Introduction
In many parts of the world, politics is an area of interest for many people, given the immense weight that political systems confer over the wellbeing and the governance of societies. Political systems are categorized as part of social systems and are among the major sectors in the world’s governance systems after economic, cultural, and legal systems (Jennings & Wlezien, 2018). Therefore, it is imperative to note that political polling remains the positions of influence and power on which many lives depend. As an analysis method, political enthusiasts and professionals have been reported to use various methods in acquiring trends and information from the populations over the possible outcomes of polls. Several studies have cited two main methods to be critical in the political polling process: probability sampling and non-probability sampling methods (Lauderdale et al. 2020). In this regard, this proposal aims at establishing the advantages and disadvantages of using probability and non-probability sampling as political sampling methods.
Probability sampling
Probability sampling is based on chances. According to Etikan, and Bala (2017), subjects in a probability sampling method have an equal chance of being selected for research information over their opinions on a poll. This sampling method can be achieved through various ways, including through mobile or phone call surveys by following the random digit dialing (RDD) for a set of poll subjects (Jennings, & Wlezien, 2018). Therefore, probability sampling has been the widely used method of political sampling over the decades, based on the ability for random subject selections.
Pros of probability sampling
The probability sampling method is the most easily accessible form of political poll sampling used in many nations worldwide due to its outstanding merits. According to Etikan and Bala (2017), probability sampling is a less costly opinion poll sampling method, making room for more people to be sampled at one time in the process. Etikan and Bala (2017) further note that probability sampling also consumes less time by randomly reaching out to many people all at once within a short period.
Several study findings have also linked probability sampling with better outcomes based on its simplicity and ease of obtaining samples from larger population groups faster and more efficiently (Elfil, & Negida, 2017). Most political polling systems use technical means of achieving outcomes from opinion polls. In contrast, probability sampling methods useless specialized equipment, requiring no technical knowledge on operations (Sharma, 2017). Ultimately, probability sampling is considered better because of the possibility of calculating the sampling error margin from a sample group as a representation of the entire population (Yadav, Singh, & Gupta, 2019). Therefore, probability sampling comes out as an efficient polling system whose ease of use and access elevates its outcomes compared to other sources.
Disadvantages of probability sampling
Whereas probability sampling and its associated vices are all the better to use, some faults in using probability sampling also exist. Different types of probability sampling have their unique challenges. According to Lauderdale et al. (2020), systemic sampling is limited to randomly selecting the sample group. In contrast, the stratified random sampling method is more rigorous and tedious thus consumes more time, especially if larger samples of the population have to be considered. Further, cluster sampling is limited to the homogeneity of the sample population, whereas simple random sampling also consumes relatively more time (Sharma, (2017). Therefore, addressing the aforementioned challenges sets probability sampling as the best sampling method for political polling endeavors.
Non-probability Sampling
In non-probability sampling, the opposite of probability sampling is eminent. For non-probability sampling, the probability of selecting the sample population from the entire population cannot be adequately quantified (Quatember, 2019). In this regard, the non-probability sampling thrives on the decisions made by the researcher, which sometimes may be biased. According to Yadav, Singh, and Gupta (2019), non-probability sampling allows the room for sample subjects to select themselves or join in for a survey as in dial-in polls used mainly by media houses and the internet community. Further, a study report indicated that the time for getting a response from the sample population was faster with non-probability sampling due to the participants’ enthusiasm and excitement to participate in the process (Sharma, 2017). A similar study also revealed that non-probability sampling allows for faster access of information besides being more cost-effective than probability sampling (Lauderdale et al., 2020).
Pros of Non-Probability Sampling
According to Sharma (2017), non-probability sampling comes out as the most cost-effective and less time-consuming poll sampling method when compared with probability sampling. In cases where the poll population is tiny, non-probability sampling is considered the best method to use as it allows for the chance to sample even smaller populations (Jennings & Wlezien, 2018). In the recent past, primary survey information collection practices have resorted to non-probability sampling methods as they are less expensive (Sharma, 2017).
Cons of Non-Probability Sampling
According to Etikan and Bala (2017), non-probability sampling does not allow the researcher to know how effective the chosen sample out of the general population represents the views and the feelings of the entire population. Etikan and Bala (2017) further note that with non-probability sampling, it is close to impossible to accurately determine the confidence intervals and margins of error in the sampling process. According to Sharma (2017), non-probability sampling exhibits some difficulties in providing estimates for bias in information relay, limiting the validity and quality of the poll data. Non-probability sampling also limits the generalization of research data findings owing to the tiny population sample that the method uses (Elfil & Negida, 2017). Finally, the non-probability sampling also presents with sample population challenges as a huge part of the populations’ views remain uncaptured, thus limiting outcomes of the process (Sharma, 2017).
Conclusion
In conclusion, political opinion polls are an important part of the political process that acts as a prediction tool for the possible outcomes of a political contest. Probability and non-probability sampling methods remain the widely use methods of sampling. Cost-effectiveness and time efficiency are some of the factors of utmost consideration in polling methods. However, the best sampling method ought to be the one that allows for the collection of more accurate data within a short period at a lower cost. In this regard, probability sampling is the best possible sampling method that can be considered for more precise and cost-effective sampling. Accuracy and consistency of data findings is one massive concern over political opinion polls, as they bear the capabilities of defining and redefining outcomes of the political process. Non-probability sampling remains the best and the widely used opinion polls method based on its simplicity and wide sample calculations for margins of error.
References
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Etikan, I., & Bala, K. (2017). Sampling and sampling methods. Biometrics & Biostatistics International Journal, 5(6), 00149. Web.
Jennings, W., & Wlezien, C. (2018). Election polling errors across time and space. Nature Human Behaviour, 2(4), 276-283. Web.
Lauderdale, B. E., Bailey, D., Blumenau, J., & Rivers, D. (2020). Model-based pre-election polling for national and sub-national outcomes in the US and UK. International Journal of Forecasting, 36(2), 399-413. Web.
Quatember, A. (2019). 42. The representativeness of samples. In Handbücher zur Sprach-und Kommunikationswissenschaft/Handbooks of Linguistics and Communication Science (HSK) (pp. 514-523). De Gruyter Mouton.
Sharma, G. (2017). Pros and cons of different sampling techniques. International journal of applied research, 3(7), 749-752.
Yadav, S. K., Singh, S., & Gupta, R. (2019). Sampling methods. In Biomedical Statistics (pp. 71-83). Springer, Singapore.