**Sampling 4 : How big should a sample be?**

**Delia Chiaro**

Among the most
frequently asked questions raised by students starting out on research within
the ESP we will surely find “How many people do I need to interview?” or “How
many questionnaires do I need to administer?” which can be summed up in “How
big should my sample be?” And these questions certainly mirror the equally
frequent set of queries which can be summed up in “But you only have only
interviewed 200 people, what about the other 58, 999 million
Italians/Brits/Spaniards etc.”. Well, first of all,
let us not forget that a survey is not a census. In other words surveys do not
set out to assess *the whole population*
but just a representative sample of that population. I am using the term
“population” in a very particular way. In empirical research the term
population refers to a group of interest. In other words, a population can
indeed be made up of
people living in the same country or who belong to the same
ethnic group, but it can also consist of people of the same profession, of the
same generation, with the same interests or with just about any common
denominator we can think of which will be relevant to what we are researching.

Having said
that, how can we be sure that say, the opinions of 1,500 respondents normally
interviewed in surveys by professional polling associations in the

Having said
that, another question raised by skeptics is the following “If 75% of my sample hold a particular view, what does this mean? Can I
say 75% of the whole group in which I am interested (for example, 75% of *all* German interpreters believes x) or
can I only say 75% *of those interpreters
who answered my questionnaire* believe x?” If we have collected our data
correctly, the choice of the right statistical test will lead us to infer
information gathered from our sample to the entire population. Exit polls during elections are a good
example of how, by interviewing a *certain
quota *of voters, chosen randomly across a nation, usually leads to pretty
accurate forecasts over and above those few respondents who typically are
jokers and/or liars.

Determining the
size of a sample can either be done through statistical techniques (the *Google* search engine provides thousands
of hits for software to determine sample size) or through what are known as *ad hoc* methods. The problem with
statistical techniques is that they do not take into account costs involved in
obtaining information, thus, in the field, an entirely
different approach tends to be adopted. What is known as an *ad hoc* method can be carried out when a
person knows from experience how to establish feasible sample size notwithstanding
budget constraints. Sudman suggest that a sample
should be large enough to be divided into groups of 100 or more, with
sub-groups of respondents of between 20 and 50, the assumption being that less
accuracy is needed in a sub-group. But what about students
working not only with budget constraints, but also with strict deadlines?
Can they realistically work in terms of triple figures? Surely
not.

Given that
students are normally involved in exploratory research, the commonsensical
answer is to guide them to work with manageable, convenient and realistic
samples. For a tentative investigation, according to mathematicians, 30 seems to be a magic number. In the type of
cross-cultural/language investigations typical in translation and interpreting,
this would lead to, say, two parallel samples of 30+30 (or multiples of 30); in
other words, small, manageable, but with which significant results could be
drawn with the use of the *right
statistical test – *and specific tests for small samples do exist. As long
as we are aware of the fact that our study is a small one and that it is *exploratory* in nature and not the final
word on the matter, such hypothetical studies would be perfectly respectable
examples of quantitative research.

References:

David A. Aaker; V. Kumar and George S.
Day. *Marketing Reseach *5^{th} Edition.
*1995*.

*Applied
Sampling*.