ang hirap!
yes
There are many such methods: cluster sampling, stratified random sampling, simple random sampling.Their usefulness depends on the circumstances.
Simple Random Sample Stratified Random Sampling Cluster Sampling Systematic Sampling Convenience Sampling
simple random, stratified sampling, cluster sampling
They include: Simple random sampling, Systematic sampling, Stratified sampling, Quota sampling, and Cluster sampling.
Stratified random sampling is a sampling scheme which is used when the population comprises a number of strata, or subsets, which are similar within the strata but differ from one stratum to another. One example is school children stratified according to classes, or salaries stratified by departments.A simple random sample may not have enough representatives from each stratum and the solution is to use stratified random sampling. Under this scheme, the overall sampling proportion (sample size/population size) is determined and a sample is drawn from each stratum which represents the same proportion.
In stratified sampling, the population to be sampled is divided into groups (strata), and then a simple random sample from each strata is selected. For example, a state could be separated into counties, a school could be separated into grades. These would be the 'strata'.
Simple!
Simple random sampling.
1) Simple random sampling 2) Systematic sampling 3) Stratified sampling 4) Cluster sampling 5) Probability proportional to size sampling 6) Matched random sampling 7) Quota sampling 8) Convenience sampling 9) Line-intercept sampling 10) Panel sampling
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.
It can be but it is not simple random sampling.