Pub Date : 2021-11-10DOI: 10.4324/9781003025245-13
R. Andridge, R. Valliant
{"title":"Inference from probability and nonprobability samples","authors":"R. Andridge, R. Valliant","doi":"10.4324/9781003025245-13","DOIUrl":"https://doi.org/10.4324/9781003025245-13","url":null,"abstract":"","PeriodicalId":422456,"journal":{"name":"Handbook of Computational Social Science, Volume 2","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130327998","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}
Pub Date : 2021-11-10DOI: 10.4324/9781003025245-18
Axel Mayer, Christoph Kiefer, Benedikt Langenberg, F. Lemmerich
{"title":"Using subgroup discovery and latent growth curve modeling to identify unusual developmental trajectories","authors":"Axel Mayer, Christoph Kiefer, Benedikt Langenberg, F. Lemmerich","doi":"10.4324/9781003025245-18","DOIUrl":"https://doi.org/10.4324/9781003025245-18","url":null,"abstract":"","PeriodicalId":422456,"journal":{"name":"Handbook of Computational Social Science, Volume 2","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132998024","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}
Pub Date : 2021-11-10DOI: 10.4324/9781003025245-12
Camilla Zallot, Gabriele Paolacci, Jesse J. Chandler, Itay Sisso
{"title":"Crowdsourcing in observational and experimental research","authors":"Camilla Zallot, Gabriele Paolacci, Jesse J. Chandler, Itay Sisso","doi":"10.4324/9781003025245-12","DOIUrl":"https://doi.org/10.4324/9781003025245-12","url":null,"abstract":"","PeriodicalId":422456,"journal":{"name":"Handbook of Computational Social Science, Volume 2","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115368606","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 technological developments of the last decades have created opportunities to efficiently collect data of many individuals over time. While these technologies provide exciting research opportunities, they also provide challenges: datasets collected using these technologies grow increasingly large, or be continuously augmented with new observations. These data streams make the standard computation of well-known estimators inefficient, as computations are repeated each time new data enter. This chapter details online learning, an analysis method that updates parameter estimates instead of re-estimating them to analyze large and/or streaming data. The chapter presents several simple (and exact) examples of the online estimation for independent observations. Additionally, social scientists are often faced with nested data: pupils are nested within schools, or repeated measurements are nested within individuals. Nested data are typically analyzed using multilevel models. Estimating multilevel models, however, can be challenging in data streams: the standard algorithms used to fit these models repeatedly revisit all data points, which becomes infeasible in a data stream context. We present a solution to this problem by introducing the Streaming Expectation Maximization Approximation (SEMA) algorithm for fitting multilevel models online. We end this chapter with a discussion of the methodological challenges that remain.
{"title":"Analyzing data streams for social scientists","authors":"Lianne Ippel, M. Kaptein, J. Vermunt","doi":"10.4324/9781003025245-6","DOIUrl":"https://doi.org/10.4324/9781003025245-6","url":null,"abstract":"The technological developments of the last decades have created opportunities to efficiently collect data of many individuals over time. While these technologies provide exciting research opportunities, they also provide challenges: datasets collected using these technologies grow increasingly large, or be continuously augmented with new observations. These data streams make the standard computation of well-known estimators inefficient, as computations are repeated each time new data enter. This chapter details online learning, an analysis method that updates parameter estimates instead of re-estimating them to analyze large and/or streaming data. The chapter presents several simple (and exact) examples of the online estimation for independent observations. Additionally, social scientists are often faced with nested data: pupils are nested within schools, or repeated measurements are nested within individuals. Nested data are typically analyzed using multilevel models. Estimating multilevel models, however, can be challenging in data streams: the standard algorithms used to fit these models repeatedly revisit all data points, which becomes infeasible in a data stream context. We present a solution to this problem by introducing the Streaming Expectation Maximization Approximation (SEMA) algorithm for fitting multilevel models online. We end this chapter with a discussion of the methodological challenges that remain.","PeriodicalId":422456,"journal":{"name":"Handbook of Computational Social Science, Volume 2","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128420581","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}
Pub Date : 2021-11-10DOI: 10.4324/9781003025245-23
J. Bacher, Andreas Pöge, Knut Wenzig
{"title":"Unsupervised methods","authors":"J. Bacher, Andreas Pöge, Knut Wenzig","doi":"10.4324/9781003025245-23","DOIUrl":"https://doi.org/10.4324/9781003025245-23","url":null,"abstract":"","PeriodicalId":422456,"journal":{"name":"Handbook of Computational Social Science, Volume 2","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132492318","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}
Pub Date : 2021-11-10DOI: 10.4324/9781003025245-11
Indira Sen, Fabian Flöck, Katrin Weller, Bernd Weiss, Claudia Wagner
{"title":"Applying a total error framework for digital traces to social media research","authors":"Indira Sen, Fabian Flöck, Katrin Weller, Bernd Weiss, Claudia Wagner","doi":"10.4324/9781003025245-11","DOIUrl":"https://doi.org/10.4324/9781003025245-11","url":null,"abstract":"","PeriodicalId":422456,"journal":{"name":"Handbook of Computational Social Science, Volume 2","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130462725","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}
{"title":"A primer on probabilistic record linkage","authors":"Ted Enamorado","doi":"10.4324/9781003025245-8","DOIUrl":"https://doi.org/10.4324/9781003025245-8","url":null,"abstract":"","PeriodicalId":422456,"journal":{"name":"Handbook of Computational Social Science, Volume 2","volume":"405 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123551947","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}
Pub Date : 2021-11-10DOI: 10.4324/9781003025245-21
R.D. De Veaux, A. Eck
{"title":"Machine learning methods for computational social science","authors":"R.D. De Veaux, A. Eck","doi":"10.4324/9781003025245-21","DOIUrl":"https://doi.org/10.4324/9781003025245-21","url":null,"abstract":"","PeriodicalId":422456,"journal":{"name":"Handbook of Computational Social Science, Volume 2","volume":"611 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116454663","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}
Pub Date : 2021-11-10DOI: 10.4324/9781003025245-17
Fernando Sancho-Caparrini, Juan-Luis Suárez
{"title":"Agent-based modelling for cultural networks","authors":"Fernando Sancho-Caparrini, Juan-Luis Suárez","doi":"10.4324/9781003025245-17","DOIUrl":"https://doi.org/10.4324/9781003025245-17","url":null,"abstract":"","PeriodicalId":422456,"journal":{"name":"Handbook of Computational Social Science, Volume 2","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133684096","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}
{"title":"Introduction to the Handbook of Computational Social Science","authors":"Uwe Engel, Anabel Quan-Haase, S. Liu, L. Lyberg","doi":"10.4324/9781003025245-1","DOIUrl":"https://doi.org/10.4324/9781003025245-1","url":null,"abstract":"","PeriodicalId":422456,"journal":{"name":"Handbook of Computational Social Science, Volume 2","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124934880","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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