Good question. The short answer in no. Multistage or cluster sampling create artificial dependencies in the data that need to be taken into account using multilevel or mixed design software. If you sample the data randomly, I think you can do a multilevel analysis using generalized least squares. I have not seen this point made before.
The answer is Random Sample
random sample is a big sample and convenience sample is small sample
simple random sample is to select the sample in random method but systematic random sample is to select the sample in particular sequence (ie 1st 11th 21st 31st etc.)• Simple random sample requires that each individual is separately selected but systematic random sample does not selected separately.• In simple random sampling, for each k, each sample of size k has equal probability of being selected as a sample but it is not so in systematic random sampling.
The main difference is that the way of selecting a sample Random sample purely on randomly selected sample,in random sample every objective has a an equal chance to get into sample but it may follow heterogeneous,to over come this problem we can use stratified Random Sample Here the difference is that random sample may follow heterogeneity and Stratified follows homogeneity
A random distribution is a random sample set displayed in the form of a bell curve. See random sample set.
to select a random sample you pick them at random
A random sample should be taken from an entire population.
A gross sample in an analytical procedure is a large, random sample that is collected from a bulk material or substance. It is representative of the entire lot and is used for further analysis and testing to ensure accurate results. Subsamples are typically taken from the gross sample for more detailed analysis in the laboratory.
at random to represent the population
random sample
Random Sample
a random friend put