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Qualitative_data_analysis

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132 QUALITATIVE DATA ANALYSIS<br />

computer to retrieve it for you before you can look at it. Illustration 9.7 is an example<br />

of the information which might be held by the computer for our first <strong>data</strong>bit.<br />

Categorizing the <strong>data</strong> is anything but mechanical, for it requires a continual exercise<br />

of judgement on the part of the analyst. This judgement concerns not only how to<br />

categorize the <strong>data</strong> but also whether and how to modify categories in view of the<br />

decisions being made. As we encounter more <strong>data</strong> we can define our categories with<br />

greater precision. It is important to note and reflect upon decisions to assign—or not<br />

to assign—a category, especially where these decisions are problematic, and to use<br />

this as a basis for defining criteria for inclusion and exclusion more explicitly. Even<br />

an established category set is not cast in stone, but subject to continual modification<br />

and renewal through interaction with the <strong>data</strong>.<br />

ILLUSTRATION 9.7<br />

DATA STORED FOLLOWING CATEGORIZATION OF A<br />

DATABIT<br />

Index Will life never<br />

Databit Will life never treat me decently? I am wracked by despair! My<br />

head is pounding<br />

Categories Temperament Transposing Suffering<br />

Case Letter01<br />

DataRef1 Vincent<br />

DataRef2 Theo<br />

Date 19.1.91<br />

Analyst Ian Dey<br />

Text location Vincent’s letters Letter01 characters 1–80<br />

Comment ‘Suffering’ should involve ‘emotional release’ through ridicule<br />

etc.—does this <strong>data</strong>bit meet this criterion?<br />

We seem to have spent a surprisingly long time over one bit of <strong>data</strong>. However,<br />

the first stages of any initial categorization of the <strong>data</strong> are bound to be rather slow<br />

and tentative. It is a case of learning to walk before we can run. As we progress with<br />

categorizing, our decisions should become more confident and more consistent as<br />

categories are clarified, ambiguities are resolved and we encounter fewer surprises<br />

and anomalies within the <strong>data</strong>. This should improve considerably the speed and<br />

efficiency with which we can categorize the <strong>data</strong>.<br />

General decisions in assigning categories

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