convenience, snowball and other non-random samples and the increasing amount of
population data available to us, the constraints of sampling theory are completely
irrelevant. It is also the case that the standard error/power theory of analysis is fatally
flawed in its own terms, even when used as intended (Gorard, 2010). The accounts of
hundreds of interviewees can be properly analysed as text, and the account of one
case study can properly involve numbers. The supposed link between scale and
paradigm is just an illusion.
The logic of analysis is similar
Another possible distinction between the q-word approaches is their method of
analysis. Qualitative work is supposed to be subjective and so closer to a social world
(Gergen & Gergen, 2000). Quantitative work is supposed to help us become
objective (Bradley & Schaefer, 1998). This distinction between quantitative and
qualitative analysis is exaggerated, largely because of widespread error by those who
do handle numbers (Gorard, 2010) and ignorance of the subjective and interpretivist
nature of numeric analysis by those who do not (Gorard, 2006). The similarities of
the underlying procedures used are remarkable (Onwuegbuzie and Leech, 2005). Few
analytical techniques are restricted by data gathering methods, input data, or by
sample size. Most methods of analysis use some form of number, such as ‘tend’,
‘most’, ‘some’, ‘all’, ‘none’, ‘few’ and so on (Gorard, 1997b). Whenever one talks of
things being ‘rare’, ‘typical’, ‘great’ or ‘related’ this is a numeric claim, and can only
be so substantiated, whether expressed verbally or in figures (Meehl, 1998).
Similarly, quantification does not consist of simply assigning numbers to things, but
of relating empirical relations to numeric relations (Nash, 2002). The numbers
themselves are only valuable insofar as their behaviour is an isomorph of the qualities
they are summarising. Statistical analysis is misunderstood by observers if they do
not consider also the social settings in which it takes place, and the role of
'qualitative' factors in reaching a conclusion (MacKenzie, 1999). Normal statistical
textbooks describe ideal procedures to follow, but several studies of actual behaviour
have observed different common practices among researchers. 'Producing a statistic
is a social enterprise' (Gephart, 1988, p.15), and the stages of selecting variables,
making observations, and coding the results, take place in everyday settings where
subjective influences arise. It would be dishonest to pretend otherwise.
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