Sampling is essential to estimate the characteristics of a given population. To choose the right methodology for a population, you must be aware of the type of sampling performed. The population is not necessarily people; it can be anything else like organisms, species, countries, organizations, events, or objects. Sampling will help conclude an entire population(Boddy,2016). The group that you are concluding about is the population. The particular group from the larger population you will choose to help conclude the population is the sample. For a good representation of the whole population, a sample should be randomly picked, no bias involved, and be appropriately large. These definitions show that population and sample are very different; hence, it is essential to know the differences to distinguish them(Joseph,2019). The population's measurable quality is named a framework, while the quantifiable quality in the trial is called a demography. The reports from the population are an accurate representation of the opinion at hand, unlike the sample reports resulting from confidence interval and margin of error. A sample is a subdivision of the entire organizations, while the population is a complete set.
Further, the sampling procedures are collocated into two broad classes: non-probability sampling and probability sampling. On probability sampling, the samples are chosen according to the probability hypothesis, hence making it the most appropriate method of sampling to be used in this research. The method does not involve long processes and isn't complicated. The probability sampling techniques are stratified random sampling, systematic random sampling, cluster sampling, and simple random sampling. In stratified sampling, a population is divided into smaller groups named strata(Taherdoost,2016). The strata are created based on the shared characteristics or attributes such as educational attainment or income. The random sample is then extracted from the strata and is pooled to form a random sample. The stratified sampling highlights the population's differences, unlike the sample random sampling that views the whole population as equal. Highlighting these differences gives room for the researchers to be biased. A random sample is chosen in systematic sampling grounded on an irregular kicking off point with a periodic, moored interval. The interlude is found by dividing the organization size by the wanted sample size. Systematic sampling is preferred when there is less time, and there is no risk of data manipulation. For cluster examining, the analyst cleaves the population into panicles and then chooses an indiscriminate sample from the clumps. It is the least precise method of sampling compared to the other methods. The clusters are divided naturally; hence it is widely used in market research. In random sampling, the researchers choose a sample from the population depending on probability or luck; hence each member of the population can be selected. For this method, a relatively large sample is chosen to ensure the whole population is well represented. Large samples help lower the margin of errors. It would be time-consuming and expensive to collect data from the entire population in markets worldwide, hence selecting a few samples to represent the population.
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Additionally, to conduct research in the global markets about diversity, the researcher needs to choose the most appropriate sampling method that will give the best results(Alvi,2016). The simple random sampling would be the most appropriate method to investigate diversity in global markets. The method lacks bias as it can accurately represent the global markets. The researchers have no time to judge the markets as they are not assigned in a specific manner. This method gives each member of the population an equal chance to participate in the research; hence, the information gathered through this method will apply to the whole population. With this method, it is easy to estimate the sampling error and global market diversity. Random sampling takes a shorter period to make inferences as it is not complicated and does not involve many processes. The saved time during sampling will be useful in interpretation and analysis. Due to its simplicity, after picking the sample random, the task is half done; hence, many costs are saved as not many resources are involved in sampling. This method can be done even by non-technical persons as it does not involve a crucial, complex, and lengthy process. With computers, the monotony that would occur is reduced because of doing a repetitive job of assigning numbers. A random number generator software will be appropriate to help pick samples. A global market research will require inclusion of population all over the world, hence it will be difficult to visit all workplaces . Random sampling be viable as you may not be required to make actual visits to global markets as you can get the information via the internet. Currently the internet has provided an easier way to conduct research without involving a lot of movements. Most of the global markets have internet platforms and websites where the researchers can actively access the samples. The researchers can communicate with the managers, customers, and employers through social media and get information about the impact of workplace diversity.
References
Alvi, M. (2016). A manual for selecting sampling techniques in research.
Boddy, C. R. (2016). The sample size for qualitative research. Qualitative Market Research: An International Journal .
Joseph, k. (2019) Population vs. sample | Guide to choose the right sample | QuestionPro . QuestionPro. https://www.questionpro.com/blog/population-vs-sample/
Taherdoost, H. (2016). Sampling methods in research methodology; how to choose a sampling technique for research. How to Choose a Sampling Technique for Research (April 10, 2016) .
