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Die Methoden der sozialwissenschaftlichen Datenerhebung im digitalen Zeitalter

Entwicklungen, Möglichkeiten, Herausforderungen

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Sozialwissenschaftliche Datenerhebung im digitalen Zeitalter

Zusammenfassung

Das angebrochene digitale Zeitalter, treffend charakterisiert durch computers everywhere (Salganik 2018, S. 3), eröffnet den Sozialwissenschaften einzigartige Möglichkeiten des Erkenntnisgewinns und neuartige Forschungsfelder (z.B. Fussey und Roth 2020). Watts (2011, S. 266) sieht darin nicht weniger als „the potential to revolutionize our understanding of ourselfes and how we interact (…)“. Gleichsam sind damit aber auch große Herausforderungen verbunden.

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Literatur

  • Al Baghal, T., & Kelley, J. (2016). The stability of mode preferences: Implications for tailoring in longitudinal surveys. methods, data, analyses, 10, 143–166.

    Google Scholar 

  • Al Baghal, T., Sloan, L., Jessop, C., Williams, M. L., Burnap, P. (2019). Linking Twitter and survey data: The impact of survey mode and demographics on consent rates across three UK studies. Social Science Computer Review, 38, 517–532.

    Google Scholar 

  • Aluja-Banet, T., Daunis-i-Estadella, J., Brunsó, N., & Mompart-Penina, A. (2015). Improving prevalence estimation through data fusion: Methods and validation. BMC Medical Informatics & Decision Making, 15, doi: https://doi.org/10.1186/s12911-015-0169-z.

  • Anderson, C. (2008) The end of theory: The data deluge makes the scientific method obsolete. Wired; 23.08.2008, Link: http://www.wired.com/2008/06/pb-theory (Stand: 26.01.2021).

  • Andreadis, I. (2015). Web surveys optimized for smartphones: Are there differences between computer and smartphone users. methods, data, analyses, 9, 213–228.

    Google Scholar 

  • Antoni, M., & Sakshaug, J. W. (2020). Data linkage. In P. Atkinson, S. Delamont, A. Cernat, J. W. Sakshaug, & R. A. Williams (Hrsg.), SAGE Research Methods Foundations. doi: https://doi.org/10.4135/9781526421036931838.

  • Antoun, C., Conrad, F. G., Couper, M. P., & West, B. T. (2019). Simultaneous estimation of multiple sources of error in a smartphone-based survey. Journal of Survey Statistics & Methodology, 7, 93–117.

    Google Scholar 

  • Athey, S., & Imbens, G. W. (2019). Machine learning methods economists should know about. Annual Review of Economics, 11, 685–725.

    Google Scholar 

  • Bacher, J. (2002). Statistisches Matching. ZA-Informationen, 51, 3–66.

    Google Scholar 

  • Bacher, J., & Prandner, D. (2018). Datenfusion in der sozialwissenschaftlichen Wahlforschung – Begründeter Verzicht oder ungenutzte Chance? Theoretische Vorüberlegungen, Verfahrensüberblick und ein erster Erfahrungsbericht. Austrian Journal of Political Science, 47, 61-76.

    Google Scholar 

  • Balasuriya, L., Wijeratne, S., Doran, D., & Sheth, A. (2016). Finding street gang members on Twitter. Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis & Mining (ASONAM), doi: https://doi.org/10.1109/ASONAM.2016.7752311.

  • Bareinboim, E., Pearl, J. (2016). Causal inference and the data fusion problem. Proceedings of the National Academy of Sciences of the United States of America, 113, 7345–7352.

    Google Scholar 

  • Baur, N., Graeff, P., Braunisch, L., & Schweia, M. (2020). The quality of big data. Development, problems, and possibilities of use of process-generated data in the digital age. Historical Social Research, 45, 209–243.

    Google Scholar 

  • Behr, D., Meitinger, K., Braun, M., & Kaczmirek, L. (2017). Web Probing. Implementing Probing Techniques from Cognitive Interviewing in Web Surveys with the Goal to Assess the Validity of Survey Questions. GESIS Survey Guidelines. Mannheim: GESIS.

    Google Scholar 

  • Beyer, M. A. & Laney, D. (2012). The Importance of “Big Data”. A Definition. Stamford: Gartner Research.

    Google Scholar 

  • Biemer, P. P., de Leeuw, E. D., Eckman, S., Edwards, B., Kreuter, F., Lyberg, L. E., Tucker, N. C., & West, B. T. (Hrsg.) (2017). Total Survey Error in Practice. Hoboken: Wiley.

    Google Scholar 

  • Blank, G. (2017). The digital divide among twitter users and its implications for social research. Social Science Computer Review, 35, 679–697.

    Google Scholar 

  • Boyd, D., & Crawford, K. (2012). Critical questions for big data. Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15, 662–679.

    Google Scholar 

  • Bravo, G., & Farjam, M. (2017). Prospects and challenges for the computational social sciences. Journal of Universial Computer Science, 23, 1057–1069.

    Google Scholar 

  • Breiman, L. (2001). Statistical modeling: the two cultures. Statistical Science, 16, 199–231.

    Google Scholar 

  • Breur, T. (2011). Data analysis across various media: Data fusion, direct marketing, clickstream data and social media. Journal of Direct Data & Digital Marketing Practice, 13, 95–105.

    Google Scholar 

  • Buskirk, T. D., & Andres, C. (2012). Smart surveys for smart phones: Exploring various approaches for conducting online mobile surveys via smartphones. Survey Practice, 5, doi: https://doi.org/10.29115/SP-2012-0001.

  • Cai, L., & Zhu, Y. (2015). The challenges of data quality and data quality assessment in the big data era. Data Science Journal, 14, 1–10.

    Google Scholar 

  • Callegaro, M., & Yang, Y. (2018). The role of surveys in the era of “big data”. In D. L. Vannette, & J. A. Krosnick (Hrsg.), The Palgrave Handbook of Survey Research (S. 175–192). Cham: Palgrave Macmillan.

    Google Scholar 

  • Chang, H.-H. (2015). Psychometrics behind computerized adaptive testing. Psychometrika, 80, 1–20.

    Google Scholar 

  • Chang, R. M., Kauffman, R. J., Kwon, Y. (2014). Understanding the paradigm shift to computational social science in the presence of big data. Decision Support Systems, 63, 67–80.

