Probability Sampling Approaches

Probability Sampling Approaches

Probability sampling approaches allow you to generalize to the full population, since it ensures that special random characteristics are likely to be distributed evenly across the units included and excluded in / from the sample. It therefore is likely to yield a less biased sample and the results could be said to apply to the full population (if the appropriate sample size was selected). Different kinds of probability sampling approaches are possible.

The figures below demonstrate the different approaches. Assume each number is a unique member of the population, assume that each group consists of discreet mutually exclusive members of the population (In columns) and assume that each cluster (delineated by a block) is a group of members in the same geographic area.

With simple random sampling the sample is selected from the whole population using a table of numbers. Note that this does not necessarily ensure balanced representation amongst different groups.

With stratified random sampling, a set number of participants from each group can be selected. Note that this does not necessarily ensure that the most economical approach is used. In the example some cases from almost all of the geographic clusters are included.

With cluster sampling, a set number of clusters are randomly selected (in this case 4) with a set number of randomly selected units within each cluster (in this case 5). Although this will be more economical in terms of fieldwork costs because travel to different clusters have been limited, it does not necessarily guarantee equal representation of groups.

With systematic sampling, a set pattern is systematically applied to select participants. In the case of the example above, every 11^th member of the population were selected. Note that it did not require a random table of numbers, but were still subject to the same limitations as the simple random sample.

Type of Probability Samples	When is it applicable	Drawbacks
Simple Random Sampling (I.e.randomly select 50 schools off a list with all schools in the country)	It is ideal for statistical purposes	· It may be difficult to achieve in practice · It requires a precise list of the whole population · It is costly to conduct as those sampled may be spread over a wide area.
Stratified Random Sampling (I.e. Randomly select 50 schools per strata such as province)	· It ensures better coverage of the population than simple random sampling. · It is administratively more convenient to stratify a sample – interviewers can be specifically trained to manage particular strata (e.g. age, gender, ethnic or language groups).	· Difficulty in identifying appropriate strata. · More complex to organize and analyse results
Cluster Sampling (I.e. split the schools in a province up in geographical clusters, select 10 clusters randomly, and then proceed to visit 20 schools within each cluster)	More cost effective in terms of travel, thereby producing a reduction in the overall cost	· Units in a cluster may be very similar and therefore are less likely to represent the whole population · Cluster sampling has a larger sampling error than simple random sampling.
Systematic Sampling (i.e. a set pattern is applied to the data set, e.g. every 11th member is selected)	It spreads the sample more uniformly over the population and is easier to conduct than simple random sampling.	The system may interact with a concealed pattern in the population.