**a. Simple random sampling** :-

– Applicable when population is small, homogeneous & readily available.

– Each element of the frame thus has an equal probability of selection.

– Sampling schemes may be without replacement or with replacement.

__Advantages:__

• Estimates are easy to calculate.

• Freedom from the human bias

__Disadvantages:__

• If sampling frame large, this method impracticable.

• Minority subgroups of interest in population may not be present in sample in sufficient numbers for study.

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**b. Systemic sampling:-**

– Arranging the target population according to some ordering scheme (i.e. 1 to N)

– Decide on the n ( sample size) that you want or need

– Interval size will be K = N/n

– Randomly select an integer between 1-K

– Then take every k th unit as a sample

__Advantages:__

• Sample easy to select

• Suitable sampling frame can be identified easily

• Sample evenly spread over entire reference population

__Disadvantages:__

• Sample may be biased if hidden periodicity in population coincides with that of selection.

• Difficult to assess precision of estimate from one survey.

**c. Stratified sampling:-**

– If population is heterogeneous then this method is used.

– The items should be selected on the basis of simple random sampling from each stratum.

__Advantages__

• Adequate representation of minority subgroups of interest can be ensured by stratification

• Different sampling approaches can be applied to different strata.

__Disadvantages__

• Sampling frame of entire population has to be prepared separately for each stratum.

• In some cases (such as designs with a large number of strata, or those with a specified minimum sample size per group), stratified sampling can potentially require a larger sample than would other methods.

**d) Cluster Sampling:-**

– This method is useful if area of interest is big.

__Advantages__

• Reduces cost by concentrating surveys in selected clusters.

__Disadvantages__

• Less precise than random sampling

• There is also not as much information in ‘n’ observations within a cluster as there happens to be in ‘n’ randomly drawn observations.

e) Multistage sampling:-

– More complicated form of cluster sampling in which larger clusters are further subdivided into smaller, more targeted groupings for the purposes of surveying.