Журнал: Социология: методология, методы, математическое моделирование (Социология:4М)

Сапонова А. В., Куликов С. П.

Интеграция опросных данных и цифровых следов: обзор основных методологических подходов

Полный текст

Ссылка при цитировании:

Рубрика:

Аннотация:

Литература:

• Golder S.A., Macy M.W. Digital Footprints: Opportunities and Challenges for Online Social Research // Annual Review of Sociology. 2014. Vol. 40, No. 1. P. 129–152. DOI: 10.1146/annurev-soc-071913-043145
• Salganik M.J., Watts D.J. Web-Based Experiments for the Study of Collective Social Dynamics in Cultural Markets // Topics in Cognitive Science. 2009. Vol. 1, No. 3. P. 439–468. DOI: 10.1111/j.1756-8765.2009.01030.x
• Anderson C. The End of Theory: The Data Deluge Makes the Scientific Method Obsolete // Wired. 23.06.2008. URL: https://www.wired.com/2008/06/pbtheory/ (дата обращения: 17.04.2022).
• Майер-Шенбергер В., Кукьер К. Большие данные. Революция, которая изменит то, как мы живем, работаем и мыслим / Пер. с англ. И. Гайдюк. М.: Манн, Иванов и Фербер, 2014. 240 с.
• Boyd d., Crawford K. Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon // Information, communication & society. 2012. Vol. 15, No. 5. P. 662–679. DOI: 10.1080/1369118X.2012.678878
• Губа К.С. Большие данные в социологии: Новые данные, новая социология? // Социологическое обозрение. 2018. № 17 (1). С. 213–236. DOI: 10.17323/1728-192X-2018-1-213-236
• McFarland D. A., Lewis K., Goldberg A. Sociology in the era of big data: The ascent of forensic social science // The American Sociologist. 2016. Vol. 47, No. 1. P. 12–35. DOI: 10.1007/s12108-015-9291-8
• Burrows R., Savage M. After the crisis? Big Data and the methodological challenges of empirical sociology // Big Data & Society. 2014. Vol. 1, No. 1. DOI: 10.1177/0038038507080443
• Thrift N. Knowing Capitalism. London: SAGE Publications Ltd, 2005. 256 p. DOI: 10.4135/9781446211458
• Gane N. Measure, Value and the Current Crises of Sociology // The Sociological Review. 2011. Vol. 59, No. 2. P. 151–173. DOI: 10.1111/j.1467-954X.2012.02054.x
• Ignatow G. Sociological Theory in the Digital Age. 1st ed. New York: Routledge, 2020. 120 p. DOI: 10.4324/9780429292804
• Kitchin R., Lauriault T.P. Small data in the era of big data // GeoJournal. 2015. Vol. 80, No. 4. P. 463–475. DOI: 10.1007/s10708-014-9601-7
• de Vreese C.H. et al. Linking Survey and Media Content Data: Opportunities, Considerations, and Pitfalls / C.H. de Vreese, M. Boukes, A. Schuck, R. Vliegenthart, L. Bos, Y. Lelkes // Communication Methods and Measures. 2017. Vol. 11, No. 4. P. 221–244. DOI: 10.1080/19312458.2017.1380175
• Stier S. et al. Integrating Survey Data and Digital Trace Data: Key Issues in Developing an Emerging Field / S. Stier, J. Breuer, P. Siegers, K. Thorson // Social Science Computer Review. 2020. Vol. 38, No. 5. P. 503–516. DOI: 10.1177/0894439319843669
• Beninger K. et al. Understanding Society: How people decide whether to give consent to link their administrative and survey data / K. Beninger, A. Digby, G. Dillon, J. MacGregor // Understanding Society Working Paper Series. 2017. No. 13. 65 p.
• Webb E.J. et al. Unobtrusive measures: nonreactive research in the social sciences / E.J. Webb, D.T. Campbell, R.D. Schwartz, L. Sechrest. Chicago: Rand McNally, 1966. 220 p.
