Notes on sampling methods

Multistage Sampling, Purposive Sampling, Snowball Sampling

Sampling is the process of selecting observations to provide an adequate description and robust inferences of the population. Sample is representative of the population. There are two types of sampling. They are Random sampling and Non random sampling. Non-random sampling methods select locations for sampling by either: according to regular (i.e., systematic) patterns, targeting specific features or events, using personal or anecdotal information, or without any specific plan. Care must be exercised when using non-random sample selection methods because the samples may not be representative of the entire population. If this is the case, then inference cannot extend beyond the set of sampling units. Random sampling methods rely on randomization at some point in the sample design process in an attempt to achieve statistically unbiased samples. Random sampling methods are a form of design-based inference where 1): the population being measured is assumed to have fixed parameters at the time they are sampled, and 2) that a randomly-selected set of samples for the population represents one realization of all possible sample sets (i.e., the sample set is a random variable).

Figure 1- Types of sampling methods

Multistage sampling included in random sampling method. Purposive and snowball sampling are included in Non- random sampling method.

Multistage sampling                         

Multistage sampling also known as multistage cluster sampling is a more complex form of cluster sampling which contains two or more stages in sample collection. In simple terms, in multistage sampling large clusters of population are divided into smaller clusters in several stages in order to make primary data collection more manageable. It has to be acknowledged that multistage sampling is not as effective as true random sampling ; however, it addresses certain disadvantages associated with true random sampling being overly expensive and time consuming(‘multistage sampling—Google Search’, n.d.).

Figure 2- Multistage sampling

Application of multistage sampling: an example. Contrary to its name, multistage sampling can be easy to apply in business studies. Application of this sampling method can be divided into 4 stages.

1. Choosing sampling frame, numbering each group with a unique number and selecting a small sample of relevant discrete groups.

2. Choosing a sampling frame of relevant discrete subgroups. This should be done from relevant discrete groups selected in the previous stage.

3. Repeat the second stage above, if necessary.

4. Choosing the members of the sample group from the subgroups using some variations of probability sampling.

example:-  your research objective is to evaluate  online spreading patterns of households in the US through online questionnaires .You can form your sample group comprising 120 households in the following manner:

1. Choose 6 states in the USA using simple random sampling.

2. Choose 4 districts with in each state using systematic sampling method.

3. Choose 5 households from each district using simple random methods. This will result 120 households to be included in your sample group.

Figure 3- Example for Multistage sampling

Table 1

Advantages and disadvantages of multistage sampling

Advantages of Multistage sampling	Disadvantages of Multistage sampling
Effective in primary data collection from geographically dispersed population when face to face contact in required.	High level of subjectivity.
Cost effectiveness and time effectiveness.	Research findings can never be 100% representatives of population.
High level of flexibility.	The presence of group level information is required.

Purposive sampling

Purposive sampling also known as judgment sampling, selective or subject sampling. It is a sampling technique in which researcher relies on his or her own judgment when choosing members of population to participate in the study. Purposive sampling is a non- probability sampling method and it occurs when elements selected for the samples are chosen by the judgment  of the researcher. Researchers  often believe that they can obtain a representative sample by using a sound judgment, which will result in saving time and money.

TV reporters stopping certain individuals on the street in order to ask their opinions about certain political changes constitutes the most popular example of this sampling method.

In purposive sampling personal judgment needs to be used to choose cases that help answer research questions or achieve research questions(Crossman, n.d.).

Figure 4- Purposive sampling

Applications of Purposive sampling:- an example. Your research  objective is to determine the patterns of use of social media by global IT consulting companies based in the US .Rather than applying random sampling and choosing subject who may not be available , you can use purposive sampling to choose IT companies whose availability and attitude are compatible with the study.

Advantages of purposive sampling	Disadvantages of purposive sampling
Purposive sampling is one of the most cost effective and time effective sampling methods available.	Vulnerably to errors in judgment by researchers.
Purposive sampling may be the only appropriate method available if there are only limited numbers of primary data sources who can contribute to the study.	Low level of reliability and high levels of bias.
The sampling technique can be effective in exploring anthropological situations where the discovery of meaning can benefit from an intuitive approach.	Inability to generalize research findings.

Snowball sampling

Snowball sampling also known as chain referral sampling. It is a non- probability sampling method used when characteristics to be possessed by samples are rare and difficult to find. This sampling method involves primary data sources nominating another potential primary data sources to be used in research. In other words, this method is based on referrals from initial subjects to generate additional subjects. Therefore when applying this sampling method members of the sample group are recruited via chain referral(‘Snowball sampling’, 2019).

Types of snowball sampling

Linear snowball sampling:-  Formation of a sample group starts with only one subject and the subject provides only one referral. The referral is recruited into the sample group and he/she also provides only one new referral.  This pattern is continued until the sample group is fully formed.

                                                  Figure 5- Linear method

Exponential non discriminative snowball sampling:-  The first subject recruited to the sample group provides multiple referrals. Each new referral is explored until primary data from sufficient amount of samples are collected.

Figure 6- Exponential non discriminative method

Exponential discriminative snowball sampling:-  Subjects give multiple referrals, however only one new subject is recruited among them. The choice of a new subject is guided by the aim and objectives of the study.

                                Figure 7- Exponential discriminative method

Advantages of snowball sampling	Disadvantages of snowball sampling
The ability to recruit hidden populations.	Oversampling a particular network of peers can lead to bias.
The possibility to collect primary data in cost effective manner.	Respondents may be hesitant to provide names of peers and asking them to do so may raise ethical concerns.
Studies with snowball sampling can be completed in a short duration of time.	There is no guarantee about the representatives of samples. It is not possible to determine the actual pattern of distribution of population.
A very little planning is required to start primary data collection.	It is not possible to determine the sampling error and make statistical inferences from the sample to the population due to the absence of random selection of samples.

Conclusion

Sampling methods are classified as either probability or non- probability. In probability samples, each member of the population has a known non-zero probability of being selected. Probability methods include random sampling, systematic sampling, and stratified sampling. In non- probability sampling, members are selected from the population in some nonrandom manner. These include convenience sampling, judgment sampling, quota sampling, and snowball sampling. The advantage of probability sampling is that sampling error can be calculated. Sampling error is the degree to which a sample might differ from the population. When inferring to the population, results are reported plus or minus the sampling error. In non -probability sampling, the degree to which the sample differs from the population remains unknown. Sampling is a tool that is used to indicate how much data to collect and how often it should be collected. This tool defines the samples to take in order to quantify a system, process, issue, or problem

Reference

https://research-methodology.net/sampling-in-primary-data-collection/multi-stage-sampling/

https://research-methodology.net/sampling-in-primary-data-collection/purposive-sampling/

https://research-methodology.net/sampling-in-primary-data-collection/snowball-sampling/
Crossman, A. (n.d.). What You Need to Understand About Purposive Sampling. Retrieved 20 August 2019, from ThoughtCo website: https://www.thoughtco.com/purposive-sampling-3026727

multistage sampling—Google Search. (n.d.). Retrieved 20 August 2019, from https://www.google.com/search?q=multistage+sampling&rlz=1C1CHBF_enIN727IN727&oq=mulyis&aqs=chrome.2.69i57j0l5.9469j0j8&sourceid=chrome&ie=UTF-8

Snowball sampling.(2019). In Wikipedia. Retrieved from https://en.wikipedia.org/w/index.php?title=Snowball_sampling&oldid=910136381
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