How to Make a Sample Representative of a Population

How to Make a Sample Representative of a Population: Describe methods and procedures to achieve this, and reflect on how this is solved in this study if you ask 15 people to join your survey…

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How to Make a Sample Representative of a Population

Paper details

  1. Describe methods and procedures to achieve this, and reflect on how this is solved in this study if you ask 15 people to join your survey. The invitations for participation should be personal (mail or other channel, but not publicly announced on Facebook or other social media). We do it this way to make sure we can describe the invited population, and say something about response-rates. 
  2. The survey is sent out, so don’t worry about this. 

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Solution

How to Make a Sample Representative of a Population

A representative sample refers to a subset of data from a larger population that can depict comparable traits. It aids in analyzing larger groups since it produces smaller data representations that are easy to manage. Latpate et al. (2021) observe that researchers can utilize various sampling techniques to generate a representative sample from a population. They include probability, quota, and simple random, non-random sampling approaches.

Probability sampling involves sample selection from a population based on a random principle (Bhardwaj, 2019). The approach allows researchers to select participants randomly, making it easy to determine the probability of selecting each unit, generating accurate estimates, and drawing conclusions from the population based on statistical analysis.

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In this case, the sampling technique offers the best opportunity for obtaining a sample that accurately represents the population. Probability sampling relies on the statistical theory of randomly selecting a small sample from a larger population and then forecasting the consistency of the responses with a larger population (Bhardwaj, 2019). This eliminates the possibility of human bias or sampling error.

Besides the probability technique, researchers can obtain representative samples through simple random sampling. In this technique, random selection gives each participant within a subset of a population have equal probability of being selected from the larger population (Pal, Singh & Solanki, 2020). The sampling technique is intended to depict the entire study population accurately.

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Latpate et al. (2021) explain that researchers can produce simple random samples by utilizing several techniques, such as lotteries or random draws. For example, in the lottery method, each person in the population is given a number, then random numbers are chosen. In most cases, the resulting subset is balanced, increasing its likelihood of accurate population representation.

In addition, the researchers can utilize the non-probability sampling technique, an approach to choosing samples based on the researchers’ assessment, instead of the random selection used in probability sampling (Latpate et al., 2021). This strategy is less accurate since it relies heavily on the researcher’s expertise, which factors such as fatigue can sometimes bias.

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Due to its observative nature, the method is suitable for qualitative research. Despite failing to give participants equal selection chances, Lamm and Lamm (2019) argue that the method generates a higher possibility of accurate population representation. This is because the researchers already know the chances of selecting a particular member of the population.

Before starting the sampling process, researchers control the sample selection process to produce an adequate representative sample by knowing selection criteria like age, gender, and residence. The method applies in exploratory research like pilot study, where the survey is conducted to smaller population sizes than the predetermined ones.

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Lastly, researchers can utilize quota sampling to obtain representative samples. This non-probability sampling technique allows the compilation of samples from the populations’ representative samples. In this technique, Bhardwaj (2019) explains that researchers select participants based on specific characteristics, then decide on how to establish quotas, ensuring the reliability of the samples in collecting the required data. It can utilize the entire population, with the final population subset selected depending on the researcher’s comprehension of the study population.

Reflection

In the survey example involving 15 participants invited through personalized means like emails, the researcher will utilize the quota sampling technique to obtain samples representative of the population. In this context, before selecting the 15 participants, the researcher must divide a larger population from which the participants were drawn into subgroups of specific elements, like age, gender, job type, and residence.

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Then the researcher will determine the weightage average of each group to ensure that they are similar (Bhardwaj, 2019). Afterward, the researcher maintains the evaluated weightage proportion when selecting the appropriate sample size, which is 15 and is representative of the population. The final step is conducting surveys based on the predetermined quotas.

References

  • Bhardwaj, P. (2019). Types of sampling in research. Journal of the Practice of Cardiovascular Sciences, 5(3), 157. DOI: 10.4103/jpcs.jpcs_62_19
  • Lamm, A. J., & Lamm, K. W. (2019). Using non-probability sampling methods in agricultural and extension education research. Journal of International Agricultural and Extension Education, 26(1), 52-59. DOI: 10.5191/jiaee.2019.26105
  • Latpate, R. Kshirsagar, J. Kumar-Gupta, V. & Chandra, G. (2021). Advanced sampling methods. Singapore: Springer. DOI: https://doi.org/10.1007/978-981-16-0622-9
  • Pal, S. K., Singh, H. P., & Solanki, R. S. (2020). A new efficient class of estimators of finite populations means simple random sampling. Afrika Matematika, 31(3), 595-607. DOI: https://doi.org/10.1007/s13370-019-00745-5

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