How is cluster sampling different from stratified sampling? Explain the difference in the between and within...

How is cluster sampling different from stratified sampling? Explain the difference in the between and within variance of clusters vs strata.

Expert Solution

In stratified sampling, the population is divided into homogeneous (based on some characteristic) sub-groups or strata, then random sampling is performed within each of these strata.

In cluster sampling, the population is divided into sub-groups or clusters, a sample of clusters is randomly chosen, then each element from these chosen clusters is taken as part of the sample.

Below has been given the difference between the cluster and stratified sampling

BASIS FOR COMPARISON	STRATIFIED SAMPLING	CLUSTER SAMPLING
Meaning	Stratified sampling is one, in which the population is divided into homogeneous segments, and then the sample is randomly taken from the segments.	Cluster sampling refers to a sampling method wherein the members of the population are selected at random, from naturally occurring groups called 'cluster'.
Sample	Randomly selected individuals are taken from all the strata.	All the individuals are taken from randomly selected clusters.
Selection of population elements	Individually	Collectively
Homogeneity	Within group	Between groups
Heterogeneity	Between groups	Within group
Bifurcation	Imposed by the researcher	Naturally occurring groups
Objective	To increase precision and representation.	To reduce cost and improve efficiency.

Stratified sampling wants the low variance with in strata & High variance between the strata

Whereas in cluster sampling wants high variance with in clusters & Low variance between the clusters

orchestra answered 2 years ago

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