a. Simple random sampling

Simple random sampling requires a researcher to have a list of all the members' in a population and allocate them numbers. The subjects are then selected from the list randomly by the use of a random numbers approach.

This method is applicable when the members of a population are homogenous.

Advantages

1. Using this method, the researcher is able to get a good representation of the population.

2. Simple random sampling is free from bias and allows the researcher to easily detect errors

3. The method is simple to use as the researcher does not require to have much knowledge about the population

Disadvantages

1. The process is time-consuming and tedious especially when a big sample is required.

2. This method is suitable for a population with homogenous units and therefore cannot be applied to a population with heterogeneous units.

3. The selection of the sample may not be accurate especially for widely dispersed populations if the researcher fails to include all members of the list.

b. Systematic random sampling

Under systematic sampling, a list is also required and the researcher will select the units to form a sample at regular intervals. The researcher sets a random start point and decides the sampling interval to apply. That is every nth member in the list will be included in the sample. For instance, in a list of employees, the researcher may select one at intervals of ten units.

This method is used when the units of the population are similar

Advantages

1. It provides a high degree of control to researchers if compared with simple random sampling through the sample size selected.

2. Systematic random sampling is easier than stratification as the units do not have to be classified and therefore save on time.

Disadvantages

1. Systematic random sampling requires the researcher to acquire a close approximation of the population before they start to sample and this may be difficult to achieve.

2. This method is used on the basis that the units of a population are similar and therefore the researcher may fail to achieve the desired outcome if the units are not uniform.

3. A systematic random sampling approach may be affected by the hidden periodic traits of the population.

c. Stratified random sampling

Stratified random sampling involves classifying the units of the population into different strata. The units in each stratum should possess similar characteristics which then allows the researcher to pick a sample from each. The stratified sampling approach can be divided into proportionate stratified sampling and disproportionate stratified sampling

This method is employed in cases where the population is heterogeneous.

Advantages

1. It allows for a higher degree of representation for all the strata in the population.

2. Stratified Random Sampling provides better precision compared to simple random sampling

3. The high level of precision offered by this method increases the level of accuracy and allows the researcher to use a smaller sample therefore it is cost-effective.

4. This approach reduces the biases in selecting the samples.

Disadvantages

1. Classification of units in well-distinguished groups may be difficult if some of them possess overlapping characteristics.

2. Stratified Random Sampling can be a tedious and time-consuming process as it involves handling a lot of data.

d. Proportionate stratified sampling

This approach is a sub-classification of stratified sampling it requires that the sample from each stratum be proportional to the population. To achieve this the researcher will have to use an equal sampling fraction across all the strata.

Advantages

1. Better precision can be achieved as the samples are proportional to the random population.

Disadvantages

1. Proportionate stratified sampling may be time-consuming at times the researcher may have to re-interview the respondents or acquire new respondents to ensure the sample is proportionate and minimize any potential bias.

e. Disproportionate stratified sampling

This approach also falls under stratified sampling. However, unlike the proportionate stratified sampling, the researcher will not apply the same sampling fraction across all the strata.

It is used when some strata in the population are small but critical in a study that may be overlooked by other sampling methods. ng approaches are used.

Advantages

1. High level of precision if the sample fractions are allocated correctly.

Disadvantages

1. The representativeness of this sample depends on the sampling fraction of the researcher and it may provide skewed results.

f. Multistage cluster sampling

The cluster sampling technique involves dividing the population into groups These groups can then be divided further into different steps of the research and this forms a multi-stage cluster sampling. Multistage cluster sampling is applied when the population is too large and it would be impossible to research each individual.

Advantages

1. This approach is cost-effective and time-effective as the population is divided into smaller groups.

2. The multi-stage cluster approach allows the researcher to have flexibility in that the groups formed can be broken down until the desired data is collected.

Disadvantages

1. The selection process of the sample may have some sample bias as it is subjective depending on the researchers' desired outcome.

2. The samples acquired using this method are susceptible to a higher degree of sampling error.