Probability and Non-probability sampling methods

 In the field of Statistics, the sampling method, also known as the sampling methodology, refers to the systematic process of collecting and evaluating data in order to research a population. The data serves as the foundation, with a vast sample space.

There are several sampling strategies that may be categorized into two classes. All these sampling strategies may require particularly targeting hard-to-reach or difficult-to-approach groups.

Sampling Methods

Statistics offers several sampling approaches to obtain meaningful data from a population. There are two distinct types of sampling methods:
  1. Sampling method based on the principles of probability theory.
  2. Non-probability sampling refers to a method of selecting participants for a study that does not include random selection.

 1. Probability Sampling Overview

  • Probability sampling is a method that uses random selection to ensure a representative sample. 
  • It is more time-consuming and expensive than non-probability sampling but provides a reliable sample.

Types of Probability Sampling

  1. Simple Random Sampling: Every item in the population has an equal chance of being selected in the sample.
  2. Systematic Sampling: Items are selected from the target population by selecting the random selection point and selecting other methods after a fixed sample interval.
  3. Stratified Sampling: The total population is divided into smaller groups based on certain characteristics.
  4. Clustered Sampling: Clusters or groups of people are formed from the population set with similar significatory characteristics. Example: Random sampling can be used to select three or four branches as clusters for data collection.

 2. Non-Probability Sampling Methods Overview

a) Convenience or Haphazard Sampling:

  • Assumes all population units are identical, allowing any unit to be selected for the sample.
  • Example: vox pop survey where the interviewer selects any person who happens to walk by.
  • Selection subject to the biases of the interviewer and whoever happened to walk by at the time of sampling.
b) Volunteer Sampling:
  • Respondents are volunteers, usually screened to get a set of characteristics suitable for the survey.
  • Can be subject to large selection biases, but sometimes necessary.
  • Example: callers to a radio or television show, where only those who care strongly enough about the subject tend to respond.
  • Often used for focus groups or in-depth interviews.

C) Judgement Sampling:

  • Sampling done based on previous ideas of population composition and behavior.
  • An expert with knowledge of the population decides which units in the population should be sampled.
  • Subject to the researcher’s biases and potentially more biased than haphazard sampling.
  • Can be useful in exploratory studies, such as selecting members for focus groups or in-depth interviews.

D) Quota Sampling:

  •  One of the most common forms of non-probability sampling.
  • Sampling done until a specific number of units (quotas) for various sub populations have been selected.
  • Quota sampling is preferable to other forms of non-probability sampling because it forces the inclusion of members of different sub populations.
  • Unlike stratified sampling, quota sampling uses a non-random method, leaving the interviewer to decide who is sampled.

E) Snowball and Crowdsourcing in Research

  • Snowball sampling is a method used to find rare or hard-to-reach populations, such as people with disabilities, homeless people, drug users, or non-organized groups.
  •  However, some individuals or subgroups may not be included in the sample.


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