Sampling Methods PDF

Sampling  Sampling: is a procedure to obtain information about only a part of a population.  Advantages of sampling: -Lower cost -Saves time - Provides more intensive and accurate investigations and information. - It eliminates bias.  Precautions in Sampling 1. It must be well chosen (Re...

Sampling  Sampling: is a procedure to obtain information about only a part of a population.  Advantages of sampling: -Lower cost -Saves time - Provides more intensive and accurate investigations and information. - It eliminates bias.  Precautions in Sampling 1. It must be well chosen (Representative to the parent population). 2. Sample must be sufficiently large to minimize sampling variation. 3. Adequate coverage of the sample to avoid sample bias.  Methods of sampling 1. Non probability sample 1. Purposive samples. These are samples chosen according to the persons own judgment so one cannot generalize their results on the whole population. 2. Quota samples. This type of non-probability sample is used in sampling public opinions. It is of no use in public health and clinical practice. II. Probability sample (Random Sample) :Every unit in the sampled population has equal probability or chance of being selected. It is the recommended method; generalization can be made to the parent population Types of Random samples 1. Simple Random Sample 2. Systematic Random Sample 3. Stratified Random Sample 4. Cluster Sample 5. Multistage Random Sample 1. Simple Random Sample:  The basic procedure is to prepare a sampling frame; a list showing all the units, decide on the size of the sample. Select your sample size using random number tables, or computer programs sample unit.  Used for homogenous population.  2. Systematic Random Sample:  Instead of selecting randomly, a predetermined system may be used.  *A list of all sampling units.  *A sampling interval.  * Choose a random start, then select every nth item at regular intervals (every 5th, 10th……) It is often easier than simple random sample. Example :  If we wanted to select a random group of 10 people from a population of 30 using systematic sampling, all the potential participants must be placed in a list and a starting point would be selected. Once the list is formed, every 3rd person on the list (starting the count at the selected starting point) would be chosen as a participant, since 30/10 = 3.  For example, if the selected starting point was 2, the 5th person on the list would be chosen followed by the 8th, and so on. Once the end of the list was reached and if additional participants are required, the count loops to the beginning of the list to finish the count. 3. Stratified Random Sample:  Done when dealing with a character not equally distributed among the population.  *First divide the population into strata.  *Then simple random sampling is performed in each stratum.  Used for heterogenous population. 4. Cluster Sample:  Done when the sampling unit is not the individual but a group of individuals (clusters) as city blocks. It is easier to investigate people living in small areas than those in scattered places.  5. Multistage Sample:  In this type we draw one sample in two or more stages. It is used if the population is distributed over a large area in national study. Field of work is arranged into stages: governorates (the first level), then districts, then cities or towns or villages and lastly families and individuals. A random sample is drawn from each stage successively to collectively form the final sample of study.