To produce an account, we have to search, select and summarise data. In QR we do not "impose" our ideas upon the data. But there is a difference between an open mind and empty head. Our problem is to find a "focus" without connecting us prematurely to a particular perspective and so foreclosing options for our analysis.
[Class debate on "Why are women underrepresented in Internet?"]

Questions to help find a focus:

- What kind of data are we analysing?
- How can we chartreuse this data?
- What are our analytic objectives?
- Why have we selected this data?
- How is the data representative / exceptional?
- Who wants to know? What do they want to know?

Resources to help find a focus:

a. Personal experience,
b. General culture,
c. Academic literature.

Finally we can consider how all these can be woven together.

Good analysis requires efficient management of one's data. Data must be recorded fully and accurately and in a format, which facilitates analysis. The computer can improve our efficiency in managing data especially when our data derives from a variety of sources, and our analysis has multiple foci.

Reading data is a process of interpreting what the data may mean. The aim of reading the data is to prepare the ground for analysis.
[E.Burke's phrase "to read without reflecting is like eating without digesting" is discussed in class]

Techniques for interactive reading of data and generating ideas:

1. Substantive:

a. Settings: describe setting and context.
b. Definitions: perceptions of situations and objects.
c. Processes: sequences, changes, transitions, turning points.
d. Activities: regular patterns of behaviour,
e. Events: specific happenings or incidents,
f. Strategies: how people get things done,
g. Relationships/structure: friendships, cliques, coalitions etc.

II. Transposition:

 Transposition involves asking "what if" type questions.
 ["If" statements from expert systems are given as example]

III. The interrogative quintet:

 Who? What? When? Where? Why?

IV. Making comparisons:

 Free associations.

V. Shifting focus:

VI. Shifting sequence:

Annotating data is a way of opening up the data, preparing the ground for a more systematic and thorough analysis. Even once
we have embarked on a more organised and disciplined analysis, through categorising and linking data, we may return again
and again to the freer and more creative mode of annotating. In this way we can continue to capture the impressions, insights
and intuitions which provide us with fresh perspectives and new directions for analysis.

Creating categories is both a conceptual and empirical challenge. Categories, which seem "fine" in theory, are no good if they cannot relate to a wider conceptual context.

We can try to create conceptual tools to classify and compare the important or essential features of the phenomena we are studyingŠ Abstraction is a means to greater clarity and precision in making comparisons, but we must also remember their origins and limitations.

Categories must have two aspects:
1. Internal aspect: they must be meaningful in relation to the data.
2. External aspect: they must be meaningful in relation to other categories.

Therefore, in generating categories we have to think systematically and logically as well as creatively.

In making comparisons it is helpful to distinguish between two forms of relation: objectives and events:

a. Substantial relations of connection and interaction
b. Purely formal relations of similarity and difference between things.

Categorization of the data requires a dialectic to develop between categories and data: one moves backwards and forwards between the two.

Resources for generating categories:

a. Inferences from the data,
b. Initial or emergent research questions,
c. Substantive, policy and theoretical issues,
d. Imagination, intuition and previous knowledge.

Questions about categories:

a. What do these categories mean?
b. Is this list sufficiently refined?
c. What about relationships between the categories?

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