Data CondensationData condensation

Data Condensation
Data condensation refers to the process of selecting, focusing, simplifying,
abstracting, and/or transforming the data that appear in the full corpus (body) of
written- up fild notes, interview transcripts, documents, and other empirical materials.
By condensing, we’re making data stronger. (We stay away from data reduction as a term
because that implies we’re weakening or losing something in the process.)
As we see it, data condensation occurs continuously throughout the life of any
qualitatively oriented project. Even before the data are actually collected, anticipatory
data condensation is occurring as the researcher decides (often without full awareness)
which conceptual framework, which cases, which research questions, and which
data collection approaches to choose. As data collection proceeds, further episodes of
data condensation occur: writing summaries, coding, developing themes, generating
categories, and writing analytic memos. The data condensing/transforming process
continues after the fildwork is over, until a fial report is completed.
Data condensation is not something separate from analysis. It is a part of analysis.
The researcher’s decisions—which data chunks to code and which to pull out, which
category labels best summarize a number of chunks, which evolving story to tell—are
all analytic choices. Data condensation is a form of analysis that sharpens, sorts, focuses,
discards, and organizes data in such a way that “fial” conclusions can be drawn and
verifid.
By data condensation, we do not necessarily mean quantifiation. Qualitative data
can be transformed in many ways: through selection, through summary or paraphrase,
through being subsumed in a larger pattern, and so on. Occasionally, it may be helpful
to convert the data into magnitudes (e.g., the analyst decides that the program being
looked at has a “high” or “low” degree of effectiveness), but this is not always necessary.