stratified sampling advantages and disadvantages

Advantages. Stratified sampling designs can be either proportionate or disproportionate. When the population is heterogeneous and contains several different groups, some of which are related to the topic of the study . Free from researcher bias; beyond the influence of the researcher; produces a representative sample; Disadvantages. Disadvantages: Stratified Random Sampling requires more administrative works as compared with Simple Random Sampling. Stratified random sampling is a type of probability sampling technique [see our article Probability sampling if you do not know what probability sampling is]. [see our article, Sampling: The basics, if you are unsure about the terms unit, sample, strata and population]. Stratified random sampling intends to guarantee that the sample represents specific sub-groups or strata. This way is free from bias and representative Stratified sampling offers several advantages over simple random sampling. It is best suited for strata with varying characteristics because it can only … Disproportionate stratification provides for varying sample size for each stratum. All the individuals are … Time consuming and tedious . Stratified Random Sampling can be tedious and time consuming job to those who are not keen towards handling such data. A stratified sample can guard against an "unrepresentative" sample (e.g., an all-male … Cannot reflect all differences; complete representation is not possible; Evaluation. Accordingly, application of stratified sampling method involves dividing population into … Criteria used to allocate the strata points will determine whether the precision of the design is excellent or pitiable. Sample: Randomly selected individuals are taken from all the strata. It is sometimes hard to classify each kind of population into clearly distinguished classes. Stratified random sampling involves first dividing a population into subpopulations and then applying random sampling methods to each subpopulation to form a test group. Unlike the simple random sample and the systematic random sample, sometimes we are interested in particular strata (meaning groups) within the population (e.g., males vs. females; houses vs. apartments, etc.) Cluster Sampling Stratified sampling is a probability sampling method and a form of random sampling in which the population is divided into two or more groups (strata) according to one or more common attributes.. A stratified sample can provide greater precision than a simple random sample of the same size. Cluster sampling refers to a sampling method wherein the members of the population are selected at random, from naturally occurring groups called 'cluster'. A disadvantage is when researchers can’t classify every member of the population into a subgroup. Stratified Random Sampling. Stratified sampling is one, in which the population is divided into homogeneous segments, and then the sample is randomly taken from the segments. Because it provides greater precision, a stratified sample often requires a smaller sample, which saves money. As a result, there is a higher precision level which is magnified by a homogeneous population. With the stratified random sample, … Ensures a high degree of representativeness of all the strata or layers in the population . Advantages and Disadvantages. In proportionate sampling, the sample size is proportional to the stratum size. Less random than simple random sampling .

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