Sampling

1. Sampling www.educationforum.co.uk

2. 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.

3. 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.

4. 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.

5. Types of Sampling • Systematic samplinginvolves 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

6. 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.

7. 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.

8. 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.

9. 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.

10. 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.