    Google Scholar 

  • Chatzittheochari, S., Fisher, K., Gilbert, E., Calderwood, L., Huskinson, T., Cleary, A, & Gershuny, J. (2018). Using new technologies for time diary data collection: Instrument design and data quality findings from a mixed-mode pilot study. Social Indicators Research, 137, 379–390.

    Google Scholar 

  • Christen, P. (2012). Data Matching: Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection. Berlin: Springer.

    Google Scholar 

  • Cielebak, J., & Rässler, S. (2018). Data Fusion, Record Linkage und Data Mining. In N. Baur, & J. Blasius (Hrsg.), Handbuch Methoden der empirischen Sozialforschung. Band 1 (S. 423–439). Wiesbaden: Springer VS (2. Auflage).

    Google Scholar 

  • Conte, R., Gilbert, N., Bonelli, G., Cioffi-Revilla, C., Deffuant, G., Kertesz, J., Loreto, V., Moat, S., Nadal, J.-P., Sanchez, A., Nowak, A., Flache, A., San Miguel, M., & Helbing, D. (2012). Manifesto of computational social science. The European Physical Journal Special Topics, 214, 325–346.

    Google Scholar 

  • Couper, M. P. (2005). Technology trends in survey data collection. Social Science Computer Review, 23, 486–501.

    Google Scholar 

  • Couper, M. P. (2017). New developments in survey data collection. Annual Review of Sociology, 43, 121–145.

    Google Scholar 

  • Couper, M. P., Antoun, C., & Mavletova, A. (2017). Mobile web surveys. A total survey error perspective. In P. P. Biemer, E. de Leeuw, S. Eckman, B. Edwards, F. Kreuter, L. E. Lyberg, N. C. Tucker, & B. T West (Hrsg), Total Survey Error in Practice (S. 133–154). Hoboken: New Jersey.

    Google Scholar 

  • Couper, M. P., & Bosnjak, M. (2010). Internet surveys. In P. V. Marsden, & J. D. Right (Hrsg)., Handbook of Survey Methodology (S. . 527–550). Howard House: Emerald.

    Google Scholar 

  • Couper, M. P., Singer, E., & Tourangeau R. (2003). Understanding the effects of audio-CASI on self-reports of sensitive behavior. Public Opinion Quarterly, 67, 385–395.

    Google Scholar 

  • Couper, M. P., Singer, E., & Tourangeau R. (2004). Does voice matter? An interactive voice response (IVR) experiment. Journal of Official Statistics, 20, 551–570.

    Google Scholar 

  • D’Ambrosio, A., Aria, M., & Siciliano, R. (2012). Accurate tree-based missing data imputation and data fusion within the statistical learning paradigm. Journal of Classification, 29, 227–258.

    Google Scholar 

  • de Bruijne, M., & Wijnant, A. (2013). Can mobile web surveys be taken on computers? A discussion on a multi-device survey design. Survey Practice, 6, doi: https://doi.org/10.29115/SP-2013-0019.

  • de Bruijne, M., & Wijnant, A. (2014). Mobile response in web panels. Social Science Computer Review, 32, 728–742.

    Google Scholar 

  • de Leeuw, E. D., & Berzelak, N. (2016). Survey mode or survey modes? In. C. Wolf, D. Joye, T. W. Smith, & Y.-C. Fu (Hrsg.), The SAGE Handbook of Survey Methodology (S. 142–156). Thousand Oaks: Sage.

    Google Scholar 

  • de Leeuw, E. D., Dillman, D. A., & Hox, J. J. (2008). Mixed mode surveys: When and why. In E. D. de Leeuw, J. J. Hox, & D. A. Dillman (Hrsg), International Handbook of Survey Methodology (S. 299–316). New York: Taylor & Francis/Lawrence Erlbaum Associates.

    Google Scholar 

  • de Leeuw, E. D., & Hox, J. J. (2011). Internet surveys as part of a mixed-mode design. In M. Das, P. Ester, L. Kaczmirek (Hrsg.), Social and Behavioral Research and the Internet: Advances in Applied Methods and Research Strategies (S. 45–76). New York: Routledge/Taylor & Francis Group.

    Google Scholar 

  • De Mauro, A., Greco, M., & Grimaldi, M. (2015). What is big data? A consensual definition and a review of key research topics. AIP Conference Proceedings, 1644, 97–104.

    Google Scholar 

  • DESTATIS (2020). Erhebung über die private Nutzung von Informations- und Kommunikationstechnologien. IKT 2019. Qualitätsbericht. Verfügbar unter: https://www.destatis.de/DE/Methoden/Qualitaet/Qualitaetsberichte/Einkommen-Konsum-Lebensbedingungen/ikt-private-haushalte-2019.pdf?__blob=publicationFile (Stand: 26.01.2021)

  • Diekmann, A. (2020). Die Renaissance der „Unobstrusive Methods“ im digitalen Zeitalter. In A. Mays, A. Dingelstedt, V. Hambauer, S. Schlosser, F. Berens, J. Leibold, & J K. Höhne (Hrsg.), Grundlagen – Methoden – Anwendungen in den Sozialwissenschaften. Festschrift für Steffen-M. Kühnel (S. 161-172). Wiesbaden: VS Verlag.

    Google Scholar 

  • Diekmann, A., Jann, B., Przepiorka, W., & Wehrli, S. (2014). Reputation formation and the evolution of cooperation in anonymous online markets. American Sociological Review, 79, 65–85.

    Google Scholar 

  • Dillman, D. A. (2017). The promise and challenge of pushing respondents to the web in mixed-mode surveys. Survey Methodology, 43, 3–30.

    Google Scholar 

  • Dillman, D. A., & Edwards, M. L. (2016). Designing a mixed-mode survey. In. C. Wolf, D. Joye, T. W. Smith, & Y.-c. Fu (Hrsg.), The SAGE Handbook of Survey Methodology (S. 255–268). Thousand Oaks: Sage.

    Google Scholar 

  • Dillman, D. A., & Messer, B. L. (2010). Mixed-mode surveys. In P. V. Marsden, & J. D. Wright (Hrsg.), Handbook of Survey Research (S. 551–574). Howard House: Emerald Group Publishing Limited (2. . Auflage).