• Bouchard Jr T.J. Unobtrusive Measures: An Inventory of Uses // Sociological Methods & Research. 1976. Vol. 4, No. 3. P. 267–300. DOI: 10.1177/004912417600400301
• Hill A.D., White M.A., Wallace J.C. Unobtrusive measurement of psychological constructs in organizational research // Organizational Psychology Review. 2014. Vol. 4, No. 2. P. 148–174. DOI: 10.1177/2041386613505613
• Lee R.M. Unobtrusive Methods // Handbook of Research Methods in Health Social Sciences / Ed. by P. Liamputtong. Wiesbaden: Springer VS, 2019. P. 491–507. DOI: 10.1007/978-981-10-5251-4_85
• Девятко И.Ф. Инструментарий онлайн-исследований: попытка каталогизации // Онлайн-исследования в России 3.0 / Отв. ред. И.Ф. Девятко, А.В. Шашкин, С.Г. Давыдов; науч. ред. И.Ф. Девятко. М.: OMI RUSSIA, 2012. С. 17–30.
• Дудина В.И. Цифровые данные – потенциал развития социологического знания // Социологические исследования. 2016. № 9. С. 21–30.
• Lee R.M. Unobtrusive Measures in Social Research. Philadelphia, PA: Open University Press. 2000. 192 p.
• Kalokyri V. et al. Integration and Exploration of Connected Personal Digital Traces / V. Kalokyri, A. Borgida, A. Marian, D. Vianna // Proceedings of the ExploreDB’17. Chicago, IL: ACM, 2017. DOI: 10.1145/3077331.3077337
• Araujo T., Neijens P. Unobtrusive Measures for Media Research // The International Encyclopedia of Media Psychology. 1st ed. / Ed by J. Bulck. Hoboken, NJ: Wiley Blackwell, 2020. P. 1–7. DOI: 10.1002/9781119011071.iemp0049
• Девятко И.Ф. Новые данные, новая статистика: от кризиса воспроизводимости к новым требованиям к анализу и представлению данных в социальных науках // Социологические исследования. 2018. № 12. С. 30–38.
• Федорова А.А., Николаенко Г.А. Нереактивная стратегия: применимость незаметных методов сбора социологической информации в условиях web 2.0 на примере цифровой этнографии и big data // Социология власти. 2017. T. 29, № 4. С. 36–54.
• Savage M., Burrows R. The Coming Crisis of Empirical Sociology // Sociology. 2007. Vol. 41, No. 5. P. 885–899. DOI: 10.1177/0038038507080443
• Couper M.P. Is the Sky Falling? New Technology, Changing Media, and the Future of Surveys // Survey Research Methods. European Survey Research Association. 2013. Vol. 7, No. 3. P. 145–156. DOI: 10.18148/SRM/2013.V7I3.5751
• Beaulieu A. Sociable hyperlinks: an ethnographic approach to connectivity // In Virtual Methods: issues in social research on the Internet / Ed by C. Hine. Oxford: Berg, 2005. P. 183–198.
• Hine C. Internet Research and Unobtrusive Methods // Social Research Update. 2011. No. 61. P. 1–4.
• Dirksen V., Huizing A., Smit B. ‘Piling on layers of understanding’: the use of connective ethnography for the study of (online) work practices // New Media & Society. 2010. Vol. 12, No. 7. P. 1045–1063. DOI: 10.1177/1461444809341437
• De Heer W., De Leeuw E. Trends in household survey nonresponse: A longitudinal and international comparison // Survey nonresponse. 2002. Vol. 41, P. 41–54.
• Nonresponse in Social Science Surveys: A Research Agenda / Ed. By R. Tourangeau, T.J. Plewes. Washington, DC: The National Academies Press, 2013. 151 p.
