3.4 Social surveys and questionnaires sampling unit – that is a member of the population to be studied. Dentists, males between 30 and 40 years of age, females who own their own businesses, people with one or more A-levels, can be defined without too many problems. However, other groups are not so easy – how would you define a semi-skilled manual worker or a person living in poverty? Sampling frame Once the research population has been defined, the sample is selected from a sampling frame – a list of members of the population to be studied. In some cases, an appropriate sampling frame is readily available – for example, the Electoral Register (a list of people registered to vote) for a study of voting behaviour. In other cases, researchers may have to rely on listings, such as the Postcode Address Finder (a list of addresses complied by the Post Office – used by the British Social Attitudes survey) or telephone directories, which may or may not be suitable for their purposes. And all listings have drawbacks – not everyone is included, they are often out of date, certain groups are likely to be under-represented – for example, the poor are less likely to appear in telephone directories, and younger people on Electoral Registers as they are less likely to have registered to vote. The design and composition of the sample will partly depend on the type of sample used. Some of the more common types are outlined below.
gender, ethnicity and class groups. For instance, if the sample frame is based on women in the UK, the researcher might divide the women into ethnic and class groups and then draw a random sample from each of these groups. This will be more likely to provide a representative sample of women. In practice, researchers will add strata they think are important to their research. The annual Crime Survey for England and Wales provides an example of a stratified random sample. It is a nationally representative sample of around 35 000 adults and 3 000 children aged 10 to 15. The strata are age, gender, and region ‘to ensure the sample reflects the profile of the general population’ (Crime in England and Wales, ONS, 2016). Quota samples A market researcher stands on a street corner looking for people to fill her quota. She has to find 20 women between the ages of 30 and 45 to answer a questionnaire on magazine readership. She fills her quota with the first 20 women passing by who 1) fit the required age group and 2) agree to answer her questions. The sample selection is not random – it is not randomly selected from a sampling frame. The researcher simply fills their quota from the first available bodies. This method is known as quota sampling.
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Types of sample Random samples A random sample gives every member of the sampling frame an equal chance of being selected. Every name is given a number and then a list of random numbers is used to select a sample. This avoids bias in selection. It prevents the researcher from selecting a sample which provides a result which fits their theory, supports their hypothesis, that gives them what they expect and what they hope to find. Random samples are not necessarily representative. For example, if the sample is intended to represent college students, it might include mostly female students. This can happen when the sample is randomly drawn even though females might make up only half the student population. Stratified random sampling offers a possible solution to the problem of representativeness. The sample frame is divided into groups or strata which reflect the general population – for example age,
Selecting people for a quota sample. With some reference to the picture, suggest why quota sampling is unlikely to produce a sample which is representative of the research population.
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