    Google Scholar 

  • Dillman, D. A., Phelps, G., Tortora, R., Swift, K., Kohrell, J., Berck, J., Messer, B. L. (2009). Response rate and measurement differences in mixed-mode surveys using mail, telephone, interactive voice response (IVR) and the internet. Social Science Research, 38, 1–19.

    Google Scholar 

  • Dillman, D. A., Smyth, J. D., & Christian, L. M. (2014). Internet, Phone, Mail, and Mixed-Mode Surveys. The tailored Design Method. Hoboken: Wiley (4. Auflage).

    Google Scholar 

  • Dinora, P., Schoeneman, A., Dellinger-Wray, M., Cramer, E. P., Brandt, J., & D’Aguilar, A. (2020). Using video vignettes in research and program evaluation for people with intellectual and developmental disabilities: A case study of the Leadership for empowerment and abuse prevention (LEAP) project. Evaluation & Program Planning, 79, 101774.

    Google Scholar 

  • Dong, X. L., & Srivastava, D. (2015). Big Data Integration. San Rafael: Morgan & Claypool Publishers.

    Google Scholar 

  • D’Orazio, M., Di Zo, M., & Scanu, M. (2006). Statistical Matching: Theory and practice. Chichester: Wiley.

    Google Scholar 

  • Eck, A., Córdova Cazar, A. L., Callegaro, M., & Biemer, P. (2019). Big data meets survey science. Social Science Computer Review, online first.

    Google Scholar 

  • Edelmann, A., Wolff, T., Montagne, D., & Bail, C. A. (2020). Annual Review of Sociology, 46, 61–81.

    Google Scholar 

  • Efron, B., & Hastie, T. (2016). Computer Age Statistical Inference. Algorithms, Evidence, and Data Science. New York: Cambridge University Press.

    Google Scholar 

  • Elevelt, A., Lugtig, P., & Toepoel, V. (2019). Doing a time use survey on smartphones only: What factors predict nonresponse at different stages of the survey process? Survey Research Methods, 13, 195–213.

    Google Scholar 

  • Enders, G. (2010). Applied Missing Data Analysis. New York: Guilford Press.

    Google Scholar 

  • Engel, U., Jann, B., Lynn, P., Scherpenzeel, A., & Sturgis, P. (Hrsg.) (2015). Improving Survey Methods. Lessons from Recent Research. New York: Routledge.

    Google Scholar 

  • Evans, J. R., & Mathur, A. (2005). The value of online surveys. Internet Research, 15, 195–219.

    Google Scholar 

  • Faas, T. (2003). Offline rekrutierte Access Panels: Königsweg der Online-Forschung? ZUMA-Nachrichten, 53, 58–76.

    Google Scholar 

  • Facciani, M., Brashears, M. E., & Zhong, J. (2020). International Journal of Social Research Methodology (Online first).

    Google Scholar 

  • Felt, M. (2016). Social media and the social sciences: How researchers employ big data analytics. Big Data & Society, 3, doi: https://doi.org/10.1177/2053951716645828.

  • Fielding, N. G., Lee, R. M., & Blank, G. (Hrsg.) (2017). The SAGE Handbook of Online Research Methods. London: Sage.

    Google Scholar 

  • Fletcher, R., & Nielsen, R. K. (2018). Are people incidentally exposed to news on social media? A comparative analysis. New Media & Society, 20, 2450–2468.

    Google Scholar 

  • Foster, I., Ghani, R., Jarmin, R. S., Kreuter, F., & Lane J. (2016). Big Data and Social Sciences. A Practical Guide to Methods and Tools. Boca Raton: Chapman & Hall/CRC Press.

    Google Scholar 

  • Fowler, S., & Willis, G. B. (2020). The practice of cognitive Interviewing through web probing. In P. C. Beatty, D. Collins, L. Kaye, J.-L. Padilla, G. B. Willis, & A. Wilmot (Hrsg.), Advances in Questionnaire Design, Development, Evaluation, and Testing (S. . 451–469). Hoboken: Wiley.

    Google Scholar 

  • Frey, W. R., Patton, D. U., Gaskell, M. B., & McGregor, K. A. (2020). Artificial intelligence and inclusion: Formerly gang-involved youths as domain experts for analyzing unstructured twitter data. Social Science Computer Review, 38, 42–56.

    Google Scholar 

  • Friedrich, S., Antes, G., Behr, S., Binder, H., Brannath, W., Dumpert, F., Ickstadt, K., Kestler, H., Lederer, J., Leitgöb, H., Pauly, M., Steland, A., Wilhelm, A., & Friede, T. (2020). Is there a role for statistics in artificial intelligence? arXiv: 2009.09070v1.

    Google Scholar 

  • Friemel, T. N. (2016). The digital divide has grown old: Determinants of a digital divide among seniors. New Media & Society, 18, 313–331.

    Google Scholar 

  • Fussey, P. & Roth, S. (2020). Digitizing sociology: Continuity and change in the internet era. Sociology, 54, 659–674.

    Google Scholar 

  • Galesic, M., & Bosnjak, M. (2009). Effects of questionnaire length on participation and indicators of response quality in a web survey. Public Opinion Quarterly, 73, 349–360.

    Google Scholar 

  • Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis. Boca Raton: CRC Press.

    Google Scholar 

  • Gerich, J. (2008). Real or virtual? Response behavior in video-enhanced self-administered computer interviews. Field Methods, 39, 985–992.

    Google Scholar 

  • Ghani, N. A., Hamid, S., Hashem, I. A. T., & Ahmed, E. (2019). Social media big data analytics. A survey. Computers in Human Behavior, 101, 417–428.

    Google Scholar 

  • Gilula, Z., McCulloch, R., E., & Rossi, P. E. (2006). A direct approach to data fusion. Journal of Marketing Research, 43, 73–83.

    Google Scholar 

  • Golder, S., & Macy, M. (2014). Digital footprints: Opportunities and challenges for online social research. Annual Review of Sociology, 40, 129–152.

    Google Scholar 

  • Graeff, P., & Baur, N. (2020). Digital data, administrative data, and survey compared: Updating the classical toolbox of assessing data quality of big data, exemplified by the generation of corruption data. Historical Social Research, 45, 244–269.

    Google Scholar 

  • Hand, D. (2018). Aspects of data ethics in a changing world: Where are we now? Big Data, 6, 176–190.

    Google Scholar 

  • Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning. Data Mining, Inference, and Prediction. New York: Springer (2. Auflage).

