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RESEARCH METHOD COHEN ok

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4 Sampling<br />

Introduction<br />

The quality of a piece of research stands or falls<br />

not only by the appropriateness of methodology<br />

and instrumentation but also by the suitability<br />

of the sampling strategy that has been adopted<br />

(see also Morrison 1993: 112–17). Questions of<br />

sampling arise directly out of the issue of defining<br />

the population on which the research will focus.<br />

Researchers must take sampling decisions early in<br />

the overall planning of a piece of research. Factors<br />

such as expense, time, accessibility frequently<br />

prevent researchers from gaining information from<br />

the whole population. Therefore they often need<br />

to be able to obtain data from a smaller group or<br />

subset of the total population in such a way that<br />

the knowledge gained is representative of the total<br />

population (however defined) under study. This<br />

smaller group or subset is the sample. Experienced<br />

researchers start with the total population and<br />

work down to the sample. By contrast, less<br />

experienced researchers often work from the<br />

bottom up, that is, they determine the minimum<br />

number of respondents needed to conduct the<br />

research (Bailey 1978). However, unless they<br />

identify the total population in advance, it<br />

is virtually impossible for them to assess how<br />

representative the sample is that they have drawn.<br />

Suppose that a class teacher has been released<br />

from her teaching commitments for one month in<br />

order to conduct some research into the abilities of<br />

13-year-old students to undertake a set of science<br />

experiments; that the research is to draw on<br />

three secondary schools which contain 300 such<br />

students each, a total of 900 students, and that<br />

the method that the teacher has been asked to use<br />

for data collection is a semi-structured interview.<br />

Because of the time available to the teacher it<br />

would be impossible for her to interview all 900<br />

students (the total population being all the cases).<br />

Therefore she has to be selective and to interview<br />

fewer than all 900 students. How will she decide<br />

that selection; how will she select which students<br />

to interview<br />

If she were to interview 200 of the students,<br />

would that be too many If she were to interview<br />

just 20 of the students would that be too few If she<br />

were to interview just the males or just the females,<br />

would that give her a fair picture If she were to<br />

interview just those students whom the science<br />

teachers had decided were ‘good at science’, would<br />

that yield a true picture of the total population of<br />

900 students Perhaps it would be better for her to<br />

interview those students who were experiencing<br />

difficulty in science and who did not enjoy science,<br />

as well as those who were ‘good at science’. Suppose<br />

that she turns up on the days of the interviews only<br />

to find that those students who do not enjoy science<br />

have decided to absent themselves from the<br />

science lesson. How can she reach those students<br />

Decisions and problems such as these face<br />

researchers in deciding the sampling strategy to<br />

be used. Judgements have to be made about four<br />

key factors in sampling:<br />

<br />

the sample size<br />

representativeness and parameters of the<br />

sample<br />

access to the sample<br />

the sampling strategy to be used.<br />

The decisions here will determine the sampling<br />

strategy to be used (see http://www.routledge.<br />

com/textbo<strong>ok</strong>s/9780415368780 – Chapter 4, file<br />

4.1.ppt). This assumes that a sample is actually<br />

required; there may be occasions on which<br />

the researcher can access the whole population<br />

rather than a sample.

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