Sampling Methods in Research Methodology; Choosing a Sampling Technique for Research

Sampling Methods in Research Methodology; Choosing a Sampling Technique for Research

N Melo
by N Melo
January 21, 2022 0

Sampling Methods in Research Methodology; Choosing a Sampling Technique for Research

HIPTEX

HIGHER INSTITUTE FOR PROFESSIONALISM AND EXCELLENCE

 

Sampling Methods in Research Methodology; Choosing a Sampling Technique for Research

Dr. Nouridin Melo
nouridinmelo@gmail.com, HIPTEX, Yaounde

 

  1. SAMPLING METHODS

In order to answer the research questions, it is doubtful that researcher should be able to collect data from all cases. Thus, there is a need to select a sample. The entire set of cases from which researcher sample is drawn in called the population. Since, researchers neither have time nor the resources to analysis the entire population so they apply sampling technique to reduce the number of cases.

 

  1. Stage 1: Clearly Define Target Population

The first stage in the sampling process is to clearly define target population. Population is commonly related to the number of people living in a particular country.

  1. Stage2: Select Sampling Frame

A sampling frame is a list of the actual cases from which sample will be drawn. The sampling frame must be representative of the population.

  1. Stage 3: Choose Sampling Technique

Prior to examining the various types of sampling method, it is worth noting what is meant by sampling, along with reasons why researchers are likely to select a sample. Taking a subset from chosen sampling frame or entire population is called sampling. Sampling can be used to make inference about a population or to make generalization in relation to existing theory. In essence, this depends on choice of sampling technique.

In general, sampling techniques can be divided into two types:

         Probability or random sampling

         Non- probability or non- random sampling

Before choosing specific type of sampling technique, it is needed to decide broad sampling technique.

Sampling Techniques

Probability Sampling

  • Simple random
  • Stratified random
  • Cluster sampling
  • Systematic sampling
  • Multi stage sampling

Non-probability Sampling

  • Quota sampling
  • Snowball sampling
  • Judgment sampling
  • Convenience sampling

 

    1. Probability Sampling

Probability sampling means that every item in the population has an equal chance of being included in sample. One way to undertake random sampling would be if researcher was to construct a sampling frame first and then used a random number generation computer program to pick a sample from the sampling frame (Zikmund, 2002). Probability or random sampling has the greatest freedom from bias but may represent the most costly sample in terms of time and energy for a given level of sampling error (Brown, 1947).

1.1.      Simple random sampling

The simple random sample means that every case of the population has an equal probability of inclusion in sample. Disadvantages associated with simple random sampling include (Ghauri and Gronhaug, 2005):

1.2.      Systematic sampling

Systematic sampling is where every nth case after a random start is selected. For example, if surveying a sample of consumers, every fifth consumer may be selected from your sample. The advantage of this sampling technique is its simplicity.

1.3.      Stratified random sampling

Stratified sampling is where the population is divided into strata (or subgroups) and a random sample is taken from each subgroup. A subgroup is a natural set of items. Subgroups might be based on company size, gender or occupation (to name but a few). Stratified sampling is often used where there is a great deal of variation within a population. Its purpose is to ensure that every stratum is adequately represented (Ackoff, 1953).

1.4.      Cluster sampling

Cluster sampling is where the whole population is divided into clusters or groups. Subsequently, a random sample is taken from these clusters, all of which are used in the final sample (Wilson, 2010). Cluster sampling is advantageous for those researchers whose subjects are fragmented over large geographical areas as it saves time and money (Davis, 2005). The stages to cluster sampling can be summarized as follows:

Choose cluster grouping for sampling frame, such as type of company or geographical region

         Number each of the clusters

         Select sample using random sampling

 

1.5.      Multi-stage sampling

Multi-stage sampling is a process of moving from a broad to a narrow sample, using a step by step process (Ackoff, 1953). If, for example, a Malaysian publisher of an automobile magazine were to conduct a survey, it could simply take a random sample of automobile owners within the entire Malaysian population. Obviously, this is both expensive and time consuming. A cheaper alternative would be to use multi-stage sampling. In essence, this would involve dividing Malaysia into a number of geographical regions. Subsequently, some of these regions are chosen at random, and then subdivisions are made, perhaps based on local authority areas. Next, some of these are again chosen at random and then divided into smaller areas, such as towns or cities. The main purpose of multi-stage sampling is to select samples which are concentrated in a few geographical regions. Once again, this saves time and money.

  1. Non probability Sampling

 

Non probability sampling is often associated with case study research design and qualitative research. With regards to the latter, case studies tend to focus on small samples and are intended to examine a real life phenomenon, not to make statistical inferences in relation to the wider population (Yin, 2003). A sample of participants or cases does not need to be representative, or random, but a clear rationale is needed for the inclusion of some cases or individuals rather than others.