    Google Scholar 

  • Häußerling, R. (2019). Zur Erklärungsarmut von Big Social Data. Von den Schwierigkeiten, auf Basis von Big Social Data eine Erklärende Soziologie betreiben zu wollen. In D. Baron, O. Arránz Becker, & D. Lois (Hrsg.), Erklärende Soziologie und soziale Praxis (S. . 73–100). Wiesbaden: VS Verlag.

    Google Scholar 

  • Havekes, E., Coenders, M., van der Lippe, T. (2013). Positive or negative ethnic encounters in urban neighbourhoods? A photo experiment on the impact of ethnicity and neighbourhood context on attitudes towards minority and majority residents. Social Science Research, 42, 1077–1091.

    Google Scholar 

  • Heckman, J. J. (2005). The scientific model of causality. Sociological Methodology, 35, 1–97.

    Google Scholar 

  • Hedström P. (2005). Dissecting the Social. On the Principles of Analytical Sociology. Cambridge: Cambridge University Press.

    Google Scholar 

  • Helbing, D. (2015). The Automation of Society is Next: How to Survive the Digital Revolution. Scotts Valley: CreateSpace Independent Publishing Platform.

    Google Scholar 

  • Herschel, R., & Miori, V. M. (2017). Ethics & big data. Technology in Society, 49, 31–36.

    Google Scholar 

  • Herzog, T. N., Scheuren, F. J., & Winkler, W. E. (2007). Data Quality and Record Linkage Techniques. New York: Springer.

    Google Scholar 

  • Hesse, B. W., Moser, R. P., & Riley, W. T. (2015). From big data to knowledge in the social sciences. The Annals of the American Academy of Political and Social Science, 659, 16–32.

    Google Scholar 

  • Hill, C. A., Biemer, P., Buskirk, T., Callegaro, M., Córdova Cazar, A. L., Eck, A., Japec, L., Kirchner, A., Kolenikov, S., Lyberg, L., & Sturgis P. (2019). Exploring new statistical frontiers at the intersection of survey science and big data: Convergence at “BigSurv18”. Survey Research Methods, 13, 123–135.

    Google Scholar 

  • Hill, C. A., Biemer, P., Buskirk, T., Japec, L., Kirchner, A., Kolenikov, S., & Lyberg, L. (Hrsg.) (2021). Big Data Meets Survey Science. A Collection Innovative Methods. Hoboken: Wiley.

    Google Scholar 

  • Hootsuite & We Are Social (2019). Global Digital Report 2019. Verfügbar unter: https://wearesocial.com/global-digital-report-2019 (Stand: 26.01.2021).

  • Hox, J. J., de Leeuw, E. D., & Zijlmans, E. A. O. (2015) Measurement equivalence in mixed mode surveys. Frontiers in Psychology, 6, 1-11.

    Google Scholar 

  • Hünermund, P., & Bareinboim, E. (2019). Causal inference and data-fusion in econometrics. Technical Report R-51, arXiv: 1912.09104v2.

    Google Scholar 

  • Ignatow, G., & Mihalcea, R. (2017). Text Mining. A Guidebook for the Social Sciences. Los Angeles: Sage.

    Google Scholar 

  • Initiative D21 (2020). Wie digital ist Deutschland? D21 Digital-Index 19/20. Jährliches Lagebild zur Digitalen Gesellschaft. Verfügbar unter: https://initiatived21.de/app/uploads/2020/02/d21_index2019_2020.pdf (Stand: 26.01.2021).

  • Jäckle, A., Burton, J., Couper, M. P., & Lessof, C. (2019). Participation in a mobile app survey to collect expenditure data as part of a large-scale probability household panel: Coverage and participation rates and biases. Survey Research Methods, 13, 23–44.

    Google Scholar 

  • Jaidka, K., Ahmed, S., Skoric, M., Hilbert, M. (2019). Predicting elections from social media: A three-country, three-method comparative study. Asian Journal of Communication, 29, 252–273.

    Google Scholar 

  • Japec, L., Kreuter, F., Berg, M., Biemer, P., Decker, P., Lampe, C., Lane, J., O’Neil, C., & Usher, A. (2015). Big data in survey research. AAPOR task force report. Public Opinion Quarterly, 79, 839–880.

    Google Scholar 

  • Johnson, T. P., & Smith, T. W. (2017). Big data and survey research: Supplement or substitute? In P. Thakuriah, N. Tilahun, & M. Zellner, (Hrsg.), Seeing Cities Through Big Data (S. 113–125). Cham: Springer.

    Google Scholar 

  • Kandt, J. (2019). Geotracking. In N. Baur, & J. Blasius (Hrsg.), Handbuch Methoden der empirischen Sozialforschung. Band 2 (S. . 1353–1359). Wiesbaden: Springer VS (2. Auflage).

    Google Scholar 

  • Kaplan, D. (2014). Bayesian Statistics for Social Scientists. New York: Guilford Press.

    Google Scholar 

  • Kaplan, D., & McCarty, A. T. (2013). Data fusion with international large scale assessments: A case study using the OECD PISA and TALIS survey. Large-Scale Assessments in Education, 1, 1–26.

    Google Scholar 

  • Keuschnigg, M., Lovsjö, N., & Hedström, P. (2018). Analytical sociology and computational social science. Journal of Computational Social Science, 1, 3–14.

    Google Scholar 

  • Kitchin, R. (2014a). Big data, new epistemologies and paradigm shifts. Big Data & Society, 1, doi: https://doi.org/10.1177/2053951714528481.

  • Kitchin, R. (2014b). The Data Revolution. Big Data, Open Data, Data Infrastructures & Their Consequences. London: Sage.

    Google Scholar 

  • Kitchin, R., & McArdle, G. (2016). What makes big data, big data? Exploring the ontological characteristics of 26 datasets. Big Data & Society, 3, doi: https://doi.org/10.1177/2053951716631130.

  • Keusch, F., Struminskaya, B., Antoun, C., Couper, M. P., & Kreuter F. (2019). Willingness to participate in passive mobile data collection. Public Opinion Quarterly, 83, 210–235.

    Google Scholar 

  • Klausch, T., Hox, J. J., & Schouten, B. (2013). Measurement effects of survey mode on the equivalence of attitudinal rating scale questions. Sociological Methods & Research, 42, 227–263.

