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  1. It is not possible to include each element (variable) of the population in an experimental study. To overcome this limitation, sampling techniques are used.
  2. The results or conclusions obtained by studying a sample are considered representative or applicable to whole population.
  3. The sampling techniques used are:
    1. Simple random sampling
    2. Systematic sampling
    3. Stratified sampling
    4. Multistage sampling
    5. Cluster sampling
  4. Different sampling techniques are used according to their appropriateness in the study and the population.
  1. Sample: Essential characteristics
    1. Adequate size (30 or more)
    2. Random selection
  1. Simple random sampling
    1. Every unit of population has an equal chance of being selected (AI’09).
    2. It is applicable when the population is: Small, Homogenous, and readily available.
    3. Examples:
      1. Patients coming to hospital
      2. Ward patients
  2. ​​Systematic sampling
    1. It involves choosing elements in a systematic way – such as every fifth patient admitted to a hospital or every third infant born in an area after having selected the first number randomly. This type of sampling provides the equivalent of a simple random sample without actually using randomization.
    2. It is used when the population is:
      1. Large
      2. Scattered
      3. Not homogenous
    3. Popularly used in those cases when a complete list of population, from which sample is to be drawn, is available.
  3. Stratified sampling
    1. It is used when the population is: Not homogenous
    2. Merit: Greater representation of each strata of sample.
    3. Method: Population under study
      Division into homogenous groups

      Sampling drawn from each stratum at random in proportion to its size.
    4. Example: in order to determine prevalence of hemoglobinopathies in India, the equal number of children from Hindus, Sikhs, Christians and Muslims need to be selected.
  4. Multistage sampling
    1. It is employed in large country surveys.
    2. Advantages
      1. Greater flexibility
      2. Saves extra labor
  5. ​​Cluster sampling [most frequently asked and difficult to answer] (AIIMS Nov’08)
    1. It is used when units of population are natural groups or clusters such as villages, wards, blocks, slums, and school.
    2. Cluster samples may be used when it is too expensive or laborious to draw a simple or stratified random sample; eg – in a survey in medical students in United states, an investigator may start by selecting a random set of groups or clusters – such as a random set of 10 medical schools in the USA.
    3. In cluster sampling the clusters are selected randomly, and all members in the cluster are sampled.
    4. This method is much more economical and practical than trying to take a random sample directly from the widely scattered population of all medical students in the United states.
    5. Cluster sampling gives a higher standard error but the data collection in this method is simpler and less expensive. Another disadvantage of this method of sampling is that inter-cluster comparison is not possible.
    6. Design effect is a phenomenon seen with cluster sampling and is the reason why sample size in cluster sampling is larger than that in simple random sampling.
    7. As per module approved by WHO, it is most often used to evaluate vaccination coverage in expanded programme of immunization or to find out missed cases in pulse polio immunization, a 30 / 7 (30 clusters with 7 subjects in each) cluster technique is used. Q

Sample size calculation

In prevalence / Cross sectional studies
= 4 PQ.    

P = Prevalence of disease
Q = 1 - P
Zα2 = Level of significance
at 95% level
Zα = 1.96
Zα2 = (1.96)2
= 3.84 4.
L = Precision

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