• ?ehovin G., Bosnjak M., Lozar Manfreda K. Item Nonresponse in Web Versus Other Survey Modes: A Systematic Review and Meta-Analysis // Social Science Computer Review. 2022. DOI: 10.1177/08944393211056229
• Lazer D.et al. Computational social science / D. Lazer, A. Pentland, L. Adamic, S. Aral, A.-L. Barab?si, D. Brewer, N. Christakis, N. Contractor, J. Fowler, M. Gutmann, T. Jebara, G. King, M. Macy, D. Roy, M. Van Alstyne // Science. 2009. Vol. 323, No. 5915. P. 721-723. DOI: 10.1126/science.1167742
• Keeter S., Christian L. A comparison of results from surveys by the Pew Research Center and Google Consumer Surveys. Washington, DC: Pew Research Center, 2012. 30 p.
• Graham M., Hale S.A., Gaffney D. Where in the world are you? Geolocation and language identification in Twitter // The Professional Geographer. 2014. Vol. 66, No. 4. P. 568–578. DOI: 10.1080/00330124.2014.907699
• Conrad F.G. et al. Social Media as an Alternative to Surveys of Opinions About the Economy / F.G. Conrad, J. A. Gagnon-Bartsch, R. A. Ferg, M. F. Schober, J. Pasek, E. Hou // Social Science Computer Review. 2021. Vol. 39, No. 4. P. 489–508. DOI: 10.1177/0894439319875692
• Schulz A. et al. A Multi-Indicator Approach for Geolocalization of Tweets / A. Schulz, A. Hadjakos, H. Paulheim, J. Nachtwey, M. M?hlh?user // ICWSM. 2021. Vol. 7, No. 1. P. 573–582.
• Stock K. Mining location from social media: A systematic review // Computers, Environment and Urban Systems. 2018. Vol. 71. P. 209–240. DOI: 10.1016/j.compenvurbsys.2018.05.007
• Chen Q., Poorthuis A. Identifying home locations in human mobility data: an open-source R package for comparison and reproducibility // International Journal of Geographical Information Science. 2021. Vol. 35, No. 7. P. 1425–1448. DOI: 10.1080/13658816.2021.1887489
• Campbell D.T., Fiske D.W. Convergent and discriminant validation by the multitrait-multimethod matrix // Psychological bulletin. 1959. Vol. 56, No. 2. Р. 81–105. DOI: 10.1037/h0046016
• Bouchard Jr T.J. Field research methods: Interviewing, questionnaires, participant observation, systematic observation, unobtrusive measures // Handbook of industrial and organizational psychology. Vol. 1. / Ed. by M.D. Dunnette. Chicago: Rand McNally, 1976. P. 363–413.
• Zeller R.A., Carmines E.G. Measurement in the social sciences: The link between theory and data. Cambridge; New York: Cambridge University Press, 1980. 198 p.
• Pasek J. et al. Who’s Tweeting About the President? What Big Survey Data Can Tell Us About Digital Traces? / J. Pasek, C. A. McClain, F. Newport, S. Marken // Social Science Computer Review. 2020. Vol. 38, No. 5. P. 633–650. DOI: 10.1177/0894439318822007
• Климова А.М., Куликов С.П., Чмель К.Ш. Роль социальных медиа в формировании регионального экологического протеста в России // Мониторинг общественного мнения: Экономические и социальные перемены. 2021. № 6. С. 28–52. DOI: 10.14515/monitoring.2021.6.2024
• Shlomo N. Overview of Data Linkage Methods for Policy Design and Evaluation // Data-Driven Policy Impact Evaluation / Ed by N. Crato, P. Paruolo. Cham: Springer International Publishing, 2019. P. 47–65
• Quinlan S. et al. ‘Show me the money and the party!’ – variation in Facebook and Twitter adoption by politicians / S. Quinlan, T. Gummer, J. Ro?mann, C. Wolf // Information, Communication & Society. 2018. Vol. 21, No. 8. P. 1031–1049. DOI: 10.1080/1369118X.2017.1301521
• Karlsen R., Enjolras B. Styles of Social Media Campaigning and Influence in a Hybrid Political Communication System: Linking Candidate Survey Data with Twitter Data // The International Journal of Press/Politics. 2016. Vol. 21, No. 3. P. 338–357. DOI: 10.1177/1940161216645335
• Schober M. F. et al. Social Media Analyses for Social Measurement / M.F. Schober, J. Pasek, L. Guggenheim [et al.] // Public Opinion Quarterly. 2016. Vol. 80, No. 1. P. 180–211. DOI: 10.1093/poq/nfv048
• Barbera P. et al. Who Leads? Who Follows? Measuring Issue Attention and Agenda Setting by Legislators and the Mass Public Using Social Media Data / P. Barbera, A. Casas, J. Nagler [et al.]. // American Political Science Review. 2019. Vol. 113, No. 4. P. 883–901. DOI: 10.1017/S0003055419000352
• Девятко И.Ф. Диагностическая процедура в социологии: Очерк истории и теории. М.: Наука, 1993. 175 с.
