# Non Probability Samples

Probability samples are not sufficient for research. This is why some researchers apply non-probability samples. Here, researchers are completely unaware of the probability of the elements contained in each population in the samples provided for investigation.

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Non-probability samples come with several advantages. They are particularly important when used in an investigation aimed at evaluating the interrelation between two dependent and independent variables where precise results are needed. These samples are also important when it comes to qualitative investigations aimed at evaluating social procedures of a particular group. In some occasions, non-probability samples are used in situations where coming up with a comprehensive sampling framework is practically impossible (Monette, Sullivan, Dejong, 2010).

Despite the importance of non-probability samples, they also have some drawbacks that need to be taken into consideration too. First, they can only be used alongside probability sampling because in the event that probability samples are excluded, one may not be able to distinguish the populations represented by non-probability samples. Another limitation for these samples is that their extent of error is unknown. Test of significance in statistical data and information also falls amongst the limitations of these samples. There are four categories of non-probability samples and these include;

1. Availability sampling
2. Snowball sampling
3. Quota sampling
4. Purposive sampling
5. Dimensional sampling

Availability sampling

This is also known as a convenience or accidental sampling. It involves the use of readily available elements by an investigator for research purposes. It is sometimes difficult or practically impossible to come up with precise sampling elements for a particular population. This may be brought about by the high costs of identifying such elements or simply impracticability. For this reason, availability sampling is used to curb the situation (Monette, Sullivan, Dejong, 2010).

Snowball sampling

The name of this type of sampling was derived from the action of a snowball to pick snow pallets when rolled. Just like a snowball picks other snow pallets and grows in size when rolled, this type of sampling is aimed at leading the investigators to other cases when they begin with just a few. This is an implication that researchers applying this type of sampling only need a few cases to begin their investigation. The number of cases then multiplies as the investigation proceeds (Monette, Sullivan, Dejong, 2010).

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Quota sampling

This incorporates the subdivision of a population into a number of classes then quotas are set on the exact number of portions to be chosen from individual classes. This is done in such a manner that once the quota has been reached for a class then no more elements from the class are incorporated into the sample. Quota sampling is closely related to stratified sampling in the sense that populations are subdivided into smaller classes on both occasions (Monette, Sullivan, Dejong, 2010).

Purposive sampling

Purposive sampling, also called judgmental sampling, is the type of sampling where researchers apply their judgment or prior information about a subject matter when selecting elements that would best meet the intended goals of a study. This incorporates a creative and innovative combination of rational judgment and prior knowledge of the samples to be used in a study (Monette, Sullivan, Dejong, 2010).

Dimensional sampling

In spite of the importance of small samples, it is important to note that utter precision must be observed when applying them. Dimensional sampling is intended to assist in the identification of small samples in a manner that improves their representation. This type of sampling has two fundamental steps. A researcher should first spell out the most significant dimensions. This is followed by a meticulous selection of a sample that incorporates a case that signifies each probable amalgamation of dimensions (Monette, Sullivan, Dejong, 2010).

## Reference

Monette, D.R., Sullivan, T.J., & Dejong, C.R. (2010). Applied Social Research: A Tool for the Human Services (8th Edition). Boston, Mass: Thompson Publishing.

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