With Enough Data, the Numbers Speak for Themselves...
"Not a chance. The promoters of big data would like us to believe that behind the lines of code and vast databases lie objective and universal insights into patterns of human behavior, be it consumer spending, criminal or terrorist acts, healthy habits, or employee productivity. But many big–data evangelists avoid taking a hard look at the weaknesses. Numbers can't speak for themselves, and data sets –– no matter their scale –– are still objects of human design. The tools of big–data science, such as the Apache Hadoop software framework, do not immunize us from skews, gaps, and faulty assumptions. Those factors are particularly significant when big data tries to reflect the social world we live in, yet we can often be fooled into thinking that the results are somehow more objective than human opinions. Biases and blind spots exist in big data as much as they do in individual perceptions and experiences. Yet there is a problematic belief that bigger data is always better data and that correlation is as good as causation."
(Kate Crawford, 12 May 2013, Foreign Policy)
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Simon PerkinsSHARE
Complex representations not simple quantified measurement
"Primarily because of its association with achievements in the physical sciences, quantified measurement seems a step toward enhanced precision. But, precision, as understood here, means more than reliability and validity; it also requires appropriately complex representation of the target construct. In phenomenological terms, precision refers to the distinctiveness that fosters reliability, the coherence that assures validity, and the richness that is appropriate to the targeted phenomenon. First, distinctiveness is the extent to which a phenomenon is discriminable from others. Judgments about distinctiveness require more than explicit (e.g., operational) definitions. They require the capacity to anticipate attributes that remain implicit in even the most explicitly conceived phenomenon and, on the basis of those implicit meanings, to consistently verify that phenomenon's presence or absence. Second, coherence is the extent to which judgments about the attribute structure of a particular phenomenon are congruent. Short of logical entailment but beyond associative contingency, judgments about coherence require consideration of both the explicit and implicit meanings of the attribute structure they describe. Third, richness is the extent to which judgments about a phenomenon capture its complexity and intricacy. Richness entails full differentiation of a phenomenon's attributes, identification of its attribute structure, and appreciation of its structural incongruities."
(Don Kuiken and David Miall, 2001)
[4] profiles and the ideal prototype. This numeric assessment of degree involves profiles of attributes rather than individual attributes. Although we appreciate the potential importance of the latter (see note 3), we have not attempted to address the analytic problems that arise from the combination of nominal and ordinal variables in estimates of profile similarity. It should be noted, however, that some available software facilitates the assessment of ordinal information during attribute identification (cf. KUCKARTZ 1995; WEITZMAN & MILES 1995). The possibility of coordinating ordinal and nominal attribute judgments deserves further consideration.
Kuiken, Don & Miall, David S. (2001). "Numerically Aided Phenomenology: Procedures for Investigating Categories of Experience." [68 paragraphs]. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research, 2(1), Art. 15, http://nbn–resolving.de/urn:nbn:de:0114– fqs0101153.
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Simon PerkinsSHARE
