The article presents the experience of implementing discourse analysis methods as interpreted by E. Laclau and C. Mouffe and the concept mapping method as interpreted by W. Trochim through the network analysis technique using the example of healthy eating. The result of the analysis is a graph that makes it possible to highlight the struggle of discourse agents for the meaning of key discursive signs, as well as the conceptual map of participants in an online discussion on controversial issues within the topic discussed in the article as an example. The empirical basis for the discourse analysis was 3 000 collected comments in four communities about healthy eating on the social networks VKontakte and Odnoklassniki. The modified versions of the methods of concept mapping and discourse analysis were adapted for the analysis of online discussions. Subsequent validation of the methods seems to be a promising direction for further research. The proposed designs have a number of limitations, which are discussed in the article.
{"title":"Experience of implementing discourse analysis and conceptual mapping of healthy eating communities","authors":"Sergey Tkach, Polina Vorobyova, Maya Rusakova","doi":"10.19181/4m.2023.32.1.4","DOIUrl":"https://doi.org/10.19181/4m.2023.32.1.4","url":null,"abstract":"The article presents the experience of implementing discourse analysis methods as interpreted by E. Laclau and C. Mouffe and the concept mapping method as interpreted by W. Trochim through the network analysis technique using the example of healthy eating. The result of the analysis is a graph that makes it possible to highlight the struggle of discourse agents for the meaning of key discursive signs, as well as the conceptual map of participants in an online discussion on controversial issues within the topic discussed in the article as an example. The empirical basis for the discourse analysis was 3 000 collected comments in four communities about healthy eating on the social networks VKontakte and Odnoklassniki. The modified versions of the methods of concept mapping and discourse analysis were adapted for the analysis of online discussions. Subsequent validation of the methods seems to be a promising direction for further research. The proposed designs have a number of limitations, which are discussed in the article.","PeriodicalId":271863,"journal":{"name":"Sociology: methodology, methods, mathematical modeling (Sociology: 4M)","volume":"49 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141817981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article is devoted to the application of qualitative network analysis on the example of studying the social interactions of young parents in migration. A conceptual description of qualitative network analysis, network visualization features, design of qualitative network research, comparison of two approaches to building network maps and an example of their analysis are presented. Qualitative network analysis is aimed at studying relationships in the network and consists of interpretative and structural components, which are implemented through interviews and building a network map. The two approaches to building network maps differ in that in one case, network maps are built by the researcher based on an interview, and in the other case, the construction of the map is delegated to the informant himself. The first method is limited by the interview data and is their structural visualization. The second method of building a network map by the informant himself creates additional resources for interpretation – contrasting narratives and visual structuring of the informant's interactions.
{"title":"Qualitative social network analysis in practice: comparison of methods for network maps construction","authors":"Aryuna Kim","doi":"10.19181/4m.2023.32.1.3","DOIUrl":"https://doi.org/10.19181/4m.2023.32.1.3","url":null,"abstract":"This article is devoted to the application of qualitative network analysis on the example of studying the social interactions of young parents in migration. A conceptual description of qualitative network analysis, network visualization features, design of qualitative network research, comparison of two approaches to building network maps and an example of their analysis are presented. Qualitative network analysis is aimed at studying relationships in the network and consists of interpretative and structural components, which are implemented through interviews and building a network map. The two approaches to building network maps differ in that in one case, network maps are built by the researcher based on an interview, and in the other case, the construction of the map is delegated to the informant himself. The first method is limited by the interview data and is their structural visualization. The second method of building a network map by the informant himself creates additional resources for interpretation – contrasting narratives and visual structuring of the informant's interactions.","PeriodicalId":271863,"journal":{"name":"Sociology: methodology, methods, mathematical modeling (Sociology: 4M)","volume":"58 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141817922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Daria Maltseva, Irina Pavlova, Lika Kapustina, Vasilisa Vashchenko, Dalibor Fiala
This article presents a comparative analysis of two major scientific publication databases: Web of Science Core Collection and eLibrary – to identify their differences and unique opportunities for exploration of bibliographic networks of Russian scientific authors. Current shortage of tools and approaches for collection, processing and analysis of bibliographic data in the Russian language constitutes the relevance of this study. Empirical analysis is based on comparison of respective arrays of scientific publications in the field of sociology over the period of 2010-2021. We propose a set of comparison criteria including those related to the procedure of data access, quality of data management, quantitative and qualitative features of the data. Inspection of the databases based on the proposed criteria aids in identification of intersections between both the collections and the respective qualitative observations about them. We make conclusions regarding the comparative advantages and weaknesses of both databases in regards to their potential as the sole data source for bibliographic studies, and make recommendations for their effective use in research on Russian science.