2.1.      Quota sampling

Quota sampling is a non random sampling technique in which participants are chosen on the basis of predetermined characteristics so that the total sample will have the same distribution of characteristics as the wider population (Davis, 2005).

2.2.      Snowball sampling

Snowball sampling is a non random sampling method that uses a few cases to help encourage other cases to take part in the study, thereby increasing sample size. This approach is most applicable in small populations that are difficult to access due to their closed nature, e.g. secret societies and inaccessible professions (Breweton and Millward, 2001).

 

2.3.      Convenience sampling

Convenience sampling is selecting participants because they are often readily and easily available. Typically, convenience sampling tends to be a favored sampling technique among students as it is inexpensive and an easy option compared to other sampling techniques (Ackoff, 1953). Convenience sampling often helps to overcome many of the limitations associated with research. For example, using friends or family as part of sample is easier than targeting unknown individuals.

 

2.4.      Purposive or judgmental sampling

Purposive or judgmental sampling is a strategy in which particular settings persons or events are selected deliberately in order to provide important information that cannot be obtained from other choices (Maxwell, 1996). It is where the researcher includes cases or participants in the sample because they believe that they warrant inclusion.

  1. Stage 4: Determine Sample Size

In order to generalize from a random sample and avoid sampling errors or biases, a random sample needs to be of adequate size. What is adequate depends on several issues which often confuse people doing surveys for the first time. This is because what is important here is not the proportion of the research population that gets sampled, but the absolute size of the sample selected relative to the complexity of the population, the aims of the researcher and the kinds of statistical manipulation that will be used in data analysis. While the larger the sample the lesser the likelihood that findings will be biased does hold, diminishing returns can quickly set in when samples get over a specific size which need to be balanced against the researcher’s resources (Gill et al., 2010). To put it bluntly, larger sample sizes reduce sampling error but at a decreasing rate. Several statistical formulas are available for determining sample size.

  1. Stage 5: Collect Data

 

Once target population, sampling frame, sampling technique and sample size have been established, the next step is to collect data.

  1. Stage 6: Assess Response Rate

Response rate is the number of cases agreeing to take part in the study. These cases are taken from original sample. In reality, most researchers never achieve a 100 percent response rate. Reasons for this might include refusal to respond, ineligibility to respond, inability to respond, or the respondent has been located but researchers are unable to make contact. In sum, response rate is important because each non response is liable to bias the final sample. Clearly defining sample, employing the right sampling technique and generating a large sample, in some respects can help to reduce the likelihood of sample bias.

  1. Conclusion

In this paper, the different types of sampling methods/techniques were described. Also the six steps which should be taken to conduct sampling were explained. As mentioned, there are two types of sampling methods namely; probability sampling and non-probability sampling. Each of these methods includes different types of techniques of sampling. Non-probability Sampling includes Quota sampling, Snowball sampling, Judgment sampling, and Convenience sampling, furthermore, Probability Sampling includes Simple random, Stratified  random,  Cluster sampling, Systematic sampling and Multi stage sampling.

 

Acknowledgment

This research was prepared under the support of HIPTEX institute, Mbalgong Campus, Yaounde.

 

Author

 

 

Nouridin Melo holds a PhD in Economic and social History and is a secondary school teacher by profession at MINESEC. His area of research focuses on immigrant entrepreneurship, ethnic firms, transnational African entrepreneurs, and non-Western forms of growth and investment. Nouridin’s research is framed in the context of economic history. He uses mixed research methods to collect, analyse, and interpret data in most of his work. He also has a perfect command of pedagogy with 11 years of experience as a tutor at MINESEC.

 

 


References

ACKOFF, R. L. 1953. The Design of Social Research, Chicago, University of Chicago Press.

BARTLETT, J. E., KOTRLIK, J. W. & HIGGINS, C. C. 2001. Organizational research: determining appropriate sample size in survey research. Learning and Performance Journal, 19, 43-50.

BREWETON, P. & MILLWARD, L. 2001. Organizational Research Methods, London, SAGE.

BROWN, G. H. 1947. A comparison of sampling methods. Journal of Marketing, 6, 331-337.

BRYMAN, A. & BELL, E. 2003. Business research methods, Oxford, Oxford University Press.

DAVIS, D. 2005. Business Research for Decision Making, Australia, Thomson South-Western.

FOWLER, F. J. 2002. Survey research methods, Newbury Park, CA, SAGE.

GHAURI,  P.  &  GRONHAUG,  K.  2005.  Research  Methods  in  Business  Studies,  Harlow,  FT/Prentice Hall.

GILL, J., JOHNSON, P. & CLARK, M. 2010. Research Methods for Managers, SAGE Publications.

Authors’ Biography

 

N Melo
NM
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