Designs","authors":"Yue Yu, Emily Smith, C. Butts","doi":"10.1177/0081175020905624","DOIUrl":"https://doi.org/10.1177/0081175020905624","url":null,"abstract":"Retrospective life history designs are among the few practical approaches for collecting longitudinal network information from large populations, particularly in the context of relationships like sexual partnerships that cannot be measured via digital traces or documentary evidence. While all such designs afford the ability to “peer into the past” vis-à-vis the point of data collection, little is known about the impact of the specific design parameters on the time horizon over which such information is useful. In this article, we investigate the effect of two different survey designs on retrospective network imputation: (1) intervalN, where subjects are asked to provide information on all partners within the past N time units; and (2) lastK, where subjects are asked to provide information about their K most recent partners. We simulate a “ground truth” sexual partnership network using a published model of Krivitsky (2012), and we then sample this data using the two retrospective designs under various choices of N and K . We examine the accumulation of missingness as a function of time prior to interview, and we investigate the impact of this missingness on model-based imputation of the state of the network at prior time points via conditional ERGM prediction. We quantitatively show that—even setting aside problems of alter identification and informant accuracy—choice of survey design and parameters used can drastically change the amount of missingness in the dataset. These differences in missingness have a large impact on the quality of retrospective parameter estimation and network imputation, including important effects on properties related to disease transmission.","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2020-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0081175020905624","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47531589","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 : 2020-01-07DOI: 10.1177/0081175019887992
Pablo A. Mitnik
The fact that the intergenerational income elasticity (IGE)—the workhorse measure of economic mobility—is defined in terms of the geometric mean of children’s income generates serious methodological problems. This has led to a call to replace it with the IGE of the expectation, which requires developing the methodological knowledge necessary to estimate the latter with short-run measures of income. This article contributes to this aim. The author advances a “bracketing strategy” for the set estimation of the IGE of the expectation that is equivalent to that used to set estimate (rather than point estimate) the conventional IGE with estimates obtained with the ordinary least squares and instrumental variable (IV) estimators. The proposed bracketing strategy couples estimates generated with the Poisson pseudo–maximum likelihood estimator and a generalized method of moments IV estimator of the Poisson or exponential regression model. The author develops a generalized error-in-variables model for the IV estimation of the IGE of the expectation and compares it with the corresponding model underlying the IV estimation of the conventional IGE. By considering both bracketing strategies from the perspective of the partial-identification approach to inference, the author specifies how to construct confidence intervals for the IGEs, in particular when the upper bound is estimated more than once with different sets of instruments. Finally, using data from the Panel Study of Income Dynamics, the author shows that the bracketing strategies work as expected and assesses the information they generate and how this information varies across instruments and short-run measures of parental income. Three computer programs made available as companions to the article make the set estimation of IGEs, and statistical inference, very simple endeavors.
{"title":"Intergenerational Income Elasticities, Instrumental Variable Estimation, and Bracketing Strategies","authors":"Pablo A. Mitnik","doi":"10.1177/0081175019887992","DOIUrl":"https://doi.org/10.1177/0081175019887992","url":null,"abstract":"The fact that the intergenerational income elasticity (IGE)—the workhorse measure of economic mobility—is defined in terms of the geometric mean of children’s income generates serious methodological problems. This has led to a call to replace it with the IGE of the expectation, which requires developing the methodological knowledge necessary to estimate the latter with short-run measures of income. This article contributes to this aim. The author advances a “bracketing strategy” for the set estimation of the IGE of the expectation that is equivalent to that used to set estimate (rather than point estimate) the conventional IGE with estimates obtained with the ordinary least squares and instrumental variable (IV) estimators. The proposed bracketing strategy couples estimates generated with the Poisson pseudo–maximum likelihood estimator and a generalized method of moments IV estimator of the Poisson or exponential regression model. The author develops a generalized error-in-variables model for the IV estimation of the IGE of the expectation and compares