本文对两个主要科学出版物数据库进行了比较分析：Web of Science Core Collection 和 eLibrary，以确定它们之间的差异以及探索俄罗斯科学作者书目网络的独特机会。目前缺乏收集、处理和分析俄语书目数据的工具和方法，这是本研究的相关性所在。实证分析基于对 2010-2021 年间社会学领域各自科学出版物阵列的比较。我们提出了一套比较标准，包括与数据访问程序、数据管理质量、数据的定量和定性特征相关的标准。根据建议的标准对数据库进行检查，有助于确定两个数据集之间的交叉点以及对它们各自的定性观察。我们就这两个数据库作为书目研究唯一数据源的潜力方面的相对优势和劣势得出结论，并就在俄罗斯科学研究中有效使用这两个数据库提出建议。
{"title":"Comparative analysis of the capabilities of WoS and eLibrary for analyzing bibliographic networks","authors":"Daria Maltseva, Irina Pavlova, Lika Kapustina, Vasilisa Vashchenko, Dalibor Fiala","doi":"10.19181/4m.2023.32.1.1","DOIUrl":"https://doi.org/10.19181/4m.2023.32.1.1","url":null,"abstract":"This article presents a comparative analysis of two major scientific publication databases: Web of Science Core Collection and eLibrary – to identify their differences and unique opportunities for exploration of bibliographic networks of Russian scientific authors. Current shortage of tools and approaches for collection, processing and analysis of bibliographic data in the Russian language constitutes the relevance of this study. Empirical analysis is based on comparison of respective arrays of scientific publications in the field of sociology over the period of 2010-2021. We propose a set of comparison criteria including those related to the procedure of data access, quality of data management, quantitative and qualitative features of the data. Inspection of the databases based on the proposed criteria aids in identification of intersections between both the collections and the respective qualitative observations about them. We make conclusions regarding the comparative advantages and weaknesses of both databases in regards to their potential as the sole data source for bibliographic studies, and make recommendations for their effective use in research on Russian science.","PeriodicalId":271863,"journal":{"name":"Sociology: methodology, methods, mathematical modeling (Sociology: 4M)","volume":"80 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141818847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
O. Chepyuk, O. Angelova, A. Sochkov, Tatyana Podolskaya
Based on a data set (100 biographies) created by the authors through content analysis of biographical material about outstanding scientists of the 19th and 20th centuries in the humanities and natural sciences, the clustering of professional trajectories of gifted individuals was carried out. Neural network analysis based on self-organizing Kohonen maps was used as a clustering method. The professional trajectories were formed within the framework of the behavioral model of the linear-stage approach to studying life cycles. Within this approach, career and professional self-realization are understood as a sequence of evolutionary stages fixed in their order of occurrence. Each stage was encoded, and the biographies were transformed into a vector system. In turn, the task of clustering consisted in dividing a hundred vectors into typical groups with several real-valued coordinates. The criteria for the quality of clustering were the minimum sum of quantization errors and the silhouette coefficient. As a result of the study, seven professional trajectories of gifted individuals were identified and interpreted. The analysis of trajectories was carried out from the point of view of the speed of success (average age of success) and those factors and conditions of the life path that could affect either rapid or slow achievement of professional goals and self-realization. This example demonstrates the possibilities and limitations of using neural network analysis for solving similar research tasks, especially when working with complex cluster forms and finding their optimal number.
