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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

Q: What is the difference between random and stratified sample in the survey method?

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In a stratified sample, the sampling proportion is the same for each stratum. In a random sample it should be but, due to randomness, need not be.

Sometimes a population consists of a number of subsets (strata) such that members within any particular strata are alike while difference between strata are more than simply random variations. In such a case, the population can be split up into strata. Then a stratified random sample consists of simple random samples, with the same sampling proportion, taken within each stratum.

There are many such methods: cluster sampling, stratified random sampling, simple random sampling.Their usefulness depends on the circumstances.

Stratified Random Sampling. Google it. .

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In a stratified sample, the sampling proportion is the same for each stratum. In a random sample it should be but, due to randomness, need not be.

Sometimes a population consists of a number of subsets (strata) such that members within any particular strata are alike while difference between strata are more than simply random variations. In such a case, the population can be split up into strata. Then a stratified random sample consists of simple random samples, with the same sampling proportion, taken within each stratum.

In (Simple) random sampling, all of the units in the sample have the same chance of being included in the sample. Units are selected randomly from a population by some random method that gives equal probability to each element. In stratified random sampling, the entire population is divided into heterogeneous sub-popuation known as strata (sub-population with unequal variances) and a random sample is chosen from each of these stratum. The reason when to use which depends on the situation and need of the experimenter.

There are many such methods: cluster sampling, stratified random sampling, simple random sampling.Their usefulness depends on the circumstances.

Stratified Random Sampling. Google it. .

yes

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stratified random sampling is a sample(strata) that a same and hemogenieous in group and that a different and heterogenious in group

Welll...... when you jump... you leave the ground... when you walk.... you dont.... unless your klumsy.... or if youre jump walking....

A sampling method in which all members of a group have an equal and independent chance of being selected.

Basically in a stratified sampling procedure, the population is first partitioned into disjoint classes (the strata) which together are exhaustive. Thus each population element should be within one and only one stratum. Then a simple random sample is taken from each stratum, the sampling effort may either be a proportional allocation (each simple random sample would contain an amount of variates from a stratum which is proportional to the size of that stratum) or according to optimal allocation, where the target is to have a final sample with the minimum variabilty possible. The main difference between stratified and cluster sampling is that in stratified sampling all the strata need to be sampled. In cluster sampling one proceeds by first selecting a number of clusters at random and then sampling each cluster or conduct a census of each cluster. But usually not all clusters would be included.

No.