Araştırma Makalesi
BibTex RIS Kaynak Göster

Türkiye'deki Sosyal Bilimciler Arasında Büyük Veriye Aşinalık, Yeterlik ve Kullanım Farklarının İzini Sürmek: Duymuş Olmaktan Uygulamaya

Yıl 2025, Cilt: 22 Sayı: 3, 368 - 390, 31.05.2025

Yaser Koyuncu , İlknur Yüksel-kaptanoğlu

https://doi.org/10.26466/opusjsr.1684496

Öz

Bu çalışma, Türkiye’deki sosyal bilimler alanında çalışan akademisyenlerin büyük veri ile nasıl ilişkilendiğini, aşinalık, yeterlik ve kullanım düzeyleri üzerinden incelemektedir. 3.606 akademisyeni kapsayan büyük ölçekli bir web anketine dayanarak, bireysel tecrübelerin, metodolojik yönelimlerin ve kurumsal etkilerin büyük veri ile etkileşimle nasıl ilişkilendiği analiz edilmiştir. Betimleyici analizler ve lojistik regresyon sonuçları, nicel araştırmaya metodolojik yakınlık, yeniliğe açıklık ve bölüm müfredatları aracılığıyla kurumsal düzeyde maruz kalmanın, büyük veri yeterliği ve kullanımının temel belirleyicileri olduğunu ortaya koymaktadır. Buna karşılık, üniversiteler arası eşitsizlikler ve sosyoekonomik gelişmişlik düzeyleri gibi yapısal ayrımların, daha derin düzeydeki etkileşimden ziyade aşinalığı etkilediği görülmektedir. Çalışma, bulguları bağlamlaştırmak amacıyla bilgi üretim kültürleri, pedagojik düzenek kuramı, yeniliklerin yayılımı kuramı ve teknoloji kabul modelleri gibi kuramsal çerçeveleri bir araya getirmektedir. Bu araştırma, Küresel Kuzey dışındaki sosyal bilimcilerin büyük veriye adaptasyonu konusundaki sınırlı ampirik literatüre katkı sağlamakta ve Türkiye’de sosyal araştırma metodolojisinde daha geniş çaplı bir benimsemenin önündeki yapısal, müfredatsal ve tutumsal engellere ve yaklaşımlara dikkat çekmektedir.

Anahtar Kelimeler

büyük veriyle etkileşim hesaplamalı sosyal bilimler sosyal araştırma yöntemleri Türkiye’de yüksek öğretin yenilikleri benimseme