{"title":"Typology of professional trajectories of gifted individuals using neural network analysis","authors":"O. Chepyuk, O. Angelova, A. Sochkov, Tatyana Podolskaya","doi":"10.19181/4m.2023.32.1.5","DOIUrl":"https://doi.org/10.19181/4m.2023.32.1.5","url":null,"abstract":"Based on a data set (100 biographies) created by the authors through content analysis of biographical material about outstanding scientists of the 19th and 20th centuries in the humanities and natural sciences, the clustering of professional trajectories of gifted individuals was carried out. Neural network analysis based on self-organizing Kohonen maps was used as a clustering method. The professional trajectories were formed within the framework of the behavioral model of the linear-stage approach to studying life cycles. Within this approach, career and professional self-realization are understood as a sequence of evolutionary stages fixed in their order of occurrence. Each stage was encoded, and the biographies were transformed into a vector system. In turn, the task of clustering consisted in dividing a hundred vectors into typical groups with several real-valued coordinates. The criteria for the quality of clustering were the minimum sum of quantization errors and the silhouette coefficient. As a result of the study, seven professional trajectories of gifted individuals were identified and interpreted. The analysis of trajectories was carried out from the point of view of the speed of success (average age of success) and those factors and conditions of the life path that could affect either rapid or slow achievement of professional goals and self-realization. This example demonstrates the possibilities and limitations of using neural network analysis for solving similar research tasks, especially when working with complex cluster forms and finding their optimal number.","PeriodicalId":271863,"journal":{"name":"Sociology: methodology, methods, mathematical modeling (Sociology: 4M)","volume":"23 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141818391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The steady increase in the popularity of social media as a means of communication actualizes methodological issues related to processing of short texts with less semantic context than large corpora, which are widely used for training and testing machine learning models for textual data. Topic modeling, an unsupervised machine learning technique aimed at aggregating texts into topic clusters, has many academic and practical applications where information on true groupings of texts is not available. However, the performance of topic modeling algorithms may be limited by requirement of a sufficient semantic context for a high-quality numerical representation of a unit of text, which may not be derived effectively from a short document. This paper is dedicated to discussing 6 different approaches to topic modeling, comparing their performance on a set of Russian-language comments on TikTok and formally evaluating their performance based on speed and coherence of the resulting topics.
{"title":"Topic modeling for short texts: comparative analysis of algorithms","authors":"Vasilisa Vashchenko","doi":"10.19181/4m.2023.32.1.2","DOIUrl":"https://doi.org/10.19181/4m.2023.32.1.2","url":null,"abstract":"The steady increase in the popularity of social media as a means of communication actualizes methodological issues related to processing of short texts with less semantic context than large corpora, which are widely used for training and testing machine learning models for textual data. Topic modeling, an unsupervised machine learning technique aimed at aggregating texts into topic clusters, has many academic and practical applications where information on true groupings of texts is not available. However, the performance of topic modeling algorithms may be limited by requirement of a sufficient semantic context for a high-quality numerical representation of a unit of text, which may not be derived effectively from a short document. This paper is dedicated to discussing 6 different approaches to topic modeling, comparing their performance on a set of Russian-language comments on TikTok and formally evaluating their performance based on speed and coherence of the resulting topics.","PeriodicalId":271863,"journal":{"name":"Sociology: methodology, methods, mathematical modeling (Sociology: 4M)","volume":"98 26","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141818546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The main purpose of current study is to review the main existing methodological approaches to the integration of survey data and digital traces that are used in sociological research. The paper examines key arguments in the current methodological discussion about the place of big digital data in contemporary social science research. The authors make an attempt to scrutinize the practice of integrating survey data and digital traces through the concept of “reactive – nonreactive” measurement. The possible functions of digital traces in the design of the study are indicated (on the example of social media data). On the example of three research areas (the study of media consumption, media effects and electoral behavior) general methodological principles for integrating data of different nature are demonstrated and possible prospects for the development of these approaches is described. The article discusses a wide range of methodological issues: problems of the data linking validity; potential threats to the validity of digital traces; opportunities to improve survey questionnaire, to enrich data, to search for new valid indicators of socio-political processes and to provide cross-validation of research results. The current practices of integrating administrative data are considered as well.
