# Probability and Non-Probability Sampling

A detailed discussion on the Probability and Non-Probability Techniques with video tutorials

### Some Key Terms

• Population refers to the entire group of people, events, or things of interest that the researcher wishes to investigate
• An element is a single member of the population.
• The population frame is a listing of all the elements in the population from which the sample is drawn. For example: payroll of an organization, University registration
• A sample is a subgroup or subset of the population. By studying the sample, the researcher should be able to draw conclusions that would be generalizable to the population of interest
• A subject is a single member of the sample, just as an element is a single member of the population

### Quantitative vs Qualitative Research

Quantitative:

In Quantitative Research the sample is Unbiased and offers the Representation of population

Qualitative:

A number considerations may influence the selection of a sample such as:

• the ease in accessing the potential respondents;
• your judgment that the person has extensive knowledge about an episode,
• an event or a situation of interest to you

The purpose of sampling in quantitative research is to draw inferences about the group from which you have selected the sample, whereas in qualitative research it is designed either to gain in-depth knowledge about a situation/event/episode or to know as much as possible about different aspects of an individual on the assumption that the individual is typical of the group and hence will provide insight into the group.

### Concept of Sampling

Sampling, therefore, is the process of selecting a few (a sample) from a bigger group (the sampling population) to become the basis for estimating or predicting the prevalence of an unknown piece of information, situation or outcome regarding the bigger group. A sample is a subgroup of the population you are interested in

• The advantages are that it saves time as well as financial and human resources.
• However, the disadvantage is that you do not find out the information about the populationâ€™s characteristics of interest to you but only estimate or predict them.
• Hence, the possibility of an error in your estimation exists.
• Sampling, therefore, is a trade-off between certain benefits and disadvantages. While on the one hand you save time and resources, on the other hand you may compromise the level of accuracy in your findings

### Probability Sampling

• When elements in the population have a known chance of being chosen as subjects in the sample, we resort to a probability sampling design.
• Probability sampling can be either unrestricted (simple random sampling) or restricted (complex probability sampling) in nature.

#### Simple Random Sampling

In the unrestricted probability sampling design, more commonly known as simple random sampling, every element in the population has a known and equal chance of being selected as a subject. Let us say there are 1,000 elements in the population, and we need a sample of 100.

#### Systematic Sampling

Systematic Sampling: The systematic sampling design involves drawing every nth element in the population starting with a randomly chosen element between 1 and n

Formula = N/n

Example: Customers visiting a Bank Branch

#### Stratified Sampling

• Simple Random Sampling does not account for the grouping in the population and a few groups may be under-represented. Stratified Random Sample allows to collect data from Strata (Different Groups within the population).
• In stratified random sampling, there is homogeneity within each group and heterogeneity across groups.

#### Cluster Sampling

• Groups or chunks of elements that, ideally, would have heterogeneity among the members within each group are chosen for study in cluster sampling.
• When several groups with intragroup heterogeneity and intergroup homogeneity are found, then a random sampling of the clusters or groups can ideally be done and information gathered from each of the members in the randomly chosen clusters.
• Ad hoc organizational committees drawn from various departments to offer inputs to the company president to enable him to make decisions on product development, budget allocations, marketing strategies, and the like, are good examples of different clusters.
• Each of these clusters or groups contains a heterogeneous collection of members with different interests, orientations, values, philosophy, and vested interests, drawn from different departments to offer a variety of perspectives.

#### Area Sampling

• A specific type of cluster sampling is Area Sampling. Area sampling is best suited when the goal of the research is confined to a particular locality or area
• Telephone company wants to install a public telephone outlet in a locality where crime is most rampant, so that victims can have access to a telephone.
• Studying the crime statistics and interviewing the residents in a particular area will help to choose the right location for installation of the phone.

#### Double Sampling

• In Job exit interview example, some individuals (i.e., a subset of the original cluster sample) might have indicated that they were resigning because of philosophical differences with the companyâ€™s policies.
• The researcher might want to do an in-depth interview with these individuals to obtain further information regarding the nature of the policies disliked, the actual philosophical differences, and why these particular issues were central to the individualsâ€™ value systems.
• Such additional detailed information from the target group through the double sampling design could help the company to look for ways of retaining employees in the future.

### Non-Probability Sampling

• In non-probability sampling designs, the elements in the population do not have any probabilities attached to their being chosen as sample subjects. This means that the findings from the study of the sample cannot be confidently generalized to the population.
• Researchers may at times be less concerned about generalizability than obtaining some preliminary information in a quick and inexpensive way. They would then resort to non-probability sampling.
• Sometimes non-probability sampling could be the only way to obtain data, as discussed later.

#### Convenience Sampling

• Convenience sampling refers to the collection of information from members of the population who are conveniently available to provide it.

#### Purposive Sampling

• Instead of obtaining information from those who are most readily or conveniently available, it might sometimes become necessary to obtain information from specific target groups. The sampling here is confined to specific types of people who can provide the desired information, either because they are the only ones who have it, or conform to some criteria set by the researcher.
• There are two major types of purposive sampling, Judgment sampling and Quota sampling
##### Judgement Sampling
• Judgment sampling involves the choice of subjects who are most advantageously placed or in the best position to provide the information required.
• For instance, if a researcher wants to find out what it takes for women managers to make it to the top, the only people who can give firsthand information are the women who have risen to the positions of presidents, vice presidents, and important top level executives in work organizations.
• Judgment sampling may curtail the generalizability of the findings, due to the fact that we are using a sample of experts who are conveniently available to us.
##### Quota Sampling
• Quota sampling, a second type of purposive sampling, ensures that certain groups are adequately represented in the study through the assignment of a quota.
• Generally, the quota fixed for each subgroup is based on the total numbers of each group in the population. However, since this is a non-probability sampling plan, the results are not generalizable to the population.
Reference:
Sekaran, U., & Bougie, R. (2019). Research methods for business: A skill building approach. john wiley & sons.