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Difference between stratified and cluster sampling

In our previous article, we have analyzed probabilistic and non-probabilistic sampling, in which we find types of probabilistic sampling, that is, stratified sampling and cluster sampling. In stratified sampling technique, the sample is created from the random selection of elements from all strata, while in cluster sampling, all units of randomly selected clusters form a sample.

In stratified sampling, a two-step process is followed to divide the population into subgroups or strata. In contrast, in group sampling, initially, a partition of the objects of study becomes mutually exclusive and collectively exhaustive subgroups, known as a group. thereafter a random sample is chosen from the group, based on a simple random sampling.

In this excerpt from the article, you can find all the differences between stratified sampling and clustering, therefore, read.

Comparative graph

Basis for comparison Stratified sampling Group sampling
Sense Stratified sampling is one in which the population is divided into homogeneous segments, and then the sample is taken randomly from the segments. Cluster sampling refers to a sampling method in which members of the population are randomly selected, from natural groups called "clusters."
Sample Randomly selected individuals are taken from all strata. All individuals are taken from randomly selected groups.
Selection of population elements. Individually Collectively
Homogeneity Within the group Between groups
Heterogeneity Between groups Within the group
Fork Tax by the researcher. Natural groups
objective To increase accuracy and representation. To reduce costs and improve efficiency.

Definition of stratified sampling

Stratified sampling is a type of probabilistic sampling, in which first the population forks into several mutually exclusive homogeneous sub-groups (strata), after that, a random subject is selected from each group (stratum), which is then combine to form a single sample. A stratum is nothing more than a homogeneous subset of the population, and when all strata are taken together, it is known as strata.

The common factors in which the population is separated are age, gender, income, race, religion, etc. An important point to remember is that the strata must be collectively exhaustive so that no individual is excluded and they do not overlap, since the overlapping stratum can result in an increase in the possibilities of selecting some elements of the population. The subtypes of stratified sampling are:

  • Proportional Stratified Sampling
  • Disproportionate stratified sampling

Cluster Sampling Definition

Cluster sampling is defined as a sampling technique in which the population is divided into existing clusters (clusters), and then a cluster sample is randomly selected from the population. The term group refers to a natural, but heterogeneous, intact group of members of the population.

The most common variables used in the grouping population are the geographical area, the buildings, the school, etc. Cluster heterogeneity is an important characteristic of an ideal cluster sample design. Cluster sampling types are given below:

  • One-stage group sampling
  • Sampling in two-stage groups
  • Multi-stage group sampling

Key differences between stratified sampling and clustering

The differences between stratified sampling and cluster sampling can be clearly drawn for the following reasons:

  1. A probabilistic sampling procedure in which the population is separated into different homogeneous segments called "strata", and then the sample is chosen at random from each stratum, is called stratified sampling. Cluster sampling is a sampling technique in which units of the population are randomly selected from existing groups called "clusters."
  2. In stratified sampling, individuals are randomly selected from all strata, to constitute the sample. On the other hand, cluster sampling, the sample is formed when all individuals are taken from randomly selected groups.
  3. In cluster sampling, the elements of the population are selected in aggregates, however, in the case of stratified sampling, the elements of the population are selected individually from each stratum.
  4. In stratified sampling, there is homogeneity within the group, while in the case of cluster sampling, homogeneity is found between the groups.
  5. Heterogeneity occurs between groups in stratified sampling. In contrast, group members are heterogeneous in cluster sampling.
  6. When the sampling method adopted by the researcher is stratified, then he imposes the categories. In contrast, categories are already existing groups in cluster sampling.
  7. Stratified sampling aims to improve accuracy and representation. Unlike cluster sampling whose objective is to improve profitability and operational efficiency.


To conclude the discussion, we can say that a preferable situation for stratified sampling is when the identity within an individual stratum and the strata mean that they vary between them. On the other hand, the standard situation for cluster sampling is when the diversity within the clusters and the cluster should not vary between them.

In addition, sampling errors can be reduced in stratified sampling if differences between groups between strata increase, while differences between groups between groups must be minimized to reduce sampling errors in group sampling.