Kaynakça

  • Agrawal, M., Peterson, J. C., & Griffiths, T. L. (2020). Scaling up psychology via Scientific Regret Minimization. Proceedings of the National Academy of Sciences, 117(16), 8825–8835. https://doi.org/10.1073/pnas.1915841117
  • Aytaç, Z., & Bilge, H. Ş. (2021). Öğretim elemanlarının büyük veri farkındalık ve eğitim beklentilerinin değerlendirilmesi.1. İşletme, 2(2), 29–46.
  • Astleitner, H. (2024). We have big data, but do we need big theory? Review-based remarks on an emerging problem in the social sciences. Philosophy of the Social Sciences, 54(1), 69–92. https://doi.org/10.1177/00483931231188825
  • Barnes, T. J., & Wilson, M. W. (2014). Big data, social physics, and spatial analysis: The early years. Big Data & Society, 1(1), 205395171453536. https://doi.org/10.1177/2053951714535365
  • Bernstein, B. B. (2000). Pedagogy, symbolic control, and identity: Theory, research, critique (Rev. ed). Rowman & Littlefield Publishers.
  • Bethlehem, J. (2010). Selection bias in web surveys. International Statistical Review, 78(2), 161–188. https://doi.org/10.1111/j.1751-5823.2010.00-112.x
  • Beuving, J. J. (2020). Ethnography’s future in the big data era. Information, Communication & Society, 23(11), 1625–1639. https://doi.org/10.10-80/1369118X.2019.1602664
  • Bjerre-Nielsen, A., & Glavind, K. L. (2022). Ethnographic data in the age of big data: How to compare and combine. Big Data & Society, 9(1), 20539517211069893. https://doi.org/10.-1177/20539517211069893
  • Bölükbaş, K. (2021). Sosyal bilimlerde büyük veri üzerine yapılan akademik çalışmaların analizi: YÖK tez örneği (2010 - 2020). Akdeniz Üniversitesi İletişim Fakültesi Dergisi, 35, 158–173. https://doi.org/10.31123/akil.886708
  • Brady, H. E. (2019). The challenge of big data and data science. Annual Review of Political Science, 22(1), 297–323. https://doi.org/10.1146/annurev-polisci-090216-023229
  • Cai, L., & Zhu, Y. (2015). The challenges of data quality and data quality assessment in the big data era. Data science journal, 14, 2-2.
  • Cabrera-Álvarez, P. (2022). Survey research in times of big data. Empiria. Revista de Metodología de Ciencias Sociales, 53. https://doi.org/10.-5944/empiria.53.2022.32611
  • Callegaro, M., & Yang, Y. (2018). The role of surveys in the era of “Big Data.” In The Palgrave Handbook of Survey Research (pp. 175–198).
  • Chakravorti, B., Bhalla, A., & Chuturverdi, R. S. (2019). Which countries are leading the data economy? Harvard Business Review. https://hbr.org/2019/01/which-countries-are-leading-the-data-economy
  • Connelly, R., Playford, C. J., Gayle, V., & Dibben, C. (2016). The role of administrative data in the big data revolution in social science research. Social Science Research, 59, 1–12. https://doi.org/10.1016/j.ssresearch.2016.04.015
  • Cox, M., & Ellsworth, D. (1997). Managing big data for scientific visualization. https://www.researchgate.net/publication/238704525_Managing_big_data_for_scientific_visualization
  • Daikeler, J., Bošnjak, M., & Lozar Manfreda, K. (2020). Web versus other survey modes: an updated and extended meta-analysis comparing response rates. Journal of Survey Statistics and Methodology, 8(3), 513–539. https://doi.org/10.1093/jssam/smz008
  • Davenport, T. (2014). Stop using the term “big data.” Deloitte Insights. https://www2.deloitte.com/-us/en/insights/topics/analytics/big-data-buzzword.html
  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319. https://doi.org/10.2307/249008
  • Diaz-Bone, R., Horvath, K., & Cappel, V. (2020). Social research in times of big data. The challenges of new data worlds and the need for a sociology of social research. Historical Social Research/Historische Sozialforschung, 45(3), 314-341.
  • Favaretto, M., De Clercq, E., Schneble, C. O., & Elger, B. S. (2020). What is your definition of Big Data? Researchers’ understanding of the phenomenon of the decade. PLOS ONE, 15(2), e0228987. https://doi.org/10.1371/journal.pone.0228987
