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# SAMPLING - PowerPoint PPT Presentation

SAMPLING. Sampling. Sampling is the process that a researcher uses to select people, places, things, signals or any other item of interest to study. We will look at ways to select a sample, and consider whether it is expected to be representative. The Population.

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## PowerPoint Slideshow about 'SAMPLING' - britain

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Presentation Transcript

### SAMPLING

• Sampling is the process that a researcher uses to select people, places, things, signals or any other item of interest to study.

• We will look at ways to select a sample, and consider whether it is expected to be representative

• The population is all the people or things or animals or signals etc that could be included in your study.

• The population is usually large and it is normally not feasible to survey every member

• A good sampling scheme aims to select a representative sample of the population

• This is a sample which is big enough and varied enough to allow you to generalise from the findings for the sample to a theory about the whole population

• The more representative your sample is, the more realistic your generalisations will be.

• As a start you need to know some basic information about your population.

• The more you know, the better you can pick your sample.

• The greater the diversity in your population the bigger your sample will need to be to be representative.

• The sampling frame is the database you use to select your sample.

• It might be, for example, a set of experimental results, a telephone directory, the electoral register, a list of members of an organisation….

• Sometimes you may need to make specific exclusions from your sampling frame.

• For instance in a study of normal lung function it may be necessary to exclude all smokers.

There are two broad classes of sampling method:

• Probabilistic used when youwant to make predictions about the population (,ost common)

• Non-probabilistic used when you want to make a case study

• The aim in probabilistic sampling is to select a random sample.

• In a random sample, each member of the population has an equal chance of being selected

• We will consider 4 types of random sampling: simple, stratified, systematic and cluster sampling

Simple Random Sampling

• Take a list (sample frame) of all the members of the population and give each member a number

• Decide the number of members that will form your sample (e.g.20% of population)

• Generate a list of random numbers of equal length to the number of members you want to sample

• Pick from your list the members that have numbers corresponding to your random number list

Stratified Random Sampling

• In this method the population is divided into subgroups (e.g. by gender, ethnicity, age etc) and a simple random sample is then taken from each sub-group

• The aim is to be sure your sample has members from all the key subgroups in the population

Systematic Random Sampling

• In Systematic random sampling, the first member of the population is selected from the sample frame at random. Subsequently members are selected at every fixed interval e.g. every 20th person.

• If you have a very large population (> 100,000 members) and want a fairly small sample this may be more efficient than simple random sampling.

• You need to keep your interval fixed and use the whole list

• There is a small probability that your sample will be unrepresentative of the population if you use this method.

Cluster (area) Random Sampling

• If you have a population that is spread very widely in a geographical sense (e.g. all teenagers in the UK) it can require a lot of effort to contact your random sample.

• Cluster sampling selects the sample from existing clusters (such as all teenagers in Kent) which are thought to be representative. This may lead to a biased sample if the clusters are not in fact representative of the poluation.

• The aim here is to look at a representative group e.g. for a case study or sometimes to test a hypothesis or law.

• There is no attempt to make a random selection

• We will consider 4 types of non-probabilistic sampling: quota, convenience, purposive and snowball sampling

Quota sampling

• Subjects are selected non-randomly according to some pre-determined quota.

• First the population is broken down into sub-groups (as for stratified random sampling) and the number to be sampled from each group is determined

• Unlike stratified random sampling there is no attempt to randomise the selection within groups – researchers contact members of the sub-group until the quota is filled and then stop.

• The method is often used by market researchers and for opinion polls.

Convenience sampling

• This method uses the “easiest to reach” sample such as volunteers, people passing in the street etc

• For a convenience sample there will be no evidence that it is in any way representative of the population

Purposive sampling

• A members of the population are targeted because they are believed to be typical or in some way “average” or because they have some special, known attribute (e.g. people previously known to have voted for a particular political party, items made on a production line known to break down frequently etc)

• A purposive sample is not meant to be representative of a wider population but can be useful for exploratory studies.

Snowball sampling

• In this method the first members of the sample are identified.

• Subsequent members of the sample come by recommendation or identification by the first members.

• This does not guarantee a representative sample, but it can be the best method when the subject of research is sensitive or relates to a population that is hard to contact (e.g people engaged in social security fraud).

• Sampling is a means of selecting part of a population for study.

• The more representative the sample, the more valid your generalisations to the population will be.

• Sampling can be probabilistic – normally gives a representative sample - or non-probabilistic – not normally representative but may be a pragmatic choice.