ANALYZING DATA 203203PART III
ANALYZING DATA
In traditional presentations of research methods, the next phase of research is formulated as “data
analysis.” This formulation fits with a methodologically positivist perspective emphasizing the
successive “stages” of research, wherein propositions are deductively induced from prior theory,
concepts are operationalized and measured, quantitative data are “collected” with respect to those
measures, and then data analysis commences. This consists typically of analyses drawing on
statistics or, in the case of methodologically positivist qualitative research, using, for example,
necessary and sufficient logic (Ragin 2000a)—with all such procedures ultimately focused on
demonstrating statistically significant relationships (or the lack thereof) or causal mechanisms
and/or testing causal models. Conceived in this way, “data analysis” has excluded the kinds of
interpretive analytical processes presented here. “Analyzing data” is consistent with the herme-
neutic and phenomenological presuppositions of methodologically interpretive approaches—that
is, approaches that emphasize the iterative nature of knowledge and knowledge making—more
verb than noun—and their ties to the experiences of both researchers and researched.
Interpretive methods of analyzing data, as illustrated by the list in the book introduction (see
Table I-1, p. xx) are diverse, and their particular origins and developmental histories can be most
fully understood in terms of the reading habits and practices that structure fields of study as
epistemic-disciplinary communities. Given this diversity and historical complexity, we have not
endeavored to present the full array of analytic possibilities nor to give particularized histories for
each (although chapter 1 provides an overview of their philosophical commonalities). Still, the
chapters included in this section do give a sense of the wide range of topics and settings in which
phenomenological and hermeneutic ideas may be manifested and expressed.^1
As discussed in the introduction to part II, the demarcation between accessing-generating data
and analyzing them is useful for heuristic purposes as a way to discuss what are, in practice,
entwined processes. There are conceptually clear moments in which analysis is the main task
confronting the researcher: when, for example, she has left the field or the archives and is con-
fronted with a body of evidence—data in hand—generated through previous research decisions.
Such is the case, for example, in Steven Maynard-Moody and Michael Musheno’s chapter 18,
which recounts the strategies they used to access and generate stories but then focuses on the
analytical exchanges among the research team and the analytical processes they used to make
sense of the transcribed stories, informed by their field experiences as participant-observers. Clare
Ginger’s chapter 19 shares a similar structure, describing steps taken to access and generate data
but focusing primarily on steps in their analysis.