Sampling Techniques And Their Applications Assignment Sample

Introduction To Understanding Sampling Techniques And Their Applications Assignment

Various sampling techniques are used in social research methods

Sampling is a one of the significant method that utilized to select the factors from a target population for conducting study effectually. Moreover, due to the time and money constraints it is not possible for the scholar to conduct study on whole population. In addition to this, selection of the sampling technique is highly influenced from the research issue being investigated, resources and time limitations. There are two wide categories of sampling techniques that is mainly use for social research namely probabilistic and non-probabilistic (Stratton, 2021). The Probability sampling technique ensures that each component in the population has equal and known prospects of being demonstrated in the sample group. The probability sampling technique is consisting of 4 types such as simple random (SRS), stratified, systematic and cluster. Along with this, SRS is regarded as one of the most basic and simplest method of probability sampling that is used by researchers for social research. This method uses random number tables or lottery method to collect the sample from whole population. On the other hand, in the systematic sampling the researcher choose a random and interval starting point to select the sample. Stratified is one of the crucial methods of probability sampling that includes dividing the population into strata or subsets which are relied on the shared characteristics (Van Haute, 2021). In the cluster sampling, the social researchers divide the whole population into the clusters which defined by the predetermined variables.

Apart from this, non-probability sampling does not provide equal opportunity to all respondents of being selected in the sample group. The non-probabilistic method depends on the convenience, judgement and logic to select elements. The non-profitability sampling includes quota, judgemental, snowball and convenience. In Quota sampling, control characteristics are used to categorize the target population selected for the investigation purpose. Accordingly, multiple subpopulations are created by considering people who have similar characteristics (Pace, 2021). In the snowball sampling technique, the social researcher's uses their preliminary group of participants to create and determine a larger network of those who are the part of target population. In the judgemental sampling, emphasis is placed on using logic, judgement and expertise while making selection of sample from the population identified for the study. This type of sampling technique is also called as purposive and it is low-cost and quick sampling method. Lastly, another type of non-probability sampling technique is convenience that is widely used in social research methods. This technique is carried out by researcher according to their convenience (Rahman, 2023). This type of sampling technique is also called as accidental sampling. This non-probability method is used by the researchers at the time when the cost and time are limited in collecting the feedback. Researchers are likely to use non-probability sampling technique when they are conducting exploratory and qualitative research

Critically evaluation of all sampling techniques

Majority of the researchers are likely to use probability sampling technique for their social research methods because it helps in save time and money. As per the view of Tutz (2023), probability sampling is simple and easy method so does not require difficult procedure and it is efficient and rapid. Furthermore, this type of sampling technique includes lesser degree of judgement. In this method, the non-technical person can also collect the sample from the entire population (Wi?niowski et al. 2020) Probability sampling helps the researchers to eliminate the any sign of bias. As critically argued by Gao and Yang (2023), the probability sampling has chances of choosing specific class of samples and increase monotonous and redundant work. In addition, SRS is one of the significant type of probability sampling and it is useful in social research methods because it make sure that each member of entire population has an equal opportunity of being selected which leads to illustrative sample of the population.

As contended by Noor, Tajik and Golzar (2022) this method is easy to understand and execute for the researchers useful for them who have limited resources and new to sampling. Adaption of this method is beneficial for the researchers because it can be utilized for both large and small population as well as collects the sample from both heterogeneous and homogeneous population. On the critical note Jiang, Wu and Jiang (2020), in the SRS method collecting the sample is costly and time consuming process, do not recognize heterogeneous population and difficult to achieve good sample size, obtain response and achieve a good sample.

Along with this, systematic sampling is also useful for the researchers because it is simple and quick to execute, less opportunity for manipulated data and it is equally distributed. As critically opposed by Mendoza et al. (2021), sometimes, adaption of this method is difficult to the researchers because it produce inaccurate outcomes when some of the population is not accessible. On the other hand, stratified sampling technique is also valuable for the researcher because it allows for more accurate, diverse and manageable data and it is a cost-effective approach (Stehman et al. 2022). However, one of the biggest disadvantages of this sampling technique is lack of versatility, difficult data analysis and needs more planning. Cekim and Kadilar (2020) said that non-probability method is also useful in social research because it tends to focus on cost-effectiveness, time efficiency, flexibility and admittance to hidden populations. However Malela-Majika et al, (2021) if the size of sample is very large then this technique cannot be used by researcher because they are unable to select large number of units in an applied amount of time.