• Iannelli L. et al. Facebook Digital Traces for Survey Research: Assessing the Efficiency and Effectiveness of a Facebook Ad–Based Procedure for Recruiting Online Survey Respondents in Niche and Difficult-to-Reach Populations / L. Iannelli, F. Giglietto, L. Rossi, E. Zurovac // Social Science Computer Review. 2020. Vol. 38, No. 4. P. 462–476. DOI: 10.1177/0894439318816638
• Kosinski M., Stillwell D., Graepel T. Private traits and attributes are predictable from digital records of human behavior // Proceedings of the national academy of sciences. 2013. Vol. 110, No. 15. P. 5802–5805. DOI: 10.1073/pnas.1218772110
• Девятко И.Ф. От «виртуальной лаборатории» до «социального телескопа»: метафоры тематических и методологических инноваций в онлайн-исследованиях // Онлайн-исследования в России: тенденции и перспективы / Под общ. ред. А.В. Шашкина, И.Ф. Девятко, С.Г. Давыдова. М.: Онлайн маркет интеллидженс, 2016. С. 19–33.
• Afriat H. et al. “This is capitalism. It is not illegal”: Users’ attitudes toward institutional privacy following the Cambridge Analytica scandal / H. Afriat, S. Dvir-Gvirsman, K. Tsuriel, L. Ivan // The Information Society. 2021. Vol. 37, No. 2. P. 115–127. DOI: 10.1080/01972243.2020.1870596
• Diaz F. et al. Online and Social Media Data As an Imperfect Continuous Panel Survey / F. Diaz, M. Gamon, J. M. Hofman, E. K?c?man, D. Rothschild // PLoS ONE 2016. Vol. 11, No. 1. P. e0145406. DOI: 10.1371/journal.pone.0145406
• Бызов А.А. Интеллектуальный анализ текстов в социальных науках // Социология: методология, методы, математическое моделирование (Социология: 4М). 2019. № 49. С. 131–160.
• Lazer D. et al. The Parable of Google Flu: Traps in Big Data Analysis / D. Lazer, R. Kennedy, G. King, A. Vespignani // Science. 2014. Vol. 343, No. 6176. P. 1203–1205. DOI: 10.1126/science.1248506
• Hofstra B. et al. B. Sources of Segregation in Social Networks: A Novel Approach Using Facebook / B. Hofstra, R. Corten, F. van Tubergen, N.B. Ellison // American Sociological Review. 2017. Vol. 82, No. 3. P. 625–656. DOI: 10.1177/0003122417705656
• Henderson M. et al. Measuring Twitter Use: Validating Survey-Based Measures / M. Henderson, K. Jiang, M. Johnson, L. Porter // Social Science Computer Review. 2021. Vol. 39, No. 6. P. 1121–1141. DOI: 10.1177/0894439319896244
• Vraga E.K., Tully M. Who Is Exposed to News? It Depends on How You Measure: Examining Self-Reported Versus Behavioral News Exposure Measures // Social Science Computer Review. 2020. Vol. 38, No. 5. P. 550–566. DOI: 10.1177/0894439318812050
• Haenschen K. Self-Reported Versus Digitally Recorded: Measuring Political Activity on Facebook // Social Science Computer Review. 2020. Vol. 38, No. 5. P. 567–583. DOI: 10.1177/0894439318813586
• J?rgens P., Stark B., Magin M. Two Half-Truths Make a Whole? On Bias in Self-Reports and Tracking Data // Social Science Computer Review. 2020. Vol. 38, No. 5. P. 600–615. DOI: 10.1177/0894439319831643
