Qualitative_data_analysis

Databits Categories
5. Now she is upset because it won’t fit Suffering
in her mouth!
6. She is so bourgeois and stupid, I want Temperament
to smash her
7. I tried forcing the false plate in but it Task Suffering
sticks out like a star burst chandelier
8. Still, I find it beautiful. She claims she Task Suffering
can’t chew! What do I care whether she
can chew or not!
9. Theo, I can’t go on like this much Temperament
longer!
10. I asked Cézanne if he would share an Incongruity Suffering
office with me but he is old and infirm
and unable to hold the instruments and
they must be tied to his wrists but then
he lacks accuracy and once inside a
mouth, he knocks out more teeth than he
saves.
11. What to do? Not assigned.
ASSIGNING CATEGORIES 135
specific criterion or set of criteria, the latter becoming increasingly explicit. The
extracted data can now be inspected in detail, with a view to making further
distinctions within the data or between these and other data. Secondly, this first
categorization produces some ideas about whether and how to subcategorize the
data. Finally, we should have clarified the boundaries between categories, and begun
to develop some ideas about possible connections between them. Therefore
categorization not only involves producing the data in a format convenient for further
analysis; it can also contribute materially to the ideas needed to develop further
comparisons both within and between categories.
Categorizing data is a powerful tool for organizing our analysis, both conceptually
and empirically. Prior to categorization, the data is organized through our methods
of collection and transcription rather than the ideas and objectives which inform
our analysis. Once the data is categorized, we can examine and explore the data in
our own terms. There is an irony inherent in this process, for in order to compare
data within or between categories, we have to abstract the data from the context in
which it is located. Without abstraction, comparison is not possible. And yet one of
the most powerful injunctions of qualitative analysis is that data should be analysed
in context. How can these contradictory requirements be reconciled? The computer
provides a partial reconciliation, by allowing us to retain direct access to the context
from which the data has been abstracted. Thus we can compare all the databits