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Apart from this, convenience non- probability sampling method is valuable for the researchers because it ensures that the units or source of lists are available or not. Aside from this, quota sampling is also valuable for the researchers while collecting the sample because it ensures simplicity of usage, doesn't require lot of time to gather data (Stratton, 2021). Adaption of judgemental sampling is also beneficial for the researcher because it allow them to choose a sample that is particularly tailored to the population and research objectives.

Sampling technique that is most likely to be used in MBA thesis

In MBA thesis it is crucial for the researchers to generalize the findings which are derived from the sample to the general population so it is beneficial for them to use probability sampling. In the probability sampling the researcher are likely to use simple random sampling method (SRS) to conduct the MBA thesis correctly and effectively. SRS is a one of the significant method of probability sampling technique and it select the sample of observation from a population in order to make assumptions. In addition, in SRS each item of each population has given equal chance to select (Van Haute, 2021). Collecting the information from entire population is time consuming and costly process for the researchers.

For gathering the sample of entire population requires to contract with everyone so it is beneficial for the researcher to use SRS because it helps in provide sound information when needed. The researchers are more likely to use SRS in MBA thesis because it helps in offer fewer preceding details about the study group's population. This sampling technique is useful because it helps in determining the sample error and suitable for data analysis that provides inferential statistics.

In addition to this, the researchers are most likely to use this sampling technique to complete the MBS thesis in effective and efficient way because it is less expensive. This sampling technique requires less time and easy for implementing. Execution of this method is valuable for the researchers to conduct the MBA thesis because it includes less judgement. The sampling process can be considered as impartial since each member is allocated an arbitrarily generated number in random order (Pace, 2021). The researcher choose this sampling method to complete the MBA thesis appropriately because it assist in create the illustrative sample that correctly reflects the features of whole population. It is useful for them because it increases the chances of simplifying the findings from the example to the larger population.

This sampling technique also helps in eliminating the selection bias that minimizes the risk of systematically under and over illustrative specific segments of population. It helps in reduce the persuasion of researcher's personal preferences and bias which leads to more unbiased and objective results which ultimately positively impacts the quality of the MBA thesis. SRS includes randomly selecting samples from the populations (Rahman, 2023). This technique prevents the researchers from being biased in their selections process and provides equal opportunity. Adaption of this method is useful for the researcher to conduct MBA thesis because it helps in reducing any bias which are involved in comparing other sampling methods. In this method, the researcher who conduct MBA thesis doesn't require prior knowledge of data. SRS is regarded as fundamental method of gathering the data and for utilizing this technique it is crucial for the researchers to have effective recording and listening skills.

Process of incorporate your desired sampling technique in your MBA thesis

For incorporating the simple random sampling effectively in the MBA thesis it is essential for the researchers to follow the steps appropriately. Before collecting the sample for MBA thesis, the researchers have to determine the population. Before gathering a simple random sample, the researcher requires to select the best group from which they take a sample. That means the MBA thesis requires having some type of hypothesis that can be redress by studying the sample. After determining the population, the next step of integrating simple random sampling is select the sample size. In this step, the researcher has to make a sample frame or list of members of population (Tutz, 2023). It is vital for the researcher to assign each asset within the population in the chronological order. To determine the sample size it is essential for researcher to follow the various approaches.

To conduct the MBA thesis in effective way through simple random sampling it is important for the researcher to read other studies. If other analyst conducts the same research then the researcher has to review the sample size and used as a proportionate of population. It is also necessary for the researcher to use a table and formula to conduct the MBA thesis in effective way. The researcher can write a formula that makes sense for the parameters of the population.

