(Un)making data PhD course

Aarhus University, Denmark

15 seats available 

R̶e̶g̶i̶s̶t̶e̶r̶ ̶b̶y̶ ̶M̶a̶r̶c̶h̶ ̶1̶5̶,̶ ̶2̶0̶1̶9̶ (registration now closed!)

Instructors: Professor Annette Markham and special Guest Professor Laura Ellingson, author of Engaging Crystallization as Qualitative Method, Voicing Survivorship, Embodiment in Qualitative Research  

Course Description

The term ‘data’ refers to many things–the representation of traces of human and nonhuman behaviors and experiences, isolated and observed as discrete objects. While not the only way to describe data, this conceptualization has become prominent in the so called digital age, information age, or internet age, for good reason. Our social situations are increasingly embedded in or saturated with digital and global networks of information flows. We leave traces everywhere when we connect to the internet. Massive amounts of information can be collected. Any of us who use the internet know that we are continually producing data that will be archived–by researchers, by marketers, by the companies who provide our devices, platforms, apps, and so forth. The information itself is microscopic and detailed. Whether produced deliberately or not, it is possible to archive these traces, transforming them to units of information that can be then combined with data that has been produced, archived, and transformed elsewhere.

Computation of large datasets can reveal interesting patterns and yield novel insights about human behavior. Perhaps because data is so plentiful, minuscule, and detailed, researchers can sometimes forget that it is not meaningful in itself. This mistake sometimes takes the form of assuming the the parts add up to the whole. Or conflating data with knowledge. Whatever the specific form of faulty reasoning, overvaluing the immediate meaning and truth value of data is a problem amplified by the size and number of datasets as well as the commonplace depiction of data as pre-existent and neutral.

For interpretive ethnographers, the term “data” has been problematized for decades because the word symbolically indicates an approach fundamentally opposed to inductive, immersive, and interpretive modes of inquiry. How should qualitative researchers respond to the recent tidal shifts toward datafication? How do we design studies when “data” becomes the predominant concept for giving shape or meaning to cultural materiality? We could simply refuse to use the term, since it does not fit well with the qualitative enterprise. Or we could try to replace ‘data’ with other terms. Neither option confronts the actual problem, which is not data itself, or the growth of computation as a way of knowing, but the rise (and reprise) of positivist frameworks and procedures.

This course focuses on the concept of data alongside the concepts of embodiment, affect, and situated knowing to explore contradictions and possibilities for qualitative or mixed method social research. It begins with the premise that concepts are always multiplicitous and ambiguous. They function as powerful guides, in that they shape and target our sensibilities, but also allow for specification within contexts. We ask participants to consider, “How do our concepts of data operate on our sensemaking practices?” By unpacking some of our epistemological frameworks and everyday practices, we hope to identify some of the radically different meanings operating in our theoretical frameworks, research design, and everyday activities. Once these dual levels are recognized–a process that requires conscious and critical self reflexivity, one can more strategically frame and use data in multiple and nuanced ways to add layers of meaning or augment to our analytical processes, while at the same time resist recent positivist ideologies of datafication.

We also revive the autoethnographic focus on the researcher’s role in the process of making data. We situate autoethnography as a natural part of research as an embodied, lived practice. Understanding, focusing on, and valuing the role of the researcher in generating accounts of phenomena is a hallmark of contemporary interpretive traditions of ethnographic studies. Thus, the course also focuses on how autoethnographic sensibilities can be a strong counterpoint to data-centered orientations, a key element in building ethically sensitive, situated knowledge.

Literature

Please contact course organizer for final literature list. Meanwhile, participants would be well prepared if they read or even browsed the following books and articles:

Adams, T., Holman Jones, S., & Ellis, C. (2014). Autoethnography. Understanding Qualitative Research. Oxford: Oxford University Press.

Cassell, J. (2002). Perturbing the system: “Soft science,” “hard science,” social science, the anxiety and madness of method. Human Organization, 61(2), 177-185.

Ellingson, L. L. (2017). Embodiment in qualitative research. London: Routledge.

Ellingson, L. L. (2009). Engaging crystallization in qualitative research: An introduction. Thousand Oaks, CA: Sage.

Ellingson, L. L. (1998). “Then you know how I feel”: Empathy, identification, and reflexivity in fieldwork. Qualitative Inquiry, 4, 492-514.

Markham, A. N. (2017). Troubling the concept of data in digital qualitative research. In Flick, U. (Ed.). Handbook of Qualitative Data Collection (511-523). London: Sage.

Markham, A. N. (2013). Fieldwork in social media: What would Malinowski do? Qualitative Communication Research, 2(4), 434-446.

Markham, A. N. (2013).  Undermining ‘data’: A critical examination of a core term in scientific inquiry. First Monday, 18(10). doi:10.5210/fm.v18i10.4868.

Wolf, M. (1991). A thrice told tale: Feminism, postmodernism, and ethnographic responsibility. Palo Alto: Stanford University Press.

Lecturers

Laura Ellingson, Professor of Communication and Women’s & Gender Studies, Santa Clara University, United States. 

Professor Annette Markham, Professor MSO of Information Studies, Aarhus University, Denmark