SAMPLING THEORY 1. Random Sample Advantages:  All have an equal chance of being chosen.  This avoids subjective bias and human element. Disadvantages:  Practical use restricted to small populations.  Small samples may not be representative of the population. Therefore you need a large sample.  Costly and time consuming. 2. Systematic/ Quasi-random sample Less random than 1. E.g. choose every 10 th person. Widely used in the UK as it offers a ‘far more practical approximation to random sampling.’ A number of methods are used e.g. polling records, telephone books, trade association lists etc.

3. Stratified random-sampling This sample is divided up into segments or ‘strata’, often preferred as it makes the sample more representative of the whole group. For example if a business wished know how class affects demand for food. Put the population into classes then take a random sample from these. 4. Quota sampling Population is segmented and interviewers are given targets/quotas. Advantages Cheaper Faster Administrative efficiency Useful when population has definite segments. Disadvantages Sample is not randomly chosen. Selection bias.

5. Cluster sampling Put population into clusters (often via geographic areas then take a random sample from the cluster). Advantages Useful if population is widely spread Cheap and fast. Disadvantages May miss sections of the population if only have a few clusters. 6. Multi-stage sampling Random systematic method Choose country at random City District Street Household This method is bad when it is widely dispersed and is also very expensive 7. Snowballing Highly specialised; start with one individual or group and use these to contact others. Only used when sampling methods aren’t possible.