{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T18:39:57Z","timestamp":1774291197253,"version":"3.50.1"},"reference-count":34,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Big Data &amp; Society"],"published-print":{"date-parts":[[2022,1]]},"abstract":"<jats:p> Big data enables researchers to closely follow the behavior of large groups of individuals by using high-frequency digital traces. However, these digital traces often lack context, and it is not always clear what is measured. In contrast, data from ethnographic fieldwork follows a limited number of individuals but can provide the context often lacking from big data. Yet, there is an under-explored potential in combining ethnographic data with big data and other digital data sources. This paper presents ways that quantitative research designs can combine big data and ethnographic data and account for the synergies that such combinations can provide. We highlight the differences and similarities between ethnographic data and big data, focusing on the three dimensions: individuals, depth of information, and time. We outline how ethnographic data can validate big data by providing a \u201cground truth\u201d and complement it by giving a \u201cthick description.\u201d Further, we lay out ways that analysis carried out using big data could benefit from collaboration with ethnographers, and we discuss the potential within the fields of machine learning and causal inference. <\/jats:p>","DOI":"10.1177\/20539517211069893","type":"journal-article","created":{"date-parts":[[2022,2,9]],"date-time":"2022-02-09T16:08:43Z","timestamp":1644422923000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":23,"title":["Ethnographic data in the age of big data: How to compare and combine"],"prefix":"10.1177","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3057-5975","authenticated-orcid":false,"given":"Andreas","family":"Bjerre-Nielsen","sequence":"first","affiliation":[{"name":"Department of Copenhagen Economics, University of Copenhagen Faculty of Social Sciences, Copenhagen, Denmark"},{"name":"Copenhagen Centre for Social Data Science (SODAS), Copenhagen, Denmark"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6033-7129","authenticated-orcid":false,"given":"Kristoffer Lind","family":"Glavind","sequence":"additional","affiliation":[{"name":"Department of Copenhagen Economics, University of Copenhagen Faculty of Social Sciences, Copenhagen, Denmark"},{"name":"Copenhagen Centre for Social Data Science (SODAS), Copenhagen, Denmark"}]}],"member":"179","published-online":{"date-parts":[[2022,2,9]]},"reference":[{"key":"bibr1-20539517211069893","doi-asserted-by":"publisher","DOI":"10.2307\/2580667"},{"key":"bibr2-20539517211069893","doi-asserted-by":"publisher","DOI":"10.1177\/0081175015578740"},{"key":"bibr3-20539517211069893","unstructured":"Bernard HR (2017) Research Methods in Anthropology: Qualitative and Quantitative Approaches, chapter 13, pages 354\u2013356, 379\u2013381. Rowman & Littlefield."},{"key":"bibr4-20539517211069893","doi-asserted-by":"publisher","DOI":"10.1177\/0956797620956613"},{"key":"bibr5-20539517211069893","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2020258118"},{"key":"bibr6-20539517211069893","doi-asserted-by":"publisher","DOI":"10.1177\/2053951717736337"},{"key":"bibr7-20539517211069893","doi-asserted-by":"publisher","DOI":"10.1177\/2053951714543908"},{"key":"bibr8-20539517211069893","doi-asserted-by":"publisher","DOI":"10.1177\/2053951718765026"},{"key":"bibr9-20539517211069893","unstructured":"Brooker P (2021) Machine anthropology: A view from sociology. 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