• Shin J. How Do Partisans Consume News on Social Media? A Comparison of Self-Reports With Digital Trace Measures Among Twitter Users // Social Media + Society. 2020. Vol. 6, No. 4. DOI: 10.1177/2056305120981039
• Hopp T. et al. Correlating Self-Report and Trace Data Measures of Incivility: A Proof of Concept / T. Hopp, C. J. Vargo, L. Dixon, N. Thain // Social Science Computer Review. 2020. Vol. 38, No. 5. P. 584–599. DOI: 10.1177/0894439318814241
• Junco R. Comparing Actual and Self-Reported Measures of Facebook Use // Computers in Human Behavior. 2013. Vol. 29, No. 3. P. 626–631. DOI: 10.1016/j.chb.2012.11.007
• Hessler J. Peoplemeter Technologies and the Biometric Turn in Audience Measurement // Television & New Media. 2021. Vol. 22, No. 4. P. 400–419. DOI: 10.1177/1527476419879415
• Parry D.A. et al. A systematic review and meta-analysis of discrepancies between logged and self-reported digital media use / D.A. Parry, B.I. Davidson, C.J.R. Sewall, J.T. Fisher, H. Mieczkowski, D.S. Quintana // Nature Human Behaviour. 2021. Vol. 5, No. 11. P. 1535–1547. DOI: 10.1038/s41562-021-01117-5
• Greenberg B.S.et al. Comparing Survey and Diary Measures of Internet and Traditional Media Use / B.S. Greenberg, M.S. Eastin, P. Skalski, L. Cooper, M. Levy, K. Lachlan // Communication Reports. 2005. Vol. 18, No. 1–2. DOI: 10.1080/08934210500084164
• Araujo T. et al. ‘How Much Time Do You Spend Online? Understanding and Improving the Accuracy of Self-Reported Measures of Internet Use / T. Araujo, A. Wonneberger, P. Neijens, C. de Vreese // Communication Methods and Measures. 2017. Vol. 11, No. 3. P.173–90. DOI: 10.1002/9781119011071.iemp0049
• Wonneberger A., Irazoqui M. Tell it like it is? Inaccuracies of selfreported TV exposure in comparison to people-meter data // Annual Conference of the International Communication Association. London, UK. 17–21 June 2013.
• Prior M. The Immensely Inflated News Audience: Assessing Bias in Self-Reported News Exposure // Public Opinion Quarterly. 2009. Vol. 73, No. 1. P. 130–143. DOI: 10.1093/poq/nfp002
• Boase J., Ling R. Measuring Mobile Phone Use: Self-Report Versus Log Data // Journal of Computer-Mediated Communication. 2013. Vol. 18, No. 4. P. 508–519. DOI: 10.1111/jcc4.12021
• Ettema J.S. Explaining information system use with system-monitored vs. self-reported use measures // Public Opinion Quarterly. 1985. Vol. 49, No. 3. P. 381–387. DOI: 10.1086/268935
• van der Voort T.H.A., Vooijs M.W. Validity of children’s direct estimates of time spent television viewing // Journal of Broadcasting & Electronic Media. 1990. Vol. 34, No. 1. P. 93–99. DOI: 10.1080/08838159009386729
• Chang L.C., Krosnick J.A. Measuring the frequency of regular behaviors: Comparing the “typical week” to the “past week” // Sociological Methodology. 2003. Vol. 33, No. 1. P. 55–80. DOI: 10.1111/j.0081-1750.2003.t01-1-00127.x
