Multi Stage Random Sampling - Explained

Multi Stage Random Sampling

Definition:

Multistage sampling refers to sampling plans where the sampling is carried out in stages. Using smaller and smaller unit at each stage.

Multi-stage sampling represents a more complicated form of cluster sampling in which larger clusters are further subdivided into smaller, more targeted groupings for the purposes of surveying. Despite its name, multi-stage sampling can in fact be easier to implement and can create a more representative sample of the population than a single sampling technique.

Why we do Multi Stage Random Sampling?

The prime stimulus for multi-stage sampling is administrative convenience. It is more flexible than one-stage sampling. It reduces to one-stage sampling, unless this is the best choice of sample size of subsample. We have chance of selecting smaller value which appears more efficient.

Steps When Conducting a Multi-Stage Random Sample

The steps in multi-stage sampling are as follows:

1.      Layout Primary Clusters

2.      Sample Randomly

3.      Layout Secondary Clusters

4.      Sample Randomly And so on…

Application of Multi-Stage Sampling: an Example

Contrary to its name, multi-stage sampling can be easy to apply in business studies. Application of this sampling method can be divided into four stages:

Choosing sampling frame, numbering each group with a unique number and selecting a small sample of relevant discrete groups.

Choosing a sampling frame of relevant discrete sub-groups. This should be done from relevant discrete groups selected in the previous stage.

Repeat the second stage above, if necessary.

Choosing the members of the sample group from the sub-groups using some variation of probability sampling.

Example:

Let’s illustrate the application of the stages above using a specific example.

Your research objective is to evaluate online spending patterns of households in the US through online questionnaires. You can form your sample group comprising 120 households in the following manner:

1.      Choose 6 states in the USA using simple random sampling (or any other probability sampling).

2.      Choose 4 districts within each state using systematic sampling method (or any other probability sampling).

3.      Choose 5 households from each district using simple random or systematic sampling methods. This will result in 120 households to be included in your sample group.

Advantages & Disadvantages of Multi-Stage Sampling

The advantages and disadvantages of multi-stage sampling are similar to those for cluster sampling.

Advantages of Multi-Stage Sampling:

        i.            Simplification

The main purpose of the creation and present-day use of multi-stage sampling is ti avoid the problems of randomly sampling from a population. This sampling procedure in essence is a way to reduce the population by cutting it up into smaller groups, which then can be the subject of random sampling.

      ii.            Flexibility

The multi-stage form of sampling is flexible in many senses. First, it allows researchers to employ random sampling or cluster sampling after the determination of groups. Second, researchers can employ multi-stage sampling indefinitely to break down groups and subgroups into smaller groups until the researcher reaches the desired type or size of groups.

    iii.            Convenient

Multi-stage sampling has a convenience of finding the sample survey as there are no restrictions on how researchers divide the population into groups/ This allows a large number of possibilities for methods of convenience, the maximization or minimization of variance or interpretability.

    iv.            More Accurate than Cluster Sampling

                      Multi-stage sampling is normally more accurate than cluster sampling for the same size sample.

Disadvantages of Multi-Stage Sampling:

        i.            Biasness

The flexibility of multi-stage sampling is a double-edged sword. Because of the lack of restrictions on the decision processes involved in choosing groups, multi-stage sampling has a level of subjectivity. Thus, there will always be questions as to whether the chosen groups were optimal.

      ii.            Lost Data

Due to the fact that multi-stage sampling cuts out portions of the population from the study, the study's findings can never be 100% representative of the population.

    iii.            Less Accurate than Simple Random Sampling

                                  Multi-stage sampling is not as accurate as Simple Random Sample if the sample is the same size.

    iv.            Greater Variability

It leads to greater variability of the estimates than any other method of sampling.

      v.            More testing is required

More testing is difficult to do as errors increase by increasing samples. It is likely to cause a large number of errors as it involves a process of divisions and sub-divisions of the various strata or clusters in different stages