For most surveys, a sample or target population is necessary, as the population is too large to survey in its entirety. However, if the population is small enough, it is possible to survey each member. This type of survey is known as a census study.[1]

With larger-scale surveys, samples are almost always a necessity. The two main types of sampling methods are probability and non-probability sampling.


Probability Sampling

The probability sampling method is based on the likelihood that each member of a population has an equal chance of being selected to be in the sample. Most researchers agree that this form of sampling is the closest to representing the actual population, as human bias is eliminated with the use of computational randomization. One of the key advantages of probability sampling is that it is the easiest to measure for error. Probability sampling methods include:

  • Random sampling is the truest form of probability sampling. This type of sampling guarantees that each member of a population has an equal chance of being included in the sample. This type of sampling is ideal for more controlled studies where human bias in the selection of the sample is intolerable.
  • Stratified sampling is a more sophisticated version of random sampling that starts by determining the various stratum (i.e., subset, division) within a population and then drawing randomly from each stratum so as to not exclude or misrepresent any one stratum. Examples of popular stratum include the various ethnicities or age groups within a population.
  • Systematic sampling, also called nth name selection technique, is often used instead of random sampling due to its simpler process. After the sample is collected, every nth member within the population is recorded within the sample. For example, if a mentor wanted a systematic randomized sample from a list of mentee evaluations, she could select every 7th evaluation and analyze this extracted sample as a more random representation.[2]

The main drawback to systematic sampling is that the set pattern (e.g., every 7th time) may coincide with another underlying pattern within the sample. For example, the mentor may be selecting every 7th evaluation at random, but if she had separate mentoring sessions for every seventh mentee, she would be selecting the first evaluation from each session each time she picks.[3]

  • Multi-stage sampling essentially combines the techniques of several sampling methods.[4]


Non-probability sampling

Non-probability sampling is often used in more experimental or trial research and does not guarantee a random and close representation of the actual population. Rather than using computational or systematic randomizations, non-probability sampling employs human judgment and often relies on sheer convenience.[5] Some of the various types of non-probability sampling include:

  • Convenience sampling is used when researchers need a cost-effective estimate of the data one would find from doing a more randomized sampling. Typically, these studies are used as jumping off points for larger more comprehensive studies with more representative samples. Also, as the name suggests, the samples chosen are pulled primarily because they are convenient.[6]
  • Judgment sampling relies heavily on human judgment to select a sample that is an appropriate representation of the population. Similar to convenience sampling, judgment sampling relies on human judgment often because more effective resources are too expensive or unavailable.[7]
  • Quota sampling is comparable to stratified sampling in that the researcher first identifies the various stratums in a population. Contrary to stratified sampling, quota sampling then employs either convenience or judgment sampling to select the members within each stratum.[8]
  • Snowball sampling employs current sample members to recruit additional members within their specific population through word-of-mouth. Once the recruitment process begins and sample members sign-on to the study, the effect is similar to that of a snowball effect.[9] Snowball sampling is often used within unique or unapproachable populations, which are difficult to thoroughly assess otherwise.[10]



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