    Google Scholar 

  • Klausch, T., Schouten, B., & Hox, J. J. (2015). Evaluating bias of sequential mixed-mode designs against benchmark surveys. Sociological Methods & Research, 46, 456–489.

    Google Scholar 

  • Kreuter, F. (Hrsg.) (2013). Improving Surveys with Paradata. Analytic Uses of Process Information. Hoboken: Wiley.

    Google Scholar 

  • Kreuter, F. (2015). The use of paradata. In U. Engel, B. Jann, P. Lynn, A. Scherpenzeel, & P. Sturgis (Hrsg.), Improving Survey Methods. Lessons from Recent Research (S. 303–315). New York: Routledge.

    Google Scholar 

  • Kreuter, F., Haas, G.-C., Keusch, F., Bähr, S., & Trappmann, M. (2020). Collecting survey and smartphone sensor data with an app: Opportunities and challenges around privacy and informed consent. Social Science Computer Review, 38, 533–549.

    Google Scholar 

  • Kreuter, F., Presser, S., & Tourangeau R. (2008). Social desirability bias in CATI, IVR, and web surveys. The effects of mode and question sensitivity. Public Opinion Quarterly, 72, 847–865.

    Google Scholar 

  • Krosnick, J. A. (1991). Response strategies for coping with cognitive demands of attitude measures in surveys. Applied Cognitive Psychology, 5, 213–236.

    Google Scholar 

  • Krosnick, J. A., Narayan, S., & Smith, W. R. (1996). Satisficing in surveys: Initial evidence. New Directions for Program Evaluation, 70, 29–44.

    Google Scholar 

  • Kuhn, T. S. (1962). The Structure of Scientific Revolutions. Chicago: University of Chicago Press.

    Google Scholar 

  • Lakes, T. (2019). Geodaten. In N. Baur, & J. Blasius (Hrsg.), Handbuch Methoden der empirischen Sozialforschung. Band 2 (S. . 1345–1351). Wiesbaden: Springer VS (2. Auflage).

    Google Scholar 

  • Laney, D. (2001). 3-D Data Management: Controlling Data Volume, Velocity, and Variety. META Group Research Note.

    Google Scholar 

  • Lau, C. Q., Johnson, E., Amaya, A., LeBaron, P., & Sanders, H. (2018). High stakes, low resources: What mode(s) should youth employment training programs use to track alumni? Evidence from South Africa. Journal of International Development, 30, 1166–1185.

    Google Scholar 

  • Lau, C. Q., Sanders, H., & Lombaard, A. (2019). Questionnaire design in short message service (SMS) surveys. Field Methods, 31, 214–229.

    Google Scholar 

  • Lauro, N. C., Amaturo, E., Grassia, M. G., Aragona, B., & Marino, M. (Hrsg.). (2017). Data Science and Social Research. Epistemology, Methods, Technology and Applications. Cham: Springer.

    Google Scholar 

  • Lazer, D. M. J., Kennedy, R., King, G., & Vespignani, A. (2014). The parable of Google flu: Traps in big data analysis. Science, 343, 1203–1205.

    Google Scholar 

  • Lazer, D. M. J., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., Jebara, T., King, G., Macy, M., Roy, D., & van Alstyne, M. (2009). Computational social science. Science, 323, 721–723.

    Google Scholar 

  • Lazer, D. M. J, Pentland, A., Watts, D. J., Aral, S., Athey, S., Contractor, N., Freelon, D., Gonzalez-Bailon, S., King, G., Margetts, H., Nelson, A., Salganik, M. J., Strohmaier, M., Vespignani, A., & Wagner, C. (2020). Computational social science: Obstacles and opportunities. Science, 369, 1060–1062.

    Google Scholar 

  • Lazer, D. M. J., & Radford, J. (2017). Data ex machina: Introduction to big data. Annual Review of Sociology, 43, 19–39.

    Google Scholar 

  • Leitgöb, H. (2017). Ein Verfahren zur Dekomposition von Mode-Effekten in eine mess- und eine repräsentationsbezogene Komponente. In S. Eifler, & F. Faulbaum (Hrsg.), Methodische Probleme von Mixed-Mode-Ansätzen in der Umfrageforschung (S. 51–95). Wiesbaden: VS Verlag.

    Google Scholar 

  • Leitgöb, H. (2019). Rationales Antwortverhalten als Ursache messbezogener Mode-Effekte im Zuge der Erfassung sensitiver Merkmale. In N. Menold, & T. Wolbring (Hrsg.), Qualitätssicherung sozialwissenschaftlicher Erhebungsinstrumente (S. 261–305). Wiesbaden: VS Verlag.

    Google Scholar 

  • Lenzner, T., Kaczmirek, L., & Galesic, M. (2011). Seeing through the eyes of the respondent: An eye-tracking study on survey question comprehension. International Journal of Public Opinion Research, 23, 1–22.

    Google Scholar 

  • Lev-On, A. & Lowenstein-Barkai, H. (2019). Viewing diaries in an age of new media: An exploratory analysis of mobile phone app diaries versus paper diaries. Methodological Innovations, 12, doi: https://doi.org/10.1177/2059799119844442.

  • Lewis, K., Kaufman, J., Gonzalez, M., Wimmer, A., & Christakis, N. (2008). Tastes, ties, and time: A new social network dataset using Facebook.com. Social Networks, 30, 330–342.

    Google Scholar 

  • Link, M. E., Murphy, J., Schober, M. E., Buskirk, T. D., Childs, J. H., & Tesfaye, C. L. (2014). Mobile technologies for conducting, augmenting and potentially replacing surveys: Report of the AAPOR task force on emerging technologies in public opinion research. Public Opinion Quarterly, 78, 779–787.

    Google Scholar 

  • Little, R. J. A., & Rubin, D. B. (2002). Statistical Analysis with Missing Data. New York: Wiley (2. Auflage).

    Google Scholar 

  • Liu, M., & Wronski, L. (2018). Examining completion rates in web surveys via over 25,000 real-world surveys. Social Science Computer Review, 36, 116–124.

    Google Scholar 

  • Lynn, P. (2020). Evaluating push-to-web methodology for mixed-mode surveys using address-based samples. Survey Research Methods, 14, 19–30.

    Google Scholar 

  • Manderscheid, K. (2019). Text Mining. In N. Baur, & J. Blasius (Hrsg.), Handbuch Methoden der empirischen Sozialforschung. Band 2 (S. . 1103–1116). Wiesbaden: Springer VS (2. Auflage).