The larger sample size produces more statistical certainty it will be time-consuming and costly process. After the selecting the sample size the researcher has to generate random numbers. While conducting the MBA thesis the researcher can use a random number generator then it helps in procure the variety of random numbers between the whole population sizes that matches the sample size (Wi?niowski, et al. 2020). Generally, to select the random sample, the researcher can use lottery method to make the selection in other situations. The last step of using simple random sampling is collecting the data from the sample.

To make sure the validity of findings, researcher need to ensure all individual who are selected are actually participates in the study. If the researcher is participates in the study then the findings may be biased because a group is understated in the sample. It is vital for the researcher to follow all the steps to conduct the MBA thesis effectively and with quality content. With the help of this sampling technique the researcher can collect the reliable and accurate data from the samples which help in researching more information about the several topics (Gao and Yang, 2023). Along with this, incorporate of simple random sampling technique is useful for the researcher because it is an adaptable method that can be utilized for both small and large populations. By following all the steps of this sampling technique the researcher can easily collect the vast amount of data to collect the significant information to conduct the MBA thesis effectively.

References

• Ahmad, S., Adichwal, N.K., Aamir, M., Shabbir, J., Alsadat, N., Elgarhy, M. and Ahmad, H., 2023. An enhanced estimator of finite population variance using two auxiliary variables under simple random sampling. Scientific Reports, 13(1), p.21444.
• Cekim, H.O. and Kadilar, C., 2020. Ln-type variance estimators in simple random sampling. Pakistan Journal of Statistics and Operation Research, pp.689-696.
• Gao, C. and Yang, S., 2023. Pretest estimation in combining probability and non-probability samples. Electronic Journal of Statistics, 17(1), pp.1492-1546.
• Jiang, Y., Wu, G. and Jiang, L., 2020. A Kaczmarz method with simple random sampling for solving large linear systems. arXiv preprint arXiv:2011.14693.
• Malela-Majika, J.C., Shongwe, S.C., Aslam, M. and Abbasi, S.A., 2021. Robust distribution-free hybrid exponentially weighted moving average schemes based on simple random sampling and ranked set sampling techniques. Mathematical Problems in Engineering, 2021, pp.1-21.
• Mendoza, M., Contreras-Cristán, A. and Gutiérrez-Peña, E., 2021. Bayesian analysis of finite populations under simple random sampling. Entropy, 23(3), p.318.
• Noor, S., Tajik, O. and Golzar, J., 2022. Simple random sampling. International Journal of Education & Language Studies, 1(2), pp.78-82.
• Pace, D.S., 2021. Probability and non-probability sampling-an entry point for undergraduate researchers. International Journal of Quantitative and Qualitative Research Methods, 9(2), pp.1-15.
• Rahman, M.M., 2023. Sample Size Determination for Survey Research and Non-Probability Sampling Techniques: A Review and Set of Recommendations. Journal of Entrepreneurship, Business and Economics, 11(1), pp.42-62.
• Rahman, M.M., Tabash, M.I., Salamzadeh, A., Abduli, S. and Rahaman, M.S., 2022. Sampling techniques (probability) for quantitative social science researchers: a conceptual guidelines with examples. Seeu Review, 17(1), pp.42-51.
• Stehman, S.V., Mousoupetros, J., McRoberts, R.E., Næsset, E., Pengra, B.W., Xing, D. and Horton, J.A., 2022. Incorporating interpreter variability into estimation of the total variance of land cover area estimates under simple random sampling. Remote sensing of environment, 269, p.112806.
• Stratton, S.J., 2021. Population research: convenience sampling strategies. Prehospital and disaster Medicine, 36(4), pp.373-374.
• Tutz, G., 2023. Probability and non-probability samples: Improving regression modeling by using data from different sources. Information Sciences, 621, pp.424-436.
• Van Haute, E., 2021. Sampling Techniques. Research Methods in the Social Sciences: An AZ of Key Concepts; Oxford University Press: Oxford, UK, p.247.
• Wi?niowski, A., Sakshaug, J.W., Perez Ruiz, D.A. and Blom, A.G., 2020. Integrating probability and nonprobability samples for survey inference. Journal of Survey Statistics and Methodology, 8(1), pp.120-147.