  • Florescu, D., Karlberg, M., Reis, F., Del Castillo, P. R., Skaliotis, M., & Wirthmann, A. (2014, June). Will ‘big data’transform official statistics. In European Conference on the QualityofOfficial Statistics. Vienna, Austria (pp. 2-5).
  • Foster, I. (Ed.). (2017). Big data and social science: A practical guide to methods and tools. CRC Press Taylor & Francis Group.
  • Halford, S., & Savage, M. (2017). Speaking Sociologically with Big Data: Symphonic Social Science and the Future for Big Data Research. Sociology, 51(6), 1132–1148. https://doi.org/-10.1177/0038038517698639
  • Hosseini, M., Wieczorek, M., & Gordijn, B. (2022). Ethical issues in social science research employing big data. Science and Engineering Ethics, 28(3), 29. https://doi.org/10.1007/s11948-022-00380-7
  • Hofman, J. M., Watts, D. J., Athey, S., Garip, F., Griffiths, T. L., Kleinberg, J., Margetts, H., Mullainathan, S., Salganik, M. J., Vazire, S., Vespignani, A., & Yarkoni, T. (2021). Integrating explanation and prediction in computational social science. Nature, 595(7866), 181–188. https://doi.org/10.1038/s41586-021-03659-0
  • Household Information Technologies Survey. (2024). Türkiye İstatistik Kurumu. https://data.tuik.-gov.tr/Bulten/Index?p=Hanehalki-Bilisim-Teknolojileri-(BT)-Kullanim-Arastirmasi-2024-53492
  • Howe III, E. G., & Elenberg, F. (2020). Ethical challenges posed by big data. Innovations in clinical neuroscience, 17(10-12), 24.
  • Ignatow, G. (2020). Sociological theory in the digital age (1 Edition). Routledge.
  • Japec, L., Kreuter, F., Berg, M., Biemer, P., Decker, P., Lampe, C., Lane, J., O’Neil, C., & Usher, A. (2015). AAPOR Report: Big Data. American Association for Public Opinion Research. https://www.aapor.org/Education-Resources/Reports/Big-Data.aspx#8.%20Conclusions%20and%20Research%20Needs
  • Karaca, Y. (2024). Kamu Politikalarında Büyük Veri Kullanımının İncelenmesi ve Türkiye Bağlamında Bir Analiz. Muğla Sıtkı Koçman Üniversitesi.
  • King, G. (2011). The Social Science Data Revolution. Horizons in Political Science Talk, Harvard University. https://gking.harvard.edu/files-/gking/files/evbase-horizonsp.pdf
  • Kitchin, R. (2014). Big data, new epistemologies and paradigm shifts. Big Data & Society, 1(1), 205395171452848. https://doi.org/10.1177/2053951714528481
  • Kitchin, R., & McArdle, G. (2016). What makes Big Data, Big Data? Exploring the ontological characteristics of 26 datasets. Big Data & Society, 3(1), 205395171663113. https://doi.org/-10.1177/2053951716631130
  • Knorr-Cetina, K. (1999). Epistemic cultures: How the sciences make knowledge. Harvard University Press.
  • Kong, S. M., Carroll, K. M., Lundberg, D. J., Omura, P., & Lepe, B. A. (2020). Reducing gender bias in STEM. MIT Science Policy Review, 1. https://doi.org/10.38105/spr.11kp6lqr0a
  • Köseoğlu, Ö., & Demirci, Y. (2017). Türkiye’de büyük veri ve veri madenciliğine ilişkin politika ve stratejiler: ulusal politika belgelerinin içerik analizi. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Dergisi, 2017, 2223–2239.
  • Laney, D. (2001). 3D Data management: Controlling data volume, velocity, and variety. META Group. https://www.bibsonomy.org/bibtex/742811-cb00b303261f79a98e9b80bf49
  • Lazer, D., Hargittai, E., Freelon, D., Gonzalez-Bailon, S., Munger, K., Ognyanova, K., & Radford, J. (2021). Meaningful measures of human society in the twenty-first century. Nature, 595(7866), 189–196. https://doi.org/10.1038/-s41586-021-03660-7
  • Lukoianova, T., & Rubin, V. L. (2014). Veracity roadmap: Is big data objective, truthful and credible? Advances in Classification Research Online, 24(1), 4. https://doi.org/10.7152-/acro.v24i1.14671
  • Mann, A. (2016). Core concept: Computational social science. Proceedings of the National Academy of Sciences, 113(3), 468–470. https://doi.org/10.-1073/pnas.1524881113
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From Hearing to Doing: Tracing the Gaps in Familiarity, Competency, and Use of Big Data Among Türkiye’s Social Scientists