it with the corresponding model underlying the IV estimation of the conventional IGE. By considering both bracketing strategies from the perspective of the partial-identification approach to inference, the author specifies how to construct confidence intervals for the IGEs, in particular when the upper bound is estimated more than once with different sets of instruments. Finally, using data from the Panel Study of Income Dynamics, the author shows that the bracketing strategies work as expected and assesses the information they generate and how this information varies across instruments and short-run measures of parental income. Three computer programs made available as companions to the article make the set estimation of IGEs, and statistical inference, very simple endeavors.","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2020-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0081175019887992","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41668127","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 : 2019-12-30DOI: 10.1177/0081175019884591
J. Gershuny, Teresa A. Harms, A. Doherty, Emma Thomas, K. Milton, P. Kelly, C. Foster
This study provides a new test of time-use diary methodology, comparing diaries with a pair of objective criterion measures: wearable cameras and accelerometers. A volunteer sample of respondents (n = 148) completed conventional self-report paper time-use diaries using the standard UK Harmonised European Time Use Study (HETUS) instrument. On the diary day, respondents wore a camera that continuously recorded images of their activities during waking hours (approximately 1,500–2,000 images/day) and also an accelerometer that tracked their physical activity continuously throughout the 24-hour period covered by the diary. Of the initial 148 participants recruited, 131 returned usable diary and camera records, of whom 124 also provided a usable whole-day accelerometer record. The comparison of the diary data with the camera and accelerometer records strongly supports the use of diary methodology at both the aggregate (sample) and individual levels. It provides evidence that time-use data could be used to complement physical activity questionnaires for providing population-level estimates of physical activity. It also implies new opportunities for investigating techniques for calibrating metabolic equivalent of task (MET) attributions to daily activities using large-scale, population-representative time-use diary studies.
{"title":"Testing Self-Report Time-Use Diaries against Objective Instruments in Real Time","authors":"J. Gershuny, Teresa A. Harms, A. Doherty, Emma Thomas, K. Milton, P. Kelly, C. Foster","doi":"10.1177/0081175019884591","DOIUrl":"https://doi.org/10.1177/0081175019884591","url":null,"abstract":"This study provides a new test of time-use diary methodology, comparing diaries with a pair of objective criterion measures: wearable cameras and accelerometers. A volunteer sample of respondents (n = 148) completed conventional self-report paper time-use diaries using the standard UK Harmonised European Time Use Study (HETUS) instrument. On the diary day, respondents wore a camera that continuously recorded images of their activities during waking hours (approximately 1,500–2,000 images/day) and also an accelerometer that tracked their physical activity continuously throughout the 24-hour period covered by the diary. Of the initial 148 participants recruited, 131 returned usable diary and camera records, of whom 124 also provided a usable whole-day accelerometer record. The comparison of the diary data with the camera and accelerometer records strongly supports the use of diary methodology at both the aggregate (sample) and individual levels. It provides evidence that time-use data could be used to complement physical activity questionnaires for providing population-level estimates of physical activity. It also implies new opportunities for investigating techniques for calibrating metabolic equivalent of task (MET) attributions to daily activities using large-scale, population-representative time-use diary studies.","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2019-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0081175019884591","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46121645","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 : 2019-08-01DOI: 10.1177/0081175019866425
Han Zhang, Jennifer Pan
{"title":"The Challenges of “More Data” for Protest Event Analysis","authors":"Han Zhang, Jennifer Pan","doi":"10.1177/0081175019866425","DOIUrl":"https://doi.org/10.1177/0081175019866425","url":null,"abstract":"","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0081175019866425","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43738747","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 : 2019-08-01DOI: 10.1177/0081175019869320
Ekkehard Kopp
Ekkehard Kopp adopts a chronological framework to demonstrate that changes in our understanding of numbers have o� en relied on the breaking of long-held conven� ons, making way for new inven� ons that provide greater clarity and widen mathema� cal horizons. Viewed from this historical perspec� ve, mathema� cal abstrac� on emerges as neither mysterious nor immutable, but as a con� ngent, developing human ac� vity.
