Question

For the Cluster Random Sampling technique,

• give the steps that are required and identify the step(s) in which randomness plays a role for this technique.
• give at least one advantage, at least one disadvantage,
• give an example showing how randomization can help this sampling avoid bias.

Answer 1

Cluster sampling refers to a type of sampling method . With cluster sampling, the researcher divides the population into separate groups, called clusters. Then, a simple random sample of clusters is selected from the population. The researcher conducts his analysis on data from the sampled clusters.

STEPS :

I) Sample: Decide the target audience and also the size of the sample.

ii) Create and evaluate sampling frames: Create a sampling frame by using either an existing frame or creating a new one for the target audience. Evaluate frames on the basis of coverage and clustering and make adjustments accordingly. These groups will be varied considering the population which can be exclusive and comprehensive. Members of a sample are selected individually.

iii) Determine groups(CLUSTERS): Determine the number of groups by including the same average members in each group. Make sure each of these groups are distinct from one another.

iv) Select clusters: Choose clusters randomly for sampling. (STEP OF RANDOMNESS)

v) Geographic segmentation: Geographic segmentation is the most commonly used cluster sample.

vi) Sub-types: Cluster sampling is bifurcated into one-stage and multi-stage subtypes on the basis of the number of steps followed by researchers to form clusters.

One-stage sampling. All of the elements within selected clusters are included in the sample.

Two-stage sampling. A subset of elements within selected clusters is randomly selected for inclusion in the sample. In two-stage cluster sampling, instead of selecting all the elements of a cluster, only a handful of members are selected from each cluster by implementing systematic or simple random sampling. (step of RANDOMNESS)

Multiple Stage Cluster Sampling: For effective research to be conducted across multiple geographies, one needs to form complicated clusters that can be achieved only using multiple-stage cluster sampling technique.

ADVANTAGE : if travel costs between clusters are high, cluster sampling may be more cost-effective than the other methods.

DISADVANTAGE: Given equal sample sizes, cluster sampling usually provides less precision than either simple random sampling or stratified sampling.

HOW RANDOMIZATION HELPS TO AVOID SAMPLING BIAS WITH EXAMPLE :

In survey sampling, bias refers to the tendency of a sample statistic to systematically over- or under-estimate a population parameter.

Randomization is a procedure for sampling from a population in which (a) the selection of a sample unit is based on chance and (b) every element of the population has a known, non-zero probability of being selected. This helps produce representative samples by eliminating voluntary response bias and guarding against undercoverage bias.

EXAMPLE: A business owner is inclined towards exploring the statistical performance of her wheat industries which are spread across various parts of the U.S. Considering the number of wheat industries, number of employees per industry and work done from each industry, single-stage sampling would be time and cost consuming. This is when she decides to conduct two-stage sampling. The owner creates samples of employees belonging to different industries to form clusters and then divides it into the size or operation status of the industry. A two-level cluster sampling was formed on which other clustering techniques like simple random sampling were applied to proceed with the calculations.

So as randomization is applied finally it gives equal probability to each of the industries to be included in the sample thereby avoiding voluntary bias and various other biases.