Principles of Sampling

• Population - Is the set of complete collection or totality of all possible values of the variable.
• Sample - a subset or sub-collection of elements drawn from a population
• (1) Simple Random Sampling

  • In this technique elements of the sample are selected through a lottery.
• (2) Systematic Sampling

  • This technique of sampling is done by taking every element in the population assignment of numbers as a part of the sample.
  • To select the systematic sample of n elements from a population of N elements, we divide the N element in the population in the n groups of kth elements.
• (3) Cluster Sampling 

  • Population under this technique is being divided into sections (or clusters), randomly selecting some of these clusters as the member of the sample size.
• (4) Stratified Sampling

  • In this technique, the population is subdivided into at least two different subpopulations (or strata) that share the same characteristics and then the elements of the sample are drawn from its stratum proportionately.
• (1) Purposive Sampling (Judgmental / Authoritative)

  • In this technique , the elements of the sample are being selected according to the criteria or rules set
• (2) Quota Sampling

  • In this technique, the sample size is limited on the required number or subject in the study.
• 3) Convenience Sampling (Haphazard / Opportunity)

  • In this technique , the samples are being selected from a particular place at a specified time.
• (4) Snowballing Sampling

  • In this technique, the researcher asks respondents to give referrals to other possible respondents.
  • Linear Snowball Sampling
  • Exponential Non-discriminative Snowball Sampling
  • Exponential Discriminative Snowball Sampling