Qualitative_data_analysis

nature; photograph preferred
but not essential. [Morag]
SLIM 34 year old female, 5′
8′ tall, reasonably nice
looking, seeks tall handsome
gent who likes eating out and
socialising Must have good
sense of humour and like
children. [Fiona]
WHAT IS QUALITATIVE ANALYSIS? 43
SCORPIO MALE tall, slim,
handsome and fun loving,
seeks good looking
professional female for nights
out, wild times, romance and
fun. Photo please. [Alistair]
Even in this small selection of ads there are some surprises. For example, Morag
tells us she’s single; we can presume that not many of those advertising in the
personal columns would tell us otherwise! On the other hand, Pat doesn’t tell us
whether s/he is male or female. Someone may be in for a shock. Perhaps gender
doesn’t matter to Pat, though most of the other advertisers seem to think it does!
Most of the information supplied by these erstwhile suppliants is qualitative;
some, such as age, is quantitative. Incidentally, this balance of information offered
in the personal columns mirrors that available in most other areas of social life. The
qualitative data gives us information about a whole range of ‘qualities’, such as
whether the individual is ‘sincere’, ‘sexy’, ‘fun-loving’ and so on. Much of this
information is straightforwardly descriptive: it allows us to form an idea of the
individual’s character and interests.
The personal ads are literally ‘unclassified’; but in order to choose a mate we can
sort the data according to relevant characteristics, i.e. we can classify it. The first
thing we might do is assign individuals to various categories, according to character,
interests or the like; for example, this one is ‘lonely’, that one ‘likes eating out’; this
one is ‘glamorous’, that one ‘likes nights out’. By sorting the information into
different categories, we can make comparisons between cases much more effectively.
If we want someone interested in sports, for example, we can identify all those who
like sports, and then compare them. Or we may want to discount all those who fall
within a particular category, for example, such as those who suggest a photo would
be appreciated. We may be interested in all those who belong to a particular
category or combination of categories, such as those who express interest in ‘possible
romance’. There is no obvious limit to the number of categories, and no reason why
they shouldn’t overlap. You can enjoy as many hobbies as you like, and if you like
‘fun nights out’ or ‘cosy nights in’ that certainly doesn’t preclude any other activities
(only hinted at) of which politeness prohibits mention. Few advertisers frankly
admit to an interest in sex!
We can picture categorization as a process of funnelling the data into relevant
categories for analysis (Figure 3.3). The data loses its original shape, but we gain by
organizing it in ways which are more useful for our analysis.