Yıl 2025, Cilt: 22 Sayı: 3, 368 - 390, 31.05.2025

Yaser Koyuncu , İlknur Yüksel-kaptanoğlu

https://doi.org/10.26466/opusjsr.1684496

Öz

This study investigates how academics in Türkiye’s social sciences engage with big data by examining their familiarity, competency, and actual use. Drawing on a large-scale web survey of 3,606 academics, we analyze how individual backgrounds, methodological orientations, and institutional environments relate to engagement with big data. Descriptive and logistic regression analyses reveal that methodological proximity to quantitative research, openness to innovation, and institutional exposure through departmental curricula are key drivers of competency and use. Conversely, structural divides, such as inequalities between universities and socio-economic development levels, appear to affect familiarity more than deeper engagement. Theoretically, the study integrates frameworks including epistemic cultures, pedagogic device theory, diffusion of innovations, and technology acceptance models to contextualize the findings. This research contributes to the limited empirical literature on big data adaptation among social scientists outside the Global North and address structural, curricular, and attitudinal barriers and approaches for broader adoption in Türkiye's social research methodology.

Anahtar Kelimeler

big data engagement computational social sciences social research methodology higher education in Türkiye innovation adoption

Kaynakça

  • Agrawal, M., Peterson, J. C., & Griffiths, T. L. (2020). Scaling up psychology via Scientific Regret Minimization. Proceedings of the National Academy of Sciences, 117(16), 8825–8835. https://doi.org/10.1073/pnas.1915841117
  • Aytaç, Z., & Bilge, H. Ş. (2021). Öğretim elemanlarının büyük veri farkındalık ve eğitim beklentilerinin değerlendirilmesi.1. İşletme, 2(2), 29–46.
  • Astleitner, H. (2024). We have big data, but do we need big theory? Review-based remarks on an emerging problem in the social sciences. Philosophy of the Social Sciences, 54(1), 69–92. https://doi.org/10.1177/00483931231188825
  • Barnes, T. J., & Wilson, M. W. (2014). Big data, social physics, and spatial analysis: The early years. Big Data & Society, 1(1), 205395171453536. https://doi.org/10.1177/2053951714535365
  • Bernstein, B. B. (2000). Pedagogy, symbolic control, and identity: Theory, research, critique (Rev. ed). Rowman & Littlefield Publishers.
  • Bethlehem, J. (2010). Selection bias in web surveys. International Statistical Review, 78(2), 161–188. https://doi.org/10.1111/j.1751-5823.2010.00-112.x
  • Beuving, J. J. (2020). Ethnography’s future in the big data era. Information, Communication & Society, 23(11), 1625–1639. https://doi.org/10.10-80/1369118X.2019.1602664
  • Bjerre-Nielsen, A., & Glavind, K. L. (2022). Ethnographic data in the age of big data: How to compare and combine. Big Data & Society, 9(1), 20539517211069893. https://doi.org/10.-1177/20539517211069893
  • Bölükbaş, K. (2021). Sosyal bilimlerde büyük veri üzerine yapılan akademik çalışmaların analizi: YÖK tez örneği (2010 - 2020). Akdeniz Üniversitesi İletişim Fakültesi Dergisi, 35, 158–173. https://doi.org/10.31123/akil.886708
  • Brady, H. E. (2019). The challenge of big data and data science. Annual Review of Political Science, 22(1), 297–323. https://doi.org/10.1146/annurev-polisci-090216-023229
  • Cai, L., & Zhu, Y. (2015). The challenges of data quality and data quality assessment in the big data era. Data science journal, 14, 2-2.
  • Cabrera-Álvarez, P. (2022). Survey research in times of big data. Empiria. Revista de Metodología de Ciencias Sociales, 53. https://doi.org/10.-5944/empiria.53.2022.32611
  • Callegaro, M., & Yang, Y. (2018). The role of surveys in the era of “Big Data.” In The Palgrave Handbook of Survey Research (pp. 175–198).
  • Chakravorti, B., Bhalla, A., & Chuturverdi, R. S. (2019). Which countries are leading the data economy? Harvard Business Review. https://hbr.org/2019/01/which-countries-are-leading-the-data-economy
  • Connelly, R., Playford, C. J., Gayle, V., & Dibben, C. (2016). The role of administrative data in the big data revolution in social science research. Social Science Research, 59, 1–12. https://doi.org/10.1016/j.ssresearch.2016.04.015