{"title":"Integration of survey data and digital footprints: an overview of the main methodological approaches","authors":"Anastasia Saponova, S. Kulikov","doi":"10.19181/4m.2021.53.4","DOIUrl":"https://doi.org/10.19181/4m.2021.53.4","url":null,"abstract":"The main purpose of current study is to review the main existing methodological approaches to the integration of survey data and digital traces that are used in sociological research. The paper examines key arguments in the current methodological discussion about the place of big digital data in contemporary social science research. The authors make an attempt to scrutinize the practice of integrating survey data and digital traces through the concept of “reactive – nonreactive” measurement. The possible functions of digital traces in the design of the study are indicated (on the example of social media data). On the example of three research areas (the study of media consumption, media effects and electoral behavior) general methodological principles for integrating data of different nature are demonstrated and possible prospects for the development of these approaches is described. The article discusses a wide range of methodological issues: problems of the data linking validity; potential threats to the validity of digital traces; opportunities to improve survey questionnaire, to enrich data, to search for new valid indicators of socio-political processes and to provide cross-validation of research results. The current practices of integrating administrative data are considered as well.","PeriodicalId":271863,"journal":{"name":"Sociology: methodology, methods, mathematical modeling (Sociology: 4M)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115168490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article discusses the place of qualitative network analysis in the strategy of mixing methods in the social sciences. We conducted a systematic review of the literature that allowed us to demonstrate examples of the use of qualitative network analysis in empirical research. There are four ways of analyzing qualitative data in network mixed studies: qualitative analysis of qualitative data, quantitative analysis of qualitative data, quantitative and qualitative analysis of qualitative data and quantitative and qualitative analysis of qualitative and quantitative data. Currently, there is a lack of a single definition of the methodology of qualitative network analysis and consensus on its implementation in practice. The main possibilities of qualitative network analysis are discussed in the article. At the level of the research object, qualitative network analysis studies the personal networks of individuals’ relationships, and also make easier an access to hard-to-reach groups of respondents. At the level of the subject of research, qualitative network analysis allows us to study the deep meanings of relationships in the network and the contexts of interaction, to describe and understand networks from the inside and outside, to focus on the activities of actors and their strategies for building a network, as well as to identify the temporality of relationships in the network. The article discusses a position that questions the existence of qualitative network analysis as an independent methodology.
{"title":"Qualitative network analysis in the strategy of mixing methods in the social sciences: a systematic literature review","authors":"Aryuna Kim","doi":"10.19181/4m.2021.53.3","DOIUrl":"https://doi.org/10.19181/4m.2021.53.3","url":null,"abstract":"This article discusses the place of qualitative network analysis in the strategy of mixing methods in the social sciences. We conducted a systematic review of the literature that allowed us to demonstrate examples of the use of qualitative network analysis in empirical research. There are four ways of analyzing qualitative data in network mixed studies: qualitative analysis of qualitative data, quantitative analysis of qualitative data, quantitative and qualitative analysis of qualitative data and quantitative and qualitative analysis of qualitative and quantitative data. Currently, there is a lack of a single definition of the methodology of qualitative network analysis and consensus on its implementation in practice. The main possibilities of qualitative network analysis are discussed in the article. At the level of the research object, qualitative network analysis studies the personal networks of individuals’ relationships, and also make easier an access to hard-to-reach groups of respondents. At the level of the subject of research, qualitative network analysis allows us to study the deep meanings of relationships in the network and the contexts of interaction, to describe and understand networks from the inside and outside, to focus on the activities of actors and their strategies for building a network, as well as to identify the temporality of relationships in the network. The article discusses a position that questions the existence of qualitative network analysis as an independent methodology.","PeriodicalId":271863,"journal":{"name":"Sociology: methodology, methods, mathematical modeling (Sociology: 4M)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129815995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Consumer Sentiments