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WHAT IS SAMPLING?
A great deal of sociological research makes use of sampling. This is a technique
aiming to reduce the number of respondents in a piece of research, whilst
retaining - as accurately as possible - the characteristics of the whole group.
The purpose of taking a sample is to investigate features of the population in greater
detail than could be done if the total population was used, and to draw inferences
about this population. In addition, at the practical level, a sample is likely to be
both cheaper and quicker to investigate.
All sampling will involve error and sociologists have developed sampling techniques in
order to minimize this error. All methods of sampling make use of a sampling
frame.
WHAT IS A SAMPLING FRAME?
A sampling frame is the list of members of the total population of interest. From this
list a sample to study can be drawn. For example, such a list may be an electoral
register, if information about those with voting rights is sought, or the family
practitioner committee lists if a health survey is projected, or vehicle registration
lists, if car ownership or road transport is under study.
TYPES OF SAMPLING
The random sample
For inferences about a population to be valid, the sample must be truly
representative, the only way to ensure this is to take a Random sample. This
involves using either random numbers or systematic sampling. Random numbers
are used to ensure that every individual in a sampling frame has an equal chance
of being selected as a member of the sample.
TYPES OF SAMPLING
Systematic sampling involves randomly selecting the first individual from the list, then
subsequently individuals at every fixed interval, for example, every tenth person if
a 10% sample is desired.
Examples of random sampling include ERNIE, a telephone directory, out of a hat.
An example of systematic random sampling is Willmott and Young's sample of
Bethnal Green families.
Systematic sampling makes representativeness more probable
STRATIFIED SAMPLING
When the population to be studied is large and the sample relatively small it may be
efficient to use stratified sampling. Random sampling assumes that the list of the
population involved (sampling frame) has no particular order of characteristics,
which could have any bearing on the investigation. But in social research, most
sample frames are not of this type, but have a definite order. For example,
classroom research - school classes are already stratified and research then
often takes a sample from each point on the strata.
Stratified samples tend to have smaller sampling errors than random samples of the
same size. This is because the sample is divided into several groups in proportion
to their known prevalence in an attempt to construct a sample that is
representative of the whole.
QUOTA SAMPLING
This is a sampling method in which a sample is selected by quotas from each defined
portion of the population. The method does not fulfill the normal requirements of
random sampling. It involves breaking down the parent populations into strata
according to relevant features and calculating how many individuals to include in
each of these categories to reflect the parent population structure. Thus so far
this is the same as a stratified sample and randomness can still be achieved.
However, once the size of each of these groups is decided no attempt at randomness
is made. Instead, the interviewers are instructed to achieve appropriate
selections (quotas) to fulfill the requirements within each group.
Contacts are made until a quota is filled. Therefore, non-response cannot occur. The
interviewer makes the final choice of sample. However the choice is limited by
availability, and the diligence and honesty of the interviewer. The method is much
used by market research and opinion pollsters.
PANEL SAMPLING
This is a form of longitudinal study, but is usually of shorter duration and more
focused. It involves questioning the same sample at regular intervals to observe
trends of opinion. With this sort of sample, as opposed to cross sectional (one off)
studies, change over time can be monitored. A disadvantage is that respondents
are lost through death or lack of interest or moving and that those who remain
may become atypical through the very experience of being panel members.
CLUSTER SAMPLING
This is a method of sampling which selects from groups (clusters) already existing in
the parent population rather than assembling a random sample. This tends to be
quicker and cheaper, but may lead to a biased sample if the clusters are not
representative of the parent population. For example, polls taken of attitudes to
government policy may be carried out in selected areas of the country thought to
be representative, but because of local political dynamics this may not be the
case.
SNOWBALL SAMPLING
This is a method of selecting a sample by starting with a small selected group of
respondents and asking them for further contacts. This is not therefore a random
sample and no inferences about the characteristics of the parent population can
be made from such a study. Its use is primarily in the collection of in depth
qualitative data, perhaps on sensitive topics, where an obvious sampling frame
does not exist and the best method of selection is through personal contacts.
Such a method might be used in an investigation of sexual habits or bereavement
experiences.