• Yanovitzky I. Effect of Call-In Political Talk Radio Shows on Their Audiences: Evidence from a Multi-Wave Panel Analysis // International Journal of Public Opinion Research. 2001. Vol. 13, No. 4. P. 377–397. DOI: 10.1093/ijpor/13.4.377
• de Vreese C., Semetko H.A. News matters: Influences on the vote in the Danish 2000 euro referendum campaign // European Journal of Political Research. 2004. Vol. 43, No. 5. P. 699–722. DOI: 10.1111/j.0304-4130.2004.00171.x
• van Spanje J., de Vreese C. Europhile Media and Eurosceptic Voting: Effects of News Media Coverage on Eurosceptic Voting in the 2009 European Parliamentary Elections // Political Communication. 2014. Vol. 31, No. 2. P. 325–354. DOI: 10.1080/10584609.2013.828137
• McCombs M.E., Shaw D.L. The Agenda-Setting Function of Mass Media // Public Opinion Quarterly. 1972. Vol. 36, No. 2. P. 176–187. DOI: 10.1086/267990
• Geers S., Bos L. Priming Issues, Party Visibility, and Party Evaluations: The Impact on Vote Switching // Political Communication. 2017. Vol. 34, No. 3. P. 344–366. DOI: 10.1080/10584609.2016.1201179
• Hopmann D.N. et al. Effects of Election News Coverage: How Visibility and Tone Influence Party Choice / D.N. Hopmann, R. Vliegenthart, C.H. De Vreese, E. Alb?k // Political Communication. 2010. Vol. 27, No. 4. P. 389–405. DOI: 10.1080/10584609.2010.516798
• Matthes J. Exposure to Counterattitudinal News Coverage and the Timing of Voting Decisions // Communication Research. 2012. Vol. 39, No. 2. P. 147–169. DOI: 10.1177/0093650211402322
• Mellon J., Prosser C. Twitter and Facebook are not representative of the general population: Political attitudes and demographics of British social media users // Research & Politics. 2017. Vol. 4, No. 3. DOI: 10.1177/2053168017720008
• Stier S. et al. Election Campaigning on Social Media: Politicians, Audiences, and the Mediation of Political Communication on Facebook and Twitter / S. Stier, A. Bleier, H. Lietz, M. Strohmaier // Political Communication. 2018. Vol. 35, No. 1. P. 50–74. DOI: 10.1080/10584609.2017.1334728
• Beauchamp N. Predicting and Interpolating State?Level Polls Using Twitter Textual Data // American Journal of Political Science. 2017. Vol. 61, No. 2. P. 490–503. DOI: 10.1111/ajps.12274
• O’Connor B. et al. From tweets to polls: Linking text sentiment to public opinion time series / B. O’Connor, R. Balasubramanyan, B. R. Routledge, N.A. Smith // Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media. Palo Alto, CA: AAAI Press, 2010. P. 122–129.
• Olsson T. An indispensable resource: The Internet and young civic engagement // Young Citizens and New Media: Learning for democratic participation. New York: Routledge, 2013. P. 197–214.
• Bennett W.L., Wells C., Freelon D. Communicating citizenship online: Models of civic learning in the youth web sphere // A Report from the Civic Learning Online Project. 2009. 41 p.