Index (CSI) reflects views of the population of Russia on the economic and financial policy of the country and contributes to the understanding of recessive changes in the economy. Current methodological approach singles out inflation, exchange rate, unemployment, intensity of economic events coverage in mass media as the primary factors that guide consumers in their assessments when “rational” signals arise. The article pays attention to the peculiar features of using the ARDL approach in sociological research based on the example of assessing non-economic factors on CSI in 2010-2017, including “socially significant” factors such as mass media. In autoregressive models with distributed lag (ARDL) it is possible to use the “non-economic” indicators that are difficult to include in classical vector autoregressive models (VAR). The article shows that ARDL modeling improves the interpretation of models in the presence of mixed series, and a two-month lag in the news intensity can demonstrate a decrease in consumer sentiments. The approach used in the current study allowed to identify episodes of desynchronization of the dynamics of macro indicators since the 2010s, which, on the one hand, indicates actual changes in the corresponding indicators, and on the other hand, brings more certainty to people’s understanding of the current situation in the economy and opportunities for making large purchases. Additionally, the article examines the methodological and analytical benefits of the CSI data and describe the specifics of including various “sociological” parameters and indicators into the analysis.
{"title":"Peculiarities of ARDL modeling in sociological time series analysis (the case of economic news in the dynamics of CSI in 2010–2017)","authors":"Stanislav Pashkov","doi":"10.19181/4m.2021.53.2","DOIUrl":"https://doi.org/10.19181/4m.2021.53.2","url":null,"abstract":"The Consumer Sentiments Index (CSI) reflects views of the population of Russia on the economic and financial policy of the country and contributes to the understanding of recessive changes in the economy. Current methodological approach singles out inflation, exchange rate, unemployment, intensity of economic events coverage in mass media as the primary factors that guide consumers in their assessments when “rational” signals arise. The article pays attention to the peculiar features of using the ARDL approach in sociological research based on the example of assessing non-economic factors on CSI in 2010-2017, including “socially significant” factors such as mass media. In autoregressive models with distributed lag (ARDL) it is possible to use the “non-economic” indicators that are difficult to include in classical vector autoregressive models (VAR). The article shows that ARDL modeling improves the interpretation of models in the presence of mixed series, and a two-month lag in the news intensity can demonstrate a decrease in consumer sentiments. The approach used in the current study allowed to identify episodes of desynchronization of the dynamics of macro indicators since the 2010s, which, on the one hand, indicates actual changes in the corresponding indicators, and on the other hand, brings more certainty to people’s understanding of the current situation in the economy and opportunities for making large purchases. Additionally, the article examines the methodological and analytical benefits of the CSI data and describe the specifics of including various “sociological” parameters and indicators into the analysis.","PeriodicalId":271863,"journal":{"name":"Sociology: methodology, methods, mathematical modeling (Sociology: 4M)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128120105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The article presents the experience of using Yandex Toloka crowdsourcing platform to recruit respondents for an online survey. Analyzing methodological publications on a similar foreign platform Amazon Mechanical Turk we put forward hypotheses about the data quality obtained via Toloka in comparison with the results collected using other convenience sample types –online panels and recruitment of respondents through social networks. Additionally, only based on the Toloka data, we assessed the indicator of respondent’s attentiveness. The main conclusion is that Toloka allows to recruit respondents quickly and at low cost, and the results are comparable in terms of quality to those obtained by other methods. In particular, respondents from Toloka almost always complete the survey, fill out questionnaires faster than other types of respondents, but less often than participants of the online panel have a tendency to “straightline” (i.e., give the same answers in a tabular question), just as often as social media respondents give answers to the open-ended question (but less frequently than online panel participants), although their responses are shorter. Only 36% of the respondents passed the attention check question, attentive participants had a longer questionnaire complete time and were less likely to be straightliners. The increase of reward did not increase the proportion of attentive respondents, but decreased the questionnaire filling out speed, increased the number of answers to the open question, and reduced the proportion of straightliners.