  • Cox, M., & Ellsworth, D. (1997). Managing big data for scientific visualization. https://www.researchgate.net/publication/238704525_Managing_big_data_for_scientific_visualization
  • Daikeler, J., Bošnjak, M., & Lozar Manfreda, K. (2020). Web versus other survey modes: an updated and extended meta-analysis comparing response rates. Journal of Survey Statistics and Methodology, 8(3), 513–539. https://doi.org/10.1093/jssam/smz008
  • Davenport, T. (2014). Stop using the term “big data.” Deloitte Insights. https://www2.deloitte.com/-us/en/insights/topics/analytics/big-data-buzzword.html
  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319. https://doi.org/10.2307/249008
  • Diaz-Bone, R., Horvath, K., & Cappel, V. (2020). Social research in times of big data. The challenges of new data worlds and the need for a sociology of social research. Historical Social Research/Historische Sozialforschung, 45(3), 314-341.
  • Favaretto, M., De Clercq, E., Schneble, C. O., & Elger, B. S. (2020). What is your definition of Big Data? Researchers’ understanding of the phenomenon of the decade. PLOS ONE, 15(2), e0228987. https://doi.org/10.1371/journal.pone.0228987
  • Florescu, D., Karlberg, M., Reis, F., Del Castillo, P. R., Skaliotis, M., & Wirthmann, A. (2014, June). Will ‘big data’transform official statistics. In European Conference on the QualityofOfficial Statistics. Vienna, Austria (pp. 2-5).
  • Foster, I. (Ed.). (2017). Big data and social science: A practical guide to methods and tools. CRC Press Taylor & Francis Group.
  • Halford, S., & Savage, M. (2017). Speaking Sociologically with Big Data: Symphonic Social Science and the Future for Big Data Research. Sociology, 51(6), 1132–1148. https://doi.org/-10.1177/0038038517698639
  • Hosseini, M., Wieczorek, M., & Gordijn, B. (2022). Ethical issues in social science research employing big data. Science and Engineering Ethics, 28(3), 29. https://doi.org/10.1007/s11948-022-00380-7
  • Hofman, J. M., Watts, D. J., Athey, S., Garip, F., Griffiths, T. L., Kleinberg, J., Margetts, H., Mullainathan, S., Salganik, M. J., Vazire, S., Vespignani, A., & Yarkoni, T. (2021). Integrating explanation and prediction in computational social science. Nature, 595(7866), 181–188. https://doi.org/10.1038/s41586-021-03659-0
  • Household Information Technologies Survey. (2024). Türkiye İstatistik Kurumu. https://data.tuik.-gov.tr/Bulten/Index?p=Hanehalki-Bilisim-Teknolojileri-(BT)-Kullanim-Arastirmasi-2024-53492
  • Howe III, E. G., & Elenberg, F. (2020). Ethical challenges posed by big data. Innovations in clinical neuroscience, 17(10-12), 24.
  • Ignatow, G. (2020). Sociological theory in the digital age (1 Edition). Routledge.
  • Japec, L., Kreuter, F., Berg, M., Biemer, P., Decker, P., Lampe, C., Lane, J., O’Neil, C., & Usher, A. (2015). AAPOR Report: Big Data. American Association for Public Opinion Research. https://www.aapor.org/Education-Resources/Reports/Big-Data.aspx#8.%20Conclusions%20and%20Research%20Needs
  • Karaca, Y. (2024). Kamu Politikalarında Büyük Veri Kullanımının İncelenmesi ve Türkiye Bağlamında Bir Analiz. Muğla Sıtkı Koçman Üniversitesi.
  • King, G. (2011). The Social Science Data Revolution. Horizons in Political Science Talk, Harvard University. https://gking.harvard.edu/files-/gking/files/evbase-horizonsp.pdf
  • Kitchin, R. (2014). Big data, new epistemologies and paradigm shifts. Big Data & Society, 1(1), 205395171452848. https://doi.org/10.1177/2053951714528481
  • Kitchin, R., & McArdle, G. (2016). What makes Big Data, Big Data? Exploring the ontological characteristics of 26 datasets. Big Data & Society, 3(1), 205395171663113. https://doi.org/-10.1177/2053951716631130
  • Knorr-Cetina, K. (1999). Epistemic cultures: How the sciences make knowledge. Harvard University Press.
  • Kong, S. M., Carroll, K. M., Lundberg, D. J., Omura, P., & Lepe, B. A. (2020). Reducing gender bias in STEM. MIT Science Policy Review, 1. https://doi.org/10.38105/spr.11kp6lqr0a
  • Köseoğlu, Ö., & Demirci, Y. (2017). Türkiye’de büyük veri ve veri madenciliğine ilişkin politika ve stratejiler: ulusal politika belgelerinin içerik analizi. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Dergisi, 2017, 2223–2239.