• Giglietto F. If Likes Were Votes: An Empirical Study on the 2011 Italian Administrative Elections // SSRN Journal. 7 May 2012. DOI: 10.2139/ssrn.1982736
• MacWilliams M.C. Forecasting Congressional Elections Using Facebook Data // APSC. 2015. Vol. 48, No. 4. P. 579–583. DOI: 10.1017/S1049096515000797
• DiGrazia J. et al. More Tweets, More Votes: Social Media as a Quantitative Indicator of Political Behavior / J. DiGrazia, K. McKelvey, J. Bollen, F. Rojas // PLoS ONE. 2013. Vol. 8, No. 11. DOI: 10.1371/journal.pone.0079449
• Bach R.L. et al. Predicting Voting Behavior Using Digital Trace Data / R.L. Bach, C. Kern, A. Amaya, F. Keusch, F. Kreuter, J. Hecht, J. Heinemann // Social Science Computer Review. 2021. Vol. 39, No. 5. P. 862–883. DOI: 10.1177/0894439319882896
• Jungherr A., J?rgens P., Schoen H. Why the Pirate Party Won the German Election of 2009 or The Trouble with Predictions: A Response to Tumasjan A. Sprenger T.O., Sander P.G., & Welpe I.M. “Predicting Elections with Twitter: What 40 Characters Reveal About Political Sentiment” // Social Science Computer Review. 2012. Vol. 30, No. 2. P. 229–234. DOI: 10.1177/0894439311404119
• Guess A.M. Measure for Measure: An Experimental Test of Online Political Media Exposure // Political Analysis. 2015. Vol. 23, No. 1. P. 59–75. DOI: 10.1093/pan/mpu010
• Vraga E., Bode L., Troller-Renfree S. Beyond Self-Reports: Using Eye Tracking to Measure Topic and Style Differences in Attention to Social Media Content // Communication Methods and Measures. 2016. Vol. 10, No. 2–3. P. 149–164. DOI: 10.1080/19312458.2016.1150443
• Colleoni E., Rozza A., Arvidsson A. Echo Chamber or Public Sphere? Predicting Political Orientation and Measuring Political Homophily in Twitter Using Big Data: Political Homophily on Twitter // Journal of Communication. 2014. Vol. 64, No. 2. P. 317–332. DOI: 10.1111/jcom.12084
• Nelson J.L., Webster J.G. The Myth of Partisan Selective Exposure: A Portrait of the Online Political News Audience // Social Media + Society. 2017. Vol. 3, No. 3. DOI: 10.1177/2056305117729314
• Connelly R. et al. The role of administrative data in the big data revolution in social science research / R. Connelly, C. Playford, V. Gayle, C. Dibben // Social Science Research. 2016. Vol. 59. DOI: 10.1016/j.ssresearch.2016.04.015
• Yoshida Y., Haan M., Schaffer S. Administrative data linkage in Canada: Implications for sociological research // Canadian Review of Sociology. 2022. Vol. 59, No. 2. P. 251–270. DOI: 10.1111/cars.12376
• Harron K. et al. Challenges in administrative data linkage for research / K. Harron, C. Dibben, J. Boyd, A. Hjern, M. Azimaee, M.L. Barreto, H. Goldstein // Big Data & Society. 2017. Vol. 4, No. 2. DOI: 10.1177/2053951717745678
• Choi K.H., Ramaj S., Haan M. Age of the oldest child and internal migration of immigrant families: A study using administrative data from immigrant landing and tax files // Population Space and Place. 2021. Vol. 27, No. 4. DOI: 10.1002/psp.2409
• Rampazzo F. et al. A Framework for Estimating Migrant Stocks Using Digital Traces and Survey Data: An Application in the United Kingdom / F. Rampazzo, J. Bijak, A. Vitali, I. Weber, E. Zagheni // Demography. 2021. Vol. 58, No. 6. P. 2193–218. DOI: 10.1215/00703370-9578562
• Brown J.R. et al. Childhood cross-ethnic exposure predicts political behavior seven decades later: Evidence from linked administrative data / J.R. Brown, R.D. Enos, J. Feigenbaum, S. Mazumder // Science Advances. 2021. Vol. 7, No. 24. DOI: 10.1126/sciadv.abe8432
• Vatsalan D. et al. Privacy-Preserving Record Linkage for Big Data: Current Approaches and Research Challenges / D. Vatsalan, Z. Sehili, P. Christen, E. Rahm // Handbook of Big Data Technologies / Ed. by A.Y. Zomaya, S. Sakr. Cham: Springer International Publishing, 2017. P. 851–895. DOI: 10.1007/978-3-319-49340-4_25
• Dibben C. et al. The data linkage environment / C. Dibben, M. Elliot, H. Gowans, D. Lightfoot // Methodological Developments in Data Linkage. Chapter 3 / Ed. by K. Harron, C. Dibben, H. Goldstein. London: Wiley, 2015. P. 36–62. DOI: 10.1002/9781119072454.ch3

Содержание выпуска

>> Содержание выпуска 2021. № 53.

>> Архив журнала