{"title":"Toloka platform as a source of online survey participants: an experience of assessing data quality","authors":"K. Gavrilov","doi":"10.19181/4m.2021.53.5","DOIUrl":"https://doi.org/10.19181/4m.2021.53.5","url":null,"abstract":"The article presents the experience of using Yandex Toloka crowdsourcing platform to recruit respondents for an online survey. Analyzing methodological publications on a similar foreign platform Amazon Mechanical Turk we put forward hypotheses about the data quality obtained via Toloka in comparison with the results collected using other convenience sample types –online panels and recruitment of respondents through social networks. Additionally, only based on the Toloka data, we assessed the indicator of respondent’s attentiveness. The main conclusion is that Toloka allows to recruit respondents quickly and at low cost, and the results are comparable in terms of quality to those obtained by other methods. In particular, respondents from Toloka almost always complete the survey, fill out questionnaires faster than other types of respondents, but less often than participants of the online panel have a tendency to “straightline” (i.e., give the same answers in a tabular question), just as often as social media respondents give answers to the open-ended question (but less frequently than online panel participants), although their responses are shorter. Only 36% of the respondents passed the attention check question, attentive participants had a longer questionnaire complete time and were less likely to be straightliners. The increase of reward did not increase the proportion of attentive respondents, but decreased the questionnaire filling out speed, increased the number of answers to the open question, and reduced the proportion of straightliners.","PeriodicalId":271863,"journal":{"name":"Sociology: methodology, methods, mathematical modeling (Sociology: 4M)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121511879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The article shows the possibilities of using the blockmodeling technique as a method of clustering network data in sociological research by conducting the secondary analysis of data related to structure of the community of St. Petersburg sociologists. The methodology of blockmodeling, data and results of the original research are briefly described. Using the blockmodeling algorithm of the CONCOR program, colleagues initially identified three clusters – “West End”, “East End” and “North End”, which differed in affiliation to organizations, publication strategies and orientation of scientists to the Western and domestic scientific community. The article describes the procedure of the blockmodeling algorithm used for secondary analysis, based on an indirect approach and hierarchical clustering. Using this method, we discovered a community structure similar to that found in the original study, but also, we had the possibility to take a deeper look at the selected groups, referring their structures to the “core-periphery” type in a complex form. The intersection of clusters obtained by different methods makes it possible to cross-validate the results of the analysis carried out by two independent research teams. The work can serve as a guide for researchers from other fields dealing with the problems of identifying related subgroups, since the described blockmodeling algorithm is universal and does not depend on the specifics of the subject.
{"title":"Blockmodeling for analysis of social structures: studying the structure of St. Petersburg community of sociologists","authors":"Aryuna Kim, D. Maltseva, T. Shcheglova","doi":"10.19181/4m.2021.53.1","DOIUrl":"https://doi.org/10.19181/4m.2021.53.1","url":null,"abstract":"The article shows the possibilities of using the blockmodeling technique as a method of clustering network data in sociological research by conducting the secondary analysis of data related to structure of the community of St. Petersburg sociologists. The methodology of blockmodeling, data and results of the original research are briefly described. Using the blockmodeling algorithm of the CONCOR program, colleagues initially identified three clusters – “West End”, “East End” and “North End”, which differed in affiliation to organizations, publication strategies and orientation of scientists to the Western and domestic scientific community. The article describes the procedure of the blockmodeling algorithm used for secondary analysis, based on an indirect approach and hierarchical clustering. Using this method, we discovered a community structure similar to that found in the original study, but also, we had the possibility to take a deeper look at the selected groups, referring their structures to the “core-periphery” type in a complex form. The intersection of clusters obtained by different methods makes it possible to cross-validate the results of the analysis carried out by two independent research teams. The work can serve as a guide for researchers from other fields dealing with the problems of identifying related subgroups, since the described blockmodeling algorithm is universal and does not depend on the specifics of the subject.","PeriodicalId":271863,"journal":{"name":"Sociology: methodology, methods, mathematical modeling (Sociology: 4M)","volume":"198 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134124563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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