  • Laney, D. (2001). 3D Data management: Controlling data volume, velocity, and variety. META Group. https://www.bibsonomy.org/bibtex/742811-cb00b303261f79a98e9b80bf49
  • Lazer, D., Hargittai, E., Freelon, D., Gonzalez-Bailon, S., Munger, K., Ognyanova, K., & Radford, J. (2021). Meaningful measures of human society in the twenty-first century. Nature, 595(7866), 189–196. https://doi.org/10.1038/-s41586-021-03660-7
  • Lukoianova, T., & Rubin, V. L. (2014). Veracity roadmap: Is big data objective, truthful and credible? Advances in Classification Research Online, 24(1), 4. https://doi.org/10.7152-/acro.v24i1.14671
  • Mann, A. (2016). Core concept: Computational social science. Proceedings of the National Academy of Sciences, 113(3), 468–470. https://doi.org/10.-1073/pnas.1524881113
  • Manovich, L. (2011). The promises and the challenges of big social data. Debates in the digital humanities. Debates in the Digital Humanities, 2, 460–475.
  • McKinnon, M., & O’Connell, C. (2020). Perceptions of stereotypes applied to women who publicly communicate their STEM work. Humanities and Social Sciences Communications, 7(1), 160. https://doi.org/10.1057/s41599-020-00654-0
  • Marres, N. (2017). Digital sociology. Polity Press.
  • Mazzocchi, F. (2015). Could big data be the end of theory in science?: A few remarks on the epistemology of data‐driven science. EMBO Reports, 16(10), 1250–1255. https://doi.org/10.15252/embr.201541001
  • Metzler, K., Kim, D. A., Allum, N., & Denman, A. (2016). Who is doing computational social science? Trends in big data research (A SAGE White Paper). Sage Publication. https://static1.squarespace.com/static/5d5ad9e0100bdf0001af0f5e/t/5e15fca81845d424d285e22d/1578499246868/Who-is-doing-computational-social-science.pdf
  • Parry, D. A. (2024). Without access to social media platform data, we risk being left in the dark. South African Journal of Science, 120(3/4). https://doi.org/10.17159/sajs.2024/17008
  • Rogers, E. M. (2003). Diffusion of innovations (Fifth edition). Free Press.
  • Şallı, Ş. (2021). Büyük veri devrimi ve sosyal bilimler araştırma yöntemlerinde yeni paradigmalar. Karabük University.
  • Ruths, D., & Pfeffer, J. (2014). Social media for large studies of behavior. Science, 346(6213), 1063-1064.
  • Sarkar, S. (2021). Using qualitative approaches in the era of big data: A confessional tale of a behavioral researcher. Journal of Information Technology Case and Application Research, 23(2), 139–144. https://doi.org/10.1080/15228053.20-21.1916229
  • Strateji ve Bütçe Başkanlığı Stratejik Plan (2019-2023). (2018). Strateji ve Bütçe Başkanlığı. https://www.sbb.gov.tr/wp-content/uploads/2021/07/Strateji-ve-Butce-Baskanligi-2019-2023-Stratejik-Plani-28072021.pdf
  • Strateji ve Bütçe Başkanlığı Stratejik Plan (2024-2028). (2023). Strateji ve Bütçe Başkanlığı. https://www.sbb.gov.tr/wp-content/uploads/2024/04/STRATEJIK-PLAN-2024-2028_03042024.pdf
  • Stegenga, S. M., Steltenpohl, C. N., Lustick, H., Meyer, M. S., Renbarger, R., Standiford Reyes, L., & Lee, L. E. (2024). Qualitative research at the crossroads of open science and big data: Ethical considerations. Social and Personality Psychology Compass, 18(1), e12912. https://doi.org/10.1111/spc3.12912
  • Van Dijk, J. A. G. M. (2006). Digital divide research, achievements and shortcomings. Poetics, 34(4–5), 221–235. https://doi.org/10.1016/j.poetic.2006.05.004
  • Veltri, G. (2020). Digital Social Research. John Wiley & Sons, Inc.& Society, 1(1), 205395171452848. https://doi.org/10.1177/2053951714528481
Toplam 56 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Sosyolojik Metodoloji ve Araştırma Yöntemleri
Bölüm Research Articles
Yazarlar

Yaser Koyuncu 0000-0002-5379-3804

İlknur Yüksel-kaptanoğlu

Erken Görünüm Tarihi 26 Mayıs 2025
Yayımlanma Tarihi 31 Mayıs 2025
Gönderilme Tarihi 26 Nisan 2025
Kabul Tarihi 6 Mayıs 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 22 Sayı: 3

Kaynak Göster

APA Koyuncu, Y., & Yüksel-kaptanoğlu, İ. (2025). From Hearing to Doing: Tracing the Gaps in Familiarity, Competency, and Use of Big Data Among Türkiye’s Social Scientists. OPUS Journal of Society Research, 22(3), 368-390. https://doi.org/10.26466/opusjsr.1684496
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