The broad sampling types in research include random and non-random sampling. Probability sampling and non probability sampling are other names for these two types of sampling. If a researcher combines both random and non-random sampling techniques in one study it is a mixed sampling. With in these broad categories of sampling methods there are many other sub categories. You need to know:

- how to do sampling in each of these sampling techniques;
- what are the pros and cons of each technique, and lastly;
- what technique is most suitable for what kind of study.

It is important that the researcher the most suitable sampling technique for the research. As this can have direct impact on the research outcomes. The **validity** and **reliability** of your research highly depends on the right selection of sampling technique.

There are certain types of studies that require random or probability sampling while others prefer non random sampling. But there is no one formula rather there are many factors the researcher will consider before deciding about the sampling technique.

### Random sampling

Random sampling or probability sampling is a very common method in research. In this sampling design every element of the population has an equal

and independent chance of being included in the sample. This is by far the best sampling design if the researcher knows each element in the population and he is able to choose any element from it. Random sampling helps the researcher eliminate bias or personal preferences in the selection of the sampling units. There are several types of random sampling designs. Each of these sampling design ensure that the sample is based on probability and randomness.

#### Simple random sampling

Simple random sampling as the name suggests is the most basic type of random sampling. It fulfills all the criteria of random sampling. There are two methods to choose sampling units in this method: one, table of random numbers; and/or, two, through draw technique. The researcher identifies each element in the population, list them down. Then, he decides about the number of elements in the sample and pick up the required sample randomly from the population.

Due to the randomness this techniques has several benefits. But, simple random sampling is easier to use if the population size is small, in case of large population like a community or a city population the researcher has to use other type of random sampling technique.

#### Stratified random sampling

When the population is large and heterogeneous the researcher cannot use simple random sampling conveniently. So, there is another sampling design, stratified random sampling, it best suits to situations where the population has greater variation.

The researcher identifies the population in this sampling design. Then, the researcher decides about the number of units in the sample. Next, the researcher makes strata of the population. The reason for dividing the population in strata is that the population is too diverse and cannot be treated in a simple manner. However, researcher can make strata according to some special characteristic of the population. From each strata the researcher chooses units in the sample. The researcher ca choose the number of sampling units from each strata using one of the two techniques: One, the researcher can take proportionate units from each stratum; the other way is to take disproportionate, equal elements, from each stratum. And the choice of any one of the above methods depend on the type of study and population.

#### Systematic random sampling

In systematic probability random sampling the researcher selects every kth element in the population. The researcher can find the value of “K” by dividing the number of units in the population by the number of units in the sample. For example, there are 40 units in the population and the sample size is taken to be 20 so 40/20 is equal to 2 and hence every 2nd unit in the population will be taken as a sample. The use of stratified random sampling is most common in household surveys.

#### Cluster sampling

In stratified random sampling the researcher divides the population in strata but in cluster sampling the researcher identifies clusters or groups in the population. Then, the researcher selects sampling units from each cluster to get the total sampling frame . However, in this design the researcher does not divide the population in clusters but the clusters are naturally present in the environment.

### Non-random sampling designs

The other major type of sampling is non-random or non-probability sampling. Here, the researcher does not select the sample on the principles of probability. It does not mean, however, that these samples are not representable, valid or generalizable to the whole population. There are certain type of studies where random sampling is not possible. For example, to conduct a research on the behavior of transsexual people in a community. Here, a random sample is difficult as not all the units or members of that population will be ready to share their views or fill questionnaires. The researcher has to ask them and if they are willing he can only then take observations or interviews. Sometimes, some respondents do not fill and return the questionnaires and hence the researcher has to replace the no-response questionnaires with other respondents.

In psychology, social sciences and behavioral sciences it is not always possible to have a population that you can sample using random sampling techniques. Also, not every time the population is regular and easy to identify. So, the researcher has to design a kind of sampling design that can enable ease of selection rather than randomness.

There are different types of sampling designs in non-random sampling. The researcher develops these designs on the basis of judgement, availability, suitability, or convenience.

#### Quota sampling

**Quota sampling** is similar to stratified random sampling except that it is non-probabilistic. The researcher divides the population in sub-groups and then selects samples from each group by judgement.

In quota sampling the major issue arise because there are chances of bias.

#### Judgmental sampling

The investigator in this type of sampling selects the units from the population according to his own judgement. The reason might be that the investigator thinks certain elements in the population to be more fit for the survey than other. The other names for judgmental sampling are, **purposive**, subjective, or selective sampling.

#### Snowball sampling

In **snowball sampling** the investigator selects one element in the population randomly or non randomly to ask questions. The investigator asks that individual to identify another element of the population that can be taken in the sample.

#### Convenience sampling

In **convenience sampling** the researcher or the investigator selects samples according to his convenience. The researcher selects sample that are easily available or more easy to ask questions. In this way convenience sampling is different from judgmental sampling where the researcher uses judgment to decide what element of population to select.

### Conclusion

In short, we can conclude the following from the above discussion:

- Major types of sampling include random and non-random sampling techniques.
- Random sampling is based on the probabilistic models.
- Non-random sampling is based on the non-probabilistic models, including, convenience, judgement, availability etc.
- A mixed method sampling means that the researcher uses both random and non-random sampling in the research.
- The selection of a sampling technique depends on: study type, type of population, availability of resources, and research goals.
- The right selection of the sampling technique can improve the validity, reliability, and accuracy of a research.

Where does purposeful sampling apply – I didn’t see it described above?

Purposeful sampling is the same as Judgmental sampling.