http://bowland-files.lancs.ac.uk/monkey/ihe/linguistics/corpus3/3qual.htm
Qualitative vs Quantitative analysis
Corpus analysis can be broadly categorised as consisting of qualitative and quantitative
analysis. In this section we'll look at both types and see the pros and cons associated with
each. You should bear in mind that these two types of data analysis form different, but
not necessary incompatible perspectives on corpus data.
Qualitative analysis: Richness and Precision.
The aim of qualitative analysis is a complete, detailed description. No attempt is made to
assign frequencies to the linguistic features which are identified in the data, and rare
phenomena receives (or should receive) the same amount of attention as more frequent
phenomena. Qualitative analysis allows for fine distinctions to be drawn because it is not
necessary to shoehorn the data into a finite number of classifications. Ambiguities, which
are inherent in human language, can be recognised in the analysis. For example, the word
"red" could be used in a corpus to signify the colour red, or as a political cateogorisation
(e.g. socialism or communism). In a qualitative analysis both senses of red in the phrase
"the red flag" could be recognised.
The main disadvantage of qualitative approaches to corpus analysis is that their
findings can not be extended to wider populations with the same degree of
certainty that quantitative analyses can. This is because the findings of the
research are not tested to discover whether they are statistically significant or due
to chance.
Quantitative analysis: Statistically reliable and generalisable results.
In quantitative research we classify features, count them, and even construct more
complex statistical models in an attempt to explain what is observed. Findings can be
generalised to a larger population, and direct comparisons can be made between two
corpora, so long as valid sampling and significance techniques have been used. Thus,
quantitative analysis allows us to discover which phenomena are likely to be genuine
reflections of the behaviour of a language or variety, and which are merely chance
occurences. The more basic task of just looking at a single language variety allows one to
get a precise picture of the frequency and rarity of particular phenomena, and thus their
relative normality or abnomrality.
However, the picture of the data which emerges from quantitative analysis is less
rich than that obtained from qualitative analysis. For statistical purposes,
classifications have to be of the hard-and-fast (so-called "Aristotelian" type). An
item either belongs to class x or it doesn't. So in the above example about the
phrase "the red flag" we would have to decide whether to classify "red" as
"politics" or "colour". As can be seen, many linguistic terms and phenomena do not
therefore belong to simple, single categories: rather they are more consistent with
the recent notion of "fuzzy sets" as in the red example. Quantatitive analysis is
therefore an idealisation of the data in some cases. Also, quantatitve analysis
tends to sideline rare occurences. To ensure that certain statistical tests (such as
chi-squared) provide reliable results, it is essential that minimum frequencies are
obtained - meaning that categories may have to be collapsed into one another
resulting in a loss of data richness.
A recent trend
From this brief discussion it can be appreciated that both qualitative and quantitative
analyses have something to contribute to corpus study. There has been a recent move in
social science towards multi-method approaches which tend to reject the narrow
analytical paradigms in favour of the breadth of information which the use of more than
one method may provide. In any case, as Schmied (1993) notes, a stage of qualitative
research is often a precursor for quantitative analysis, since before linguistic phenomena
can be classified and counted, the categories for classification must first be identified.
Schmied demonstrates that corpus linguistics could benefit as much as any field from
multi-method research.
Sumber :
http://www.usq.edu.au/library/help/postgrad/resmeth.htm
http://www.tardis.ed.ac.uk/~kate/qmcweb/q2.htm
Dealing with Problem Questions
Hypothetical questions
A hypothetical question is one in which you are asking respondents to indicate
what they think they would do under particular imaginary circumstances. These
can't always be avoided in some attitudinal research, but they are difficult to
administer and often give rise to unreliable answers
Presuming / leading questions
These are often included in poor questionnaires because the researcher feels
strongly about a topic and assumes that everyone will be of the same opinion.
Questions which rely on memory
Problems which tax the respondent's memory too much are likely to lead to nonresponse or inaccurate replies. For example "What did you have for lunch each day
last week?"
Questions requiring prior knowledge
For example, "What is your National Insurance number?"
Sensitive questions


Personal details / health / age
Income
If you have to ask sensitive questions, the problem can be alleviated somewhat by the use
of SHOW CARDS. Put all of the possible responses on a card, preferably mixed up, and
ask the respondent to indicate which number relates to their own circumstances. For
example,
Can you tell me the number on this card which corresponds to you income
group?
SHOW CARD WITH……
1.
2.
3.
4.
5.
6.
7.
£7,000 - 12,000
Over £60,000
£18,000 - £30,000
Under £7,000
£40,000 - £60,000
£12,000 - £18,000
£30,000 - £40,000
Mutually exclusive responses
In the show card above, you will note that somebody earning exactly £30,000
would perhaps wonder whether to give answer 3 or answer 7 on the show card. In
practice, people are usually able to give their income as an approximation. You
should, however, always watch out for questions where the multiple choice
answers are not mutually exclusive and where a respondent will be uncertain
about which category he/she falls under. It seems to be a particular problem with
age brackets, and you can often see examples of mistakes here in even
professionally produced surveys.
Long questions
If your questions are too long and detailed, the respondent may get lost and the responses
will relate only to the beginning or the end of the question. Where definitions and
qualifications are necessary, use show cards.