The Qualitative Face of Big Data

Live Streaming and Ecologically Valid Observation of Decision-Making

  • Alexander Nicolai Wendt (Author)
    Ruprecht-Karls Universität Heidelberg

Identifiers (Article)

Abstract

The technological possibilities for new data sources in media psychology, such as online live recordings, called Live Streaming, are growing continuously. These sources do not only offer plentiful quantitative material but also a fairly new access to ecologically valid and unobtrusive observation of problem-solving and decision-making processes. However, to exploit these potentials, epistemological and methodological reflection should guide research. The availability of Big Data and naturally occurring data sets (NODS) allows to revise the historical controversies on the eligibility of self-description. Drawing on such reflections, media psychology can contribute to renovate well established research methods, such as think aloud protocols, in order to enhance their empirical claims. Apart from other confident attempts to improve these methods, phenomenology and ethnomethodology offer a fruitful account to develop innovative data sources for self-description. Yet, this approach does not support a recurrence of self-description’s previous application but proposes an epistemological shift towards more subtle observations. In order to convey the potentials of media psychology, the risk of repeating classical mistakes, such as introspectionism, have to be regarded. Beyond these fallacies, however, modern digital technology holds encouraging potentials which have already partly been sighted by video gaming research. Due to the similarity of digital environments to laboratory setups, there remains to be a continuity from offline to online research, from traditional data to Big Data. Nevertheless, a true advance into new possibilities requires understanding the qualitative meaning of such data.

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Published
2020-12-31
Language
English
Type, method or approach
text
Keywords
Live Streaming; phenomenology; ethnomethodology; Big Data; decision-making