Concept and rudimentss of chance trying methodsOne of the most of import issues in researches is choosing an appropriate sample. Among trying methods. chance sample are of much importance since most statistical trials fit on to this type of trying method. Representativeness and generalize-ability will be achieved good with likely samples from a population. although the affair of low feasibleness of a likely sampling method or high cost. don’t allow us to utilize it and switch us to the other non-probable trying methods. In chance sampling we give known opportunity to be selected to every unit of the population. We normally want to gauge some parametric quantities of a population by a sample. These parametric quantities estimations when we don’t observe whole population normally have some mistakes. Fortunately in chance trying it is possible that we know how much our estimations are trustable or close to the parametric quantity value from population by calculating standard mistakes of estimations. This is non easy possible in non-probability sampling methods. Types of chance trying methods
Simple Random SamplingWhat is it?Simple random sampling is choosing indiscriminately some units from a known and good defined population. In this method the sampling frame should be known and all units should hold same opportunity for being selected. How is it down? ( Example )
In simple random trying. from population of N. n units are selected indiscriminately and the opportunity of being selected for all units is equal. Different methods and tools can be used for making random Numberss for sample choice. Standard random figure tabular arraies and soft-wares with ability of bring forthing random Numberss like Open-Epi or Stata are available. Example: You have been asked to execute a KAP study in a prison. The list of all 2000 captives 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 indiscriminately. you can utilize a random figure generator to bring forth 300 Numberss between 1 and 2000. Most of the clip you would hold some repeated Numberss that should be replaced by new Numberss.
UsesSimple random sampling is a good method for comparing the preciseness of different methods of sampling and besides utile for learning general probabilistic sampling regulations. Criticisms Although when the population is non really large it is possible to make simple random trying. other methods of random trying are preferred to it because they brought more precise estimations from population. In large population and broad geographical trying countries it is non easy to take a list signifier all units and indiscriminately choosing them. Systematic Random Sampling
What is it?In systematic random trying we use the order of the population list or the topographic point of units in the population for taking the sample. How is it down? ( Example )First we should hold the list of the population and harmonizing to the entire figure of sample needed we define a value of “k” to leap over population units and choosing units. If we want choice 5 units over a population of 50. we can specify k=6 and pull a random figure between 1 and 6. Suppose the random figure is 3. Since. the K is 6 the 2nd. 3rd and 4th units will be 9. 12 and 18 severally. Examples: We want to gauge the prevalence of HIV infection among unpaid blood givers in Tehran. 2009. The list of blood givers was available on computing machine package and the order of patients was harmonizing to the day of the month of their referral. We decided to choose a sample of 2. 000 from 76. 000. The K was defined as 38 and a figure between 1 and 38 was chosen. Choosing 12 so 38 was added to that and 2nd individual was the record figure 50 and the following units were chosen adding each clip the 38 to the old selected record. Harmonizing to the participants names repeated units were excluded and replaced by new units with the same method. Uses
Systematic random sampling is really easy and less clip consuming. The preciseness of systematic random sampling is higher than simple random sampling.
CriticismsThe opportunity of choosing a non-representative sample is really high in this method of trying particularly when there is a correlativity between the topographic point of the unit in the population list and the features of the unit that should be observed. Here it is possible that