Types of Sampling techniques

Types of Sampling Techniques

Research sampling is basically of two types:

Probability Sampling (Random Sampling)

1.       Simple random sampling

2.       Systematic random sampling

3.     Stratified random sampling

Non Probability

1.     Quota Sampling

2.     Convenience sampling

3.     Purposive sampling

4.     Self-selection sampling

5.     Snowball sampling

A). Probability Sampling

The purpose of random selection is the creation of a sample whose units are representative of (i.e., have very similar characteristics to) the population they represent. With random selection, each unit has an equal chance (i.e., equal probability) of being selected. The use of random selection not only improves the chance of creating a representative sample, but also provides you with methods to estimate how likely (i.e., probable) this will be.

With probability sampling, units can be randomly selected with the aid of random number tables or a random number generator. However, the procedure to select units from the sampling frame differs depending on the type of probability sampling technique that is used. Nonetheless, these procedures are very clearly defined, making it easy to follow them.

Types of Probability sampling

To get a sense of what these three types of probability sampling technique are, imagine that a researcher wants to understand more about the career goals of students at a single university. Let's say that the university has roughly 10,000 students. These 10,000 students are our population (N). Each of the 10,000 students is known as a unit (although sometimes other terms are used to describe a unit; see Sampling: The basics). In order to select a sample (n) of students from this population of 10,000 students, we could choose to use simple random sampling, systematic random sampling and stratified random sampling:

·         Simple random sampling

With simple random sampling, there is an equal chance (probability) that each of the 10,000 students could be selected for inclusion in our sample. If our desired sample size was around 200 students, were would select 200 students at random, probably using random number tables.

·         Systematic random sampling

Systematic random sample is a variation on the simple random sample. Like simple random sampling, there is an equal chance (probability) that each of the 10,000 students could be selected for inclusion in our sample. Whilst you typically use random number tables to select the first unit for inclusion in your sample, the remaining units are selected in an ordered way (e.g., every 9th student). 

·         Stratified random sampling

Unlike the simple random sample and the systematic random sample, sometimes we are interested in particular strata (meaning groups) within the population (e.g., males vs. females; houses vs. apartments, etc.). With the stratified random sample, there is an equal chance (probability) of selecting each unit from within a particular stratum (group) of the population when creating the sample.

B) Non Probability Sampling

Non-probability sampling represents a group of sampling techniques that help researchers to select units from a population that they are interested in studying.  A core characteristic of non-probability sampling techniques is that samples are selected based on the subjective judgement of the researcher, rather than random selection (i.e., probabilistic methods), which is the cornerstone of probability sampling techniques.

Non-probability sampling represents a valuable group of sampling techniques that can be used in research that follows qualitative, mixed methods, and even quantitative research designs.

Non-probability sampling techniques can often be viewed in such a way because units are not selected for inclusion in a sample based on random selection, unlike probability sampling techniques. As a result, researchers following a quantitative research design often feel that they are forced to use non-probability sampling techniques because of some inability to use probability sampling (e.g., the lack of access to a list of the population being studied). 

Types of non-probability sampling

There are five types of non-probability sampling technique that you may use when doing a dissertation at the undergraduate and master's level: quota sampling, convenience sampling, purposive sampling, self-selection sampling and snowball sampling.

·         Quota sampling

With proportional quota sampling, the aim is to end up with a sample where the strata (groups) being studied (e.g., males vs. females students) are proportional to the population being studied. If we were to examine the differences in male and female students, for example, the number of students from each group that we would include in the sample would be based on the proportion of male and female students amongst the 10,000 university students.

For example, imagine we were interested in comparing the differences in career goals between male and female students at the University.  If this was the case, we would want to ensure that the sample we selected had a proportional number of male and female students relative to the population. Therefore, the total number of male and female students included in our quota would only be equal if 5,000 students from the university were male and the other 5,000 students were female.

·         Convenience sampling

A convenience sample is simply one where the units that are selected for inclusion in the sample are the easiest to access. In our example of the 10,000 university students, if we were only interested in achieving a sample size of say 100 students, we may simply stand at one of the main entrances to campus, where it would be easy to invite the many students that pass by to take part in the research.

·         Purposive sampling

Purposive sampling, also known as judgmental, selective or subjective sampling, reflects a group of sampling techniques that rely on the judgement of the researcher when it comes to selecting the units (e.g., people, cases/organisations, events, pieces of data) that are to be studied. These purposive sampling techniques include maximum variation sampling, homogeneous sampling, typical case sampling, extreme (or deviant) case sampling, total population sampling and expert sampling. Each of these purposive sampling techniques has a specific goal, focusing on certain types of units, all for different reasons. The different purposive sampling techniques can either be used on their own or in combination with other purposive sampling techniques

·         Self-selection sampling

Self-selection sampling is appropriate when we want to allow units or cases, whether individuals or organisations, to choose to take part in research on their own accord. The key component is that research subjects (or organisations) volunteer to take part in the research rather than being approached by the researcher directly.

·         Snowball sampling

Snowball sampling is particularly appropriate when the population you are interested in is hidden and/or hard-to-reach. These include populations such as drug addicts, homeless people, individuals with AIDS/HIV, prostitutes, and so forth.