One of the most important issues in researches is selecting an appropriate sample. Among sampling methods, probability sample are of much importance since most statistical tests fit on to this type of sampling method. Representativeness and generalize-ability will be achieved well with probable samples from a population, although the matter of low feasibility of a probable sampling method or high cost, don’t allow us to use it and shift us to the other non-probable sampling methods. In probability sampling we give known chance to be selected to every unit of the population. We usually want to estimate some parameters of a population by a sample. These parameters estimates when we don’t observe whole population usually have some errors. Fortunately in probability sampling it is possible that we know how much our estimates are trustable or close to the parameter value from population by computing standard errors of estimates. This is not easily possible in non-probability sampling methods. Types of probability sampling methods
Simple Random Sampling
What is it?
Simple random sampling is selecting randomly some units from a known and well defined population. In this method the sampling frame should be known and all units should have same chance for being selected. How is it down? (Example)
In simple random sampling, from population of N, n units are selected randomly and the chance of being selected for all units is equal. Different methods and tools can be used for creating random numbers for sample selection. Standard random number tables and soft-wares with ability of generating random numbers like Open-Epi or Stata are available. Example: You have been asked to perform a KAP survey in a prison. The list of all 2000 prisoners has been given to you. You think that a sample of 300 would be satisfactory for your work. If you want choose 300 of them for interview randomly, you can use a random number...