    Google Scholar 

  • Manfreda, K. L., & Vehovar, V. (2008). Internet surveys. In E. D. de Leeuw, J. J. Hox, & D. A. Dillman (Hrsg.), International Handbook of Survey Methodology (S. 264–284). New York: Psychology Press.

    Google Scholar 

  • Mann, A. (2016). Computational social sciences. Proceedings of the National Academy of Sciences of the United States of America, 113, 468–470.

    Google Scholar 

  • Mavletova, A., & Couper, M. P. (2014). Mobile web survey design: Scrolling versus paging, SMS versus e-mail invitations. Journal of Survey Statistics & Methodology, 2, 498–518.

    Google Scholar 

  • Mavletova, A., & Couper, M. P. (2015). A meta-analysis of breakoff rates in mobile web surveys. In D. Toninelli, R. Pinter, & P. de Pedraza (Hrsg.), Mobile Research Methods: Opportunities and Challenges of Mobile Research Methodologies (S. 81–88). London: Ubiquity Press.

    Google Scholar 

  • Mayerl, J. (2013). Response latency measurement in surveys. Detecting strong attitudes and response effects. Survey Methods: Insights from the Field, doi: https://doi.org/10.13094/SMIF-2013-00005.

  • Mazzocchi, F. (2015). Could big data be the end of theory in science? EMBO reports, 16, 1250–1255.

    Google Scholar 

  • McClain, C. A., Couper, M. P., Hupp, A. L., Keusch, F., Peterson, G., Piskorowski, A. D., & West, B. T. (2019). A typology of web survey paradata for assessing total survey error. Social Science Computer Review, 37, 196–213.

    Google Scholar 

  • McCullagh, P. & Nelder, J. A. (1989). Generalized Linear Models. Boca Raton: Chapman & Hall (2. Auflage).

    Google Scholar 

  • McDermott (2017). Conceptualizing the right to data protection in an era of big data. Big Data & Society, 4, doi: https://doi.org/10.1177/2053951716686994.

  • McFarland, D. A., Lewis, K., & Goldberg, A. (2016). Sociology in the era of big data: The ascent of forensic social science. The American Sociologist, 47, 12–35.

    Google Scholar 

  • Meng, T., Jing, X., Yan, Z., & Pedrycz, W. (2020). A survey on machine learning for data fusion. Information Fusion , 57, 115–129.

    Google Scholar 

  • Millar, M., & Dillman, D. A. (2012). Encouraging survey response via smartphones. Survey Practice, 5, doi: https://doi.org/10.29115/SP-2012-0018.

  • Mohan, K., & Pearl, J. (forthcoming). Graphical models for processing missing data. Journal of the American Statistical Association.

    Google Scholar 

  • Mohan, K., Pearl, J., & Tian, J. (2013). Graphical models for inference with missing data. In C J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, & K. Q. Weinberger (Hrsg.), Advances in Neural Information Processing System 26 (NIPS-2013) (S. 1277–1285). Red Hook: Curran Associates, Inc.

    Google Scholar 

  • Molina, M., & Garip, F. (2019). Machine learning for sociology. Annual Review of Sociology, 45, 27–45.

    Google Scholar 

  • Montgomery, J., & Cutler, J. (2013). Computerized adaptive testing for public opinion surveys. Political Analysis, 21, 172–192.

    Google Scholar 

  • Munger, K. (2019). The limited value of non-replicable field experiments in contexts with low temporal validity. Social Media + Society, 5, doi: https://doi.org/10.1177/2056305119859294.

  • Neuert, C. E. (2020). How effective are eye-tracking data in identifying problematic questions? Social Science Computer Review, 38, 793–802.

    Google Scholar 

  • Neuert, C. E., & Lenzner, T. (2016). Incorporating eye tracking into cognitive interviewing to pretest survey questions. International Journal of Social Research Methodology, 19, 501–519.

    Google Scholar 

  • Olshannikova, E., Olsson, T., Huhtamäki, J., & Kärkkäinen, H. (2017). Conceptualizing big social data. Journal of Big Data, 4, doi: https://doi.org/10.1186/s40537-017-0063-x.

  • Olson, K., Smyth, J. D., & Wood, H. M. (2012). Does giving people their preferred survey mode actually increase survey participation rates? An experimental examination. Public Opinion Quarterly, 76, 611–635.

    Google Scholar 

  • O’Reilly, J. M., Hubbard, M. L., Lessler, J. T., Biemer, P. P., & Turner, C. F. (1994). Audio and video computer-assisted self interviewing Preliminary tests of new technologies for data collection. Journal of Official Statistics, 10, 197–214.

    Google Scholar 

  • Patton, D. U., Patel, S., Hong, J. S., Ranney, M., Crandal, M, & Dungy, L. (2017). Tweets, gangs and guns: A snapshot of gang communications in Detroit. Violence & Victims, 32, 919–934.

    Google Scholar 

  • Peterson, G. (2012). Unintended Mobile Respondents. Präsentation gehalten auf der CASRO Technology Conference am 31.05.2012 in New York.

    Google Scholar 

  • Phan, T. U., & Airoldi, E. M. (2015). A natural experiment of social network information and dynamics. Proceedings of the National Academy of Sciences of the United States of America, 112, 6595–6600.

    Google Scholar 

  • Pigliucci, M. (2009). The end of theory in science? EMBO reports, 10, 534.

    Google Scholar 

  • Pinter, R. (2015). Willingness of online access panel members to participate in smartphone application-based research. In D. Toninelli, R. Pinter, & P. de Pedraza (Hrsg.), Mobile Research Methods: Opportunities and Challenges of Mobile Research Methods (S. . 141–156). London: Ubiquity Press.

    Google Scholar 

  • Piwek, L., Ellis, D. A, Andrews, S., & Joinson, A. (2016). The rise of consumer health wearables: Promises and barriers. PLOS Medicine, 13, e1001953.

    Google Scholar 

  • Puchinger, C. (2016). Die Anwendung von Text Mining in den Sozialwissenschaften. In M. Lemke, & G. Wiedemann (Hrsg.), Text Mining in den Sozialwissenschaften. Grundlagen und Anwendungen zwischen qualitativer und quantitativer Diskursanalyse (S. . 117–136). Wiesbaden: Springer VS.

