Qualitative_data_analysis

PRODUCING AN ACCOUNT 269
To be subjective means to be biased, unreliable and irrational. Subjective
data imply opinion rather than fact, intuition rather than logic,
impression rather than confirmation (Patton 1980:336)
Those taking this view tend to equate objectivity with achieving distance from the
data through formal measurement and quantification, but as Patton goes on to
remark ‘distance does not guarantee objectivity, it merely guarantees distance’. The
problems of objectivity lie mainly in how we conceptualize data, and as I suggested
earlier, this issue arises at all levels of measurement. To quote Patton again:
‘numbers do not protect against bias, they merely disguise it’. This overstates the
case, however, as numbers can help reduce bias, though they are not a sufficient
protection against it, just as reliable measures may not be valid ones. As Shimahara
(1988) comments, validity and reliability of research are crucial to all social research
regardless of disciplines and the methods employed.
Finally, let us turn to the problems of representation. Even if my watch gives a
valid reading, this result can be generalized only to a particular population. The
‘right’ time in Edinburgh is not the same as the ‘right’ time in New York. In telling
the time, we take for granted the population to which we are referring—those that
live within the same time zone. But in producing an account, we need to consider
carefully to whom our account refers.
It is helpful to distinguish two aspects of ‘generalization’ which are sometimes
confused. The first involves the process of induction, whereby we infer a general
proposition on the basis of our empirical observations. Generalization in this sense
refers to the theoretical process of developing concepts and connections. The second
involves the process of applying our theory to a wider population. This refers to
ascertaining the empirical circumstances in which our theory may hold true. In both
cases, we ‘generalize’ on the basis of the available evidence; but in the first sense, we
infer a general statement about the data, and in the second, we apply that statement
beyond the data on which it is based (Figure 15.8).
To contrast these two aspects of generalization, compare problems of generalizing
about artistic stereotyping in Vincent’s letters and in Woody Allen’s humour. In our
analysis of Vincent’s letters, we used the evidence of Vincent’s moodiness and
volatile behaviour to infer a generalization about the use of artistic stereotypes. This
was generalization about the data, and to consider whether or not it is justified, we
have to examine the data on which it is based. For example, we might wonder
whether it is Vincent himself, as a specific historical individual, whose temperament
is being ridiculed, rather than that of artists in general. On the other hand, the
letters do refer to other artists, like Gauguin, Seurat and so on, who seem to behave
in a similar vein. On this basis, we may justify our generalization about artistic
stereotyping. The problems of generalizing about artistic stereotyping in Woody