In their effort to promote “theory-driven” research strategies and to caution against the naïve embrace of “data-driven” research strategies that seems to have culminated recently in a veritable “’data rush’ promising new insights” into almost anything, the authors of this paper “review” a “diverse selection of literature on” digital media research methodologies and the Big Data phenomenon as they provide “an overview of ongoing debates” in this realm while arguing ultimately for a pragmatic approach based on “established principles of empirical research” and “the importance of methodological rigor and careful research design” (Mahrt & Scharkow, 2013, pp. 26, 20, 21, 30).
Mahrt and Scharkow acknowledge the advent of the Internet and other technologies has enticed “social scientists from various fields” to utilize “the massive amounts of publicly available data about Internet users” and some scholars have enjoyed success in “giving insight into previously inaccessible subject matters” (Mahrt & Scharkow, 2013, p. 21). Still, the authors note, there are some “inherent disadvantages” with sourcing data from the Internet in general and also from particular sites such as social media sites or gaming platforms (Mahrt & Scharkow, 2013, p. 21, 25). One of the most commonly cited problems with sourcing publicly available data from social media sites or gaming platforms or Internet usage is “the problem of random sampling on which all statistical inference is based, remains largely unsolved” (Mahrt & Scharkow, 2013, p. 25). The data in Big Data essentially are “huge” amounts of data “’naturally’ created by Internet users,” “not indexed in any meaningful way,” and with no “comprehensive overview” available (Mahrt & Scharkow, 2013, p. 21).
While Mahrt and Scharkow mention the positive attitude of “commercial researchers” toward a “golden future” for big data, they also mention the cautious attitude of academic researchers and explain how the “term Big Data has a relative meaning” (Mahrt & Scharkow, 2013, pp. 22, 25) contingent perhaps in part on these different attitudes. And although Mahrt and Scharkow imply most professionals would agree the big data concept “denotes bigger and bigger data sets over time,” they explain also how “in computer science” researchers emphasize the concept “refers to data sets that are too big” to manage with “regular storage and processing infrastructures” (Mahrt & Scharkow, 2013, p. 22). This emphasis on data volume and data management infrastructure familiar to computer scientists may seem to some researchers in “the social sciences and humanities as well as applied fields in business” too narrowly focused on computational or quantitative methods and this focus may seem exclusive and controversial in additional ways (Mahrt & Scharkow, 2013, pp. 22-23). Some of these additional controversies revolve around issues such as, for example, whether a “data analysis divide” may be developing that favors those with “the necessary analytical training and tools” over those without them (Mahrt & Scharkow, 2013, pp. 22-23), or whether an overemphasis on “data analysis” may have contributed to the “assumption that advanced analytical techniques make theories obsolete in the research process,” as if the numbers, the “observed data,” no longer require human interpretation to clarify meaning or to identify contextual or other confounding factors that may undermine the quality of the research and raise “concerns about the validity and generalizability of the results” (Mahrt & Scharkow, 2013, pp. 23-25).
Although Mahrt and Scharkow grant advances in “computer-mediated communication,” “social media,” and other types of “digital media” may be “fueling methodological innovation” such as analysis of large-scale data sets – or so-called Big Data – and that the opportunity to participate is alluring to “social scientists” in many fields, the authors conclude their paper by citing Herring and others urging researchers to commit to “methodological training,” “to learn to ask meaningful questions,” and to continually “assess” whether collection and analysis of massive amounts of data is truly valuable in any specific research endeavor (Mahrt & Scharkow, 2013, p. 20, 29-30). The advantages of automated, big data research are numerous, as Mahrt and Scharkow concede, for instance “convenience” and “efficiency,” or the elimination of research obstacles such as “artificial settings” and “observation effects,” or the “visualization” of massive “patterns in human behavior” previously impossible to discover and render (Mahrt & Scharkow, 2013, pp. 24-25). With those advantages understood and granted, the author’s argument seems a reasonable reminder of the “established principles of empirical research” and of the occasional need to reaffirm the value of the tradition (Mahrt & Scharkow, 2013, p. 21).