    Google Scholar 

  • Rashotte, L. S. (2003). Written versus visual stimuli in the study of impression formation. Social Science Research, 32, 278–293.

    Google Scholar 

  • Rässler, S. (2002). Statistical Matching: A Frequentist Theory, Practical Applications, and Alternative Bayesian Approaches. New York: Springer.

    Google Scholar 

  • Rässler, S. (2004). Data fusion: Identification problems, validity, and multiple imputation. Austrian Journal of Statistics, 33, 153–171.

    Google Scholar 

  • Read, B. (2019). Respondent burden in a mobile app: Evidence from a shipping receipt scanning study. Survey Research Methods, 13, 45–71.

    Google Scholar 

  • Richards, N. M., & King, J. H. (2014). Big data ethics. Wake Forest Law Review, 49, 393–432.

    Google Scholar 

  • Ruhrberg, S. D., Kirstein, G., Habermann, T., Nikolic, J., & Stock W. G. (2018). #ISIS—A comparative analysis of country-specific sentiment on Twitter. Open Journal of Social Sciences, 6, 142–158.

    Google Scholar 

  • Ruths, D., & Pfeffer, J. (2014). Social media for large studies of behavior. Science, 346, 1063–1064.

    Google Scholar 

  • Salganik, M. J. (2018). Bit by Bit. Social Research in the Digital Age. Princeton: Princeton University Press.

    Google Scholar 

  • Salganik, M. J., Dodds, P. S., & Watts, D. J. (2006). Experimental study of inequality and unpredictability in an artificial cultural market. Science, 311, 854–856.

    Google Scholar 

  • Scherpenzeel, A. (2017). Mixing online panel data collection with innovative methods. In S. Eifler & F. Faulbaum (Hrsg.), Methodische Probleme von Mixed-Mode-Ansätzen in der Umfrageforschung (S. . 27–49). Wiesbaden: Springer VS.

    Google Scholar 

  • Schnell, R. (2015). Linking surveys and administrative data. In U. Engel, B. Jann, P. Lynn, A. Scherpenzeel, & P. Sturgis (Hrsg.), Improving Survey Methods. Lessons from Recent Research (S. 273–287). New York: Routledge.

    Google Scholar 

  • Schnell, R. (2016). Privacy-preserving record linkage. In K. Harron, H. Goldstein, & C. Dibben (Hrsg.), Methodological Developments in Data Linkage (S. 201–225). Chichester: Wiley & Sons.

    Google Scholar 

  • Schnell, R. (2019). “Big Data” aus sozialwissenschaftlicher Sicht: Warum es kaum sozialwissenschaftliche Studien ohne Befragungen gibt. In D. Baron, O. Arránz Becker, & Lois, D. (Hrsg.), Erklärende Soziologie und soziale Praxis (S. 101–125). Wiesbaden: VS Verlag.

    Google Scholar 

  • Schnell, R., Bachteler, T., & Reiher, J. (2009). Privacy-preserving record linkage using Bloom filters. BMC Medical Informatics & Decision Making, 9, 1–11.

    Google Scholar 

  • Sewalk, K. C., Tuli,. G., Hswen, Y., Brownstein, J. S., Hwakins, J. B. (2018). Using Twitter to examine web-based patient experience sentiments in the United States: A longitudinal analysis. Journal of Medical Internet Research, 20, e10043, doi: https://doi.org/10.2196/10043.

  • Shah, D. V., Cappella, J. N., & Neuman, W. R. (2015). Big Data, digital media, and computational social science: Possibilities and perils. Annals of the American Academy of Political & Social Science, 659, 6–13.

    Google Scholar 

  • Simon, H. A. (1957). Models of Man. New York: Wiley.

    Google Scholar 

  • Sloan, L., Jessop, C., Al Baghal, T., & Williams M. (2020). Linking survey and Twitter data: Informed consent, disclosure, security, and archiving. Journal of Empirical Research on Human Research Ethics, 15, 63–76.

    Google Scholar 

  • Smyth, J. D., Olson, K., & Kasabian, A. (2014a). The effect of answering in a preferred versus a non-preferred survey mode on measurement. Survey Research Methods, 8, 137–152.

    Google Scholar 

  • Smyth, J. D., Olson, K., & Millar, M. M. (2014b). Identifying predictors of survey mode preference. Social Science Research, 48, 135–144.

    Google Scholar 

  • Stapleton, C. (2013). The smart(phone) way to collect survey data. Survey Practice 6, doi: https://doi.org/10.29115/SP-2013-0011.

  • Stoop, I., & Wittenberg, M. (Hrsg.). (2008). Access Panels and Online Research, Panacea or Pitfall? Amsterdam: Askant Academic Publishers.

    Google Scholar 

  • Succi, S., & Coveney, P. V. (2019). Big data: The end of the scientific method? Philosophical Transactions of the Royal Society A, 377, 20180145.

    Google Scholar 

  • Thoemmes, F., & Mohan, K. (2015). Graphical representation of missing data problems. Structural Equation Modeling, 22, 631–642.

    Google Scholar 

  • Toepoel, V., & Lugtig, P. (2015). Online surveys are mixed-device surveys. Issues associated with the use of different (mobile) devices in web surveys. methods, data, analyses, 9, 155–162.

    Google Scholar 

  • Toninelli, D., Pinter, R., & de Pedraza, P. (2015). Mobile Research Methods. Opportunities and Challenges of Mobile Research Methodologies. London: Ubiquity Press.

    Google Scholar 

  • Toninelli, D., & Revilla, M. (2016). Smartphone vs PCs: Does the device affect the web survey experience and the measurement error for sensitive topics? A replication of the Mavletova & Couper’s 2013 experiment. Survey Research Methods, 10, 153–169.

    Google Scholar 

  • Tourangeau, R. (2017). Mixing modes: Tradeoffs among coverage, nonresponse, and measurement error. In P. P. Biemer, E. D. de Leeuw, S. Eckman, B. Edwards, F. Kreuter, L. E. Lyberg, N. C. Tucker, & B. T. West (Hrsg.), Total Survey Error in Practice (S. 115–132). Hoboken: Wiley.

    Google Scholar 

  • Tourangeau, R., Steiger, D. M., & Wilson, D. (2002). Self-administered questions by telephone: Evaluating Interactive Voice Response. Public Opinion Quarterly, 66, 265–278.

