Cluster Random Sampling - Explained

Cluster and Multistage Sampling

Cluster sampling is the sampling method where different groups within a population are used as a sample. This is different from stratified sampling in that you will use the entire group, or cluster, as a sample rather than a randomly selected member of all groups.

Sometimes stratifying isn’t practical and simple random sampling is difficult. Splitting the population into similar parts or clusters can make sampling more practical. Then we could select one or a few clusters at random and perform a census within each of them. This sampling design is called cluster sampling. If each cluster fairly represents the full population, cluster sampling will give us an unbiased sample. Cluster sampling is not the same as stratified sampling. We stratify to ensure that our sample represents different groups in the population, and sample randomly within each stratum.

Following steps are included in cluster random sampling:-

STEPS IN CLUSTER RANDOM SAMPLING:

1.         Identification of population

                        In order to do cluster random sampling, first we have to identify the population. In this step we choose a proper population for our sampling and then define our population in terms of our sampling purposes. It is to be assured that the population has a proper identification and definition, otherwise we may face some errors in our output results.

For example we select a country (say Pakistan) as our population to do a survey by the students about education system. All the students from all over Pakistan will be a population.

2.         Determining desired sample size

            Determining sample size is a key step in sampling. Sample size must not be such bigger that it contains almost all the population units, and not much smaller that it could not represent the whole population properly. Sample size must be 1/3 of the population.

3.         Identification of logical cluster

                  Clusters are more or less alike, each heterogeneous and resembling the overall population. In order to make sampling more effective & accurate, we divide  the sampling frame into different clusters. Clusters are heterogeneous internally & homogeneous as compare to other clusters. As only a sample of clusters are sampled, the ones selected need to represent the ones unselected; this is best done when the clusters are as internally heterogeneous in the survey variables as possible. We then identify & define a logical cluster in belief that this will represent all the clusters of the population.

4.         Listing all clusters

                        After making clusters from the population, all clusters are listed to make up

population of the clusters. For example Lahore, Islamabad, Multan, Karachi & Faisal Abad have been made as clusters, then the all clusters will be listed and a population will be made by studying the all listed clusters.

5.         Estimating of population numbers

                        Population can’t be predicted as accurate before sampling, however the population is estimated on the basis of clusters. In order to find out the population of the clusters, the average number of population members per cluster is taken. In order to find average, the population of a single cluster is divided by the all clusters population.

6.         Determining the number of clusters

            After estimating the average number of population members per cluster, we determine the number of clusters needed by dividing the sample size by the estimated size of a cluster. In this step we choose, how much clusters will be included in sampling process. Numbers of clusters are selected to make sure that those clusters will represent the other population of their area. Number of cluster must not be larger & not be much smaller.

7.         Selecting the clusters randomly

      We select clusters to make sampling more practical or affordable. Selecting the cluster is a main step included in sampling. After the determination of number of clusters to select, the next step is to select needed numbers of clusters from all the list of clusters. Clusters are selected randomly without any biasness. Clusters can be selected randomly by using any method of selecting randomly, which are random table, bowl method & lucky draw method.

8.         Including all members in population of cluster

            After selecting the clusters randomly, the next step is to study all the members of the population of those clusters. The sample study included in all population members in each selected cluster.  Within the cluster each & every member of that cluster is studies completely without missing any single member.

Stratification Vs. Clustering

 

Stratification:

1.     In stratification, population is divided into groups which are different from each others. For example, sexes, ages, races.

2.      In stratification, sample is selected randomly from each group.

3.      Stratification has less error as compare to simple random sampling.

4.      Stratification has more cost & is expensive when obtaining stratification information before the sampling

Clustering:

1.      In clustering, population is divided into groups which are comparable to each others. For example, Schools, Cities.

2.      In clustering, sample is selected randomly from some of the groups, not from each group.

3.      Clustering has more error as compare to simple random sampling.

4.      Clustering has less cost, as some areas or organizations are used as sample, which is less expensive.