Show In stratified sampling, a two-step process is followed to divide the population into subgroups or strata. As opposed, in cluster sampling initially a partition of study objects is made into mutually exclusive and collectively exhaustive subgroups, known as a cluster. thereafter a random sample of the cluster is chosen, based on simple random sampling. In this article excerpt, you can find all the differences between stratified and cluster sampling, so take a read. Content: Stratified Sampling Vs Cluster Sampling
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Definition of Stratified SamplingStratified sampling is a type of probability sampling, in which first of all the population is bifurcated into various mutually exclusive, homogeneous subgroups (strata), after that, a subject is selected randomly from each group (stratum), which are then combined to form a single sample. A stratum is nothing but a homogeneous subset of the population, and when all the stratum 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 strata should be collectively exhaustive so that no individual is left out and also non-overlapping because overlapping stratum may result in the increase in the selection chances of some population elements. The sub-types of stratified sampling are:
Definition of Cluster SamplingCluster sampling is defined as a sampling technique in which the population is divided into already existing groupings (clusters), and then a sample of the cluster is selected randomly from the population. The term cluster refers to a natural, but heterogeneous, intact grouping of the members of the population. The most common variables used in the clustering population are the geographical area, buildings, school, etc. Heterogeneity of the cluster is an important feature of an ideal cluster sample design. The types of cluster sampling are given below:
The differences between stratified and cluster sampling can be drawn clearly on the following grounds:
ConclusionTo end up the discussion, we can say that a preferable situation for stratified sampling is when the identicalness within an individual stratum and the strata mean to vary from each other. On the other hand, the standard situation for cluster sampling is when the diversity within clusters and the cluster should not vary from each other. Further, sampling errors can be reduced in stratified sampling if between-group differences among strata are increased, whereas the between-group differences among clusters should be minimised to reduce sampling errors in cluster sampling.
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In which of the following sampling methods are the individuals of a population subdivided into mutually exclusive and collectively exhaustive separate subpopulations with a common characteristic? Select one: a. systematic sampling b. simple random sampling c. cluster sampling d. stratified sampling Definition: Stratified sampling is a type of sampling method in which the total population is divided into smaller groups or strata to complete the sampling process. The strata is formed based on some common characteristics in the population data. After dividing the population into strata, the researcher randomly selects the sample proportionally. Description: Stratified sampling is a common sampling technique used by researchers when trying to draw conclusions from different sub-groups or strata. The strata or sub-groups should be different and the data should not overlap. While using stratified sampling, the researcher should use simple probability sampling. The population is divided into various subgroups such as age, gender, nationality, job profile, educational level etc. Stratified sampling is used when the researcher wants to understand the existing relationship between two groups. The researcher can represent even the smallest sub-group in the population. There are two types of stratified sampling – one is proportionate stratified random sampling and another is disproportionate stratified random sampling. In the proportionate random sampling, each stratum would have the same sampling fraction. For example, you have three sub-groups with a population size of 150, 200, 250 subjects in each subgroup respectively. Now, to make it proportionate, the researcher uses one specific fraction or a percentage to be applied on its subgroups of population. The sample for first group would be 150*0.5= 75, 200*0.5=100 and 250*0.5= 125. Here the constant factor is the proportion ration for each population subset.ADVERTISEMENT The only difference is the sampling fraction in the disproportionate stratified sampling technique. The researcher could use different fractions for various subgroups depending on the type of research or conclusion he wants to derive from the population. The only disadvantage to that is the fact that if the researcher lays too much emphasis on one subgroup, the result could be skewed. |