    Google Scholar 

  • Triantafillou, E., Georgiadou, E., & Economides, A. A. (2008). The design and evaluation of a computerized adaptive test on mobile devices. Computers & Education, 50, 1319–1330.

    Google Scholar 

  • Tsai, C.-W., Lai, C.-F., Chao, H.-C., & Vasilakos, A. V. (2015). Big data analytics. A survey. Journal of Big Data, 2, 21, doi: https://doi.org/10.1186/s40537-015-0030-3.

  • Turner, C. F., Ku, L., Rogers, S. M., Lindberg, L. S., Pleck, J. H., & Sonenstein, F. L. (1998). Adolescent sexual behavior, drug use, and violence: increased reporting with computer survey technology. Science, 280, 867–873.

    Google Scholar 

  • Urban, D. & Mayerl, J. (2007). Antwortlatenzzeiten in der surveybasierten Verhaltensforschung. Kölner Zeitschrift für Soziologie & Sozialpsychologie, 59, 692–713.

    Google Scholar 

  • Vandenplas, C., Loosveldt, G., & Vannieuwenhuyze, J. T. A. (2016). Assessing the use of mode preference as a covariate for the estimation of measurement effects between modes. A sequential mixed mode experiment. methods, data, analyses, 10, 119–142.

    Google Scholar 

  • van de Rijt, A., Kang, S. M., Restivo, M., & Patil, A. (2014). Field experiments of success-breeds-success dynamics. Proceedings of the National Academy of Sciences, 111, 6934–6939.

    Google Scholar 

  • van der Linden, W. J., & Glas, G. A. W. (Hrsg.) (2000). Computerized Adaptive Testing: Theory and Practice. New York: Kluwer Academic Publishers.

    Google Scholar 

  • van der Putten, P, & Kok, J. N. (2010). Using data fusion to enrich customer databases with survey data for database marketing. In J. Casillas, & F. J. Marínez-López (Hrsg.), Marketing Intelligence Systems Using Soft Computing. Managerial and Research Applications. Studies in Fuzziness & Soft Computing, Vol. 258 (S. . 113–130). Berlin: Springer.

    Google Scholar 

  • Vannieuwenhuyze, J. T. A., & Loosveldt, G. (2013). Evaluating relative mode effects in mixed mode surveys: three methods to disentangle selection and measurement effects. Sociological Methods & Research, 42, 82–104.

    Google Scholar 

  • Vannieuwenhuyze, J. T. A., Loosveldt, G., & Molenberghs, G. (2010). A method for evaluating mode effects in mixed-mode surveys. Public Opinion Quarterly, 74, 1027–1045.

    Google Scholar 

  • Vannieuwenhuyze, J. T. A., Loosveldt, G., & Molenberghs, G. (2014). Evaluating mode effects in mixed-mode survey data using covariate adjustment models. Journal of Official Statistics, 30, 1–21.

    Google Scholar 

  • van Selm, M., & Jankowski, N. W. (2006). Conducting online surveys. Quality & Quantity, 40, 435–456.

    Google Scholar 

  • Vatsalan, D., Christen, P., & Verykios, V. S (2013). A taxonomy of privacy-preserving record linkage techniques. Information Systems, 38, 946–969.

    Google Scholar 

  • Vatsalan, D., Sehili, Z., Christen, P., & Rahm, E. (2017). Privacy-preserving record linkage for big data: Current approaches and research challenges. In A. Zomaya, & S. Sakr (Hrsg.), Handbook of Big Data Technologies (S. 851–895). Cham: Springer.

    Google Scholar 

  • Wainer, H. (Hrsg.) (2000). Computerized Adaptive Testing. A Primer. London: Routledge/Taylor & Francis Group (2. Auflage).

    Google Scholar 

  • Watts, D. J. (2011). Everything is Obvious: How Common Sense Fails Us. New York: Crown Business.

    Google Scholar 

  • Watts, D. J. (2014). Common sense and sociological explanations. American Journal of Sociology, 120, 313–351.

    Google Scholar 

  • Wells, T., Bailey, J., & Link, M. (2013). Filling the void: Gaining a better understanding of tablet-based surveys. Survey Practice, 6, 1–9.

    Google Scholar 

  • Wolbring, T. (2020). The digital revolution in the social sciences: Five theses about big data and other recent methodological innovations from an analytical sociologist. In S. Maasen, & J.-H. Passoth (Hrsg.), Soziologie des Digitalen – Digitale Soziologie. Soziale Welt – Sonderband 23, 60–72.

    Google Scholar 

  • Yamamoto, K. Shin, H. J., & Khorramdel, L. (2019). Introduction of multistage adaptive testing design in PISA 2018. OECD Education Working Paper No. 209. Verfügbar unter: http://www.oecd.org/officialdocuments/publicdisplaydocumentpdf/?cote=EDU/WKP(2019)17&docLanguage=En (12.01.2021).

  • Zubiaga, A., Procter, R., & Maple, C. (2018). A longitudinal analysis of the public perception of the opportunities and challenges of the internet of things. PLOS ONE, 13, e0209472.

    Google Scholar 

  • Zwitter, A. (2014). Big data ethics. Big Data & Society, 1, doi: https://doi.org/10.1177/2053951714559253.

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  1. Universität Eichstätt-Ingolstadt, Eichstätt, Deutschland

    Heinz Leitgöb

  2. Friedrich-Alexander-Universität Erlangen-Nürnberg, Nürnberg, Deutschland

    Tobias Wolbring

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    Tobias Wolbring

  2. Fachbereich Soziologie, Katholische Universität Eichstätt- Ingolstadt, Eichstätt, Germany

    Heinz Leitgöb

  3. Institut für Soziologie, Universität Duisburg-Essen, Duisburg, Germany

    Frank Faulbaum

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Leitgöb, H., Wolbring, T. (2021). Die Methoden der sozialwissenschaftlichen Datenerhebung im digitalen Zeitalter. In: Wolbring, T., Leitgöb, H., Faulbaum, F. (eds) Sozialwissenschaftliche Datenerhebung im digitalen Zeitalter. Schriftenreihe der ASI - Arbeitsgemeinschaft Sozialwissenschaftlicher Institute. Springer VS, Wiesbaden. https://doi.org/10.1007/978-3-658-34396-5_1

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