{"title":"Prologue","authors":"Ekkehard Kopp","doi":"10.1177/0081175019869320","DOIUrl":"https://doi.org/10.1177/0081175019869320","url":null,"abstract":"Ekkehard Kopp adopts a chronological framework to demonstrate that changes in our understanding of numbers have o� en relied on the breaking of long-held conven� ons, making way for new inven� ons that provide greater clarity and widen mathema� cal horizons. Viewed from this historical perspec� ve, mathema� cal abstrac� on emerges as neither mysterious nor immutable, but as a con� ngent, developing human ac� vity.","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0081175019869320","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43727831","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 : 2019-08-01DOI: 10.1177/0081175019867855
Jan Goldenstein, Philipp Poschmann
We thank the editorial board for the opportunity to discuss our methodological contribution in a symposium dialogue as well as the two commentators for their inspiring and challenging comments. We are especially delighted that the commentators agree on the relevance of analyzing the dynamics of manifest and latent meanings in big data using different textmining tools in general and for map analysis in particular. According to our reading, the commentators focused on quality criteria, namely, two different but highly relevant aspects of transparency in research processes. Laura K. Nelson (this volume, pp. 139–143) focused on transparency in the context of research foci and analytical steps in a text-analysis project to ensure the reproducibility of results, whereas Burt L. Monroe (this volume, pp. 132–139) focused on transparency regarding data inspection and thus the credibility of results. We structured our rejoinder as follows: First, we draw on selected aspects Nelson and Monroe posed that we believe they consider to be most important to reflect on transparency in the context of big data and text-mining tools. Second, because quality criteria such as transparency do not exist in isolation, we complement the discussion on quality by adding general issues regarding overarching textmining methodology. Finally, we conclude by providing a prospect for further establishment of big data analysis in the social sciences.
{"title":"A Quest for Transparent and Reproducible Text-Mining Methodologies in Computational Social Science","authors":"Jan Goldenstein, Philipp Poschmann","doi":"10.1177/0081175019867855","DOIUrl":"https://doi.org/10.1177/0081175019867855","url":null,"abstract":"We thank the editorial board for the opportunity to discuss our methodological contribution in a symposium dialogue as well as the two commentators for their inspiring and challenging comments. We are especially delighted that the commentators agree on the relevance of analyzing the dynamics of manifest and latent meanings in big data using different textmining tools in general and for map analysis in particular. According to our reading, the commentators focused on quality criteria, namely, two different but highly relevant aspects of transparency in research processes. Laura K. Nelson (this volume, pp. 139–143) focused on transparency in the context of research foci and analytical steps in a text-analysis project to ensure the reproducibility of results, whereas Burt L. Monroe (this volume, pp. 132–139) focused on transparency regarding data inspection and thus the credibility of results. We structured our rejoinder as follows: First, we draw on selected aspects Nelson and Monroe posed that we believe they consider to be most important to reflect on transparency in the context of big data and text-mining tools. Second, because quality criteria such as transparency do not exist in isolation, we complement the discussion on quality by adding general issues regarding overarching textmining methodology. Finally, we conclude by providing a prospect for further establishment of big data analysis in the social sciences.","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0081175019867855","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41966589","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 : 2019-08-01DOI: 10.1177/0081175019865231
B. Monroe
Goldenstein and Poschmann (hereafter GP, this volume, pp. 83–131) offer a novel approach, or combination of approaches, for tracking multiple layers of “meaning” in a collection of texts over time. The core theoretical argument is sound: Any given text conveys multiple layers of meaning simultaneously, the relationships across those layers may evolve dynamically over time in any given corpus, and different textanalytic tools capture different layers of meaning and should be able to characterize this co-evolution if used in combination. In fact, similar logic underlies recent developments at the cutting edge of natural language processing (NLP) research. There are, however, some pernicious measurement traps in the text-analytic techniques GP apply here, and in the absence of some important validation, we cannot be certain that they have captured the meaning they assert.
{"title":"The Meanings of “Meaning” in Social Scientific Text Analysis","authors":"B. Monroe","doi":"10.1177/0081175019865231","DOIUrl":"https://doi.org/10.1177/0081175019865231","url":null,"abstract":"Goldenstein and Poschmann (hereafter GP, this volume, pp. 83–131) offer a novel approach, or combination of approaches, for tracking multiple layers of “meaning” in a collection of texts over time. The core theoretical argument is sound: Any given text conveys multiple layers of meaning simultaneously, the relationships across those layers may evolve dynamically over time in any given corpus, and different textanalytic tools capture different layers of meaning and should be able to characterize this co-evolution if used in combination. In fact, similar logic underlies recent developments at the cutting edge of natural language processing (NLP) research. There are, however, some pernicious measurement traps in the text-analytic techniques GP apply here, and in the absence of some important validation, we cannot be certain that they have captured the meaning they assert.","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0081175019865231","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46770037","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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