Advantages And Disadvantages Of Large Sample Size
Sep 17,21Advantages And Disadvantages Of Large Sample Size
Question:
1.Discuss the advantages and disadvantages of having a sample of this size. What factors should be considered in decision on sample size?
2.What are the advantages and disadvantages of the current Sampling Method?
3.What are your suggestions to improve the Sampling Methods?
4.Discuss some of the problems in the process of data collection and how to address them in future study
5.What secondary Dataset can be used to check the representativeness of the sample and how can it be used?
Answer:
Introduction
The sample size is the part of the population chosen for obtainment of data. Hence, it is often considered piece of data that are collected and utilised for calculating statistics, and sample size can be large or small. There are various advantages and disadvantages related to the size of the sample that is collected for calculating a set of statistics. The discussion will emphasize the advantages and disadvantages of a large sample size and the discussion will facilitate the rationality needed to decide upon the right sample size in a research context.
Advantages
The collection of large samples allows sweeping out outliers from the collected sample, as the small sample may misrepresent the data. An increased number of samples allow the researchers to capture enhanced odds of outliers in the sample. It helps the researcher to enhance the reliability of the sample mean that is the key estimator of the population parameter. Also, the large size of the sample facilitates the researcher to get the quality and exact mean (Abt et al., 2020).
Disadvantages
A large sample size provides with exact and quality mean, still at the same time includes the drawback that it is time-consuming and also includes substantial cost for collection of samples. Hence, it is an expensive process. It further includes the factors that are required to be considered like the margin of error that is critically required to consider, and also it includes prior information regarding the subject of study that will allow the researcher to determine the sample size required for studying. Also, the decision on what sample size is required for the research also contributes to the advantage or disadvantage of the chosen sampling process.
What are the advantages and disadvantages of simple random sampling?
Simple random sampling is the form of surveying technique used by researchers for collecting the primary data of a large group of population. It helps to measure a subset of the individuals from a large group statistically. It allows the researchers to approximate the responses from the group of individuals. The technique of surveying includes both advantages and disadvantages that are provided below.
| Advantages | Disadvantages |
| · It includes the random selection of the participants and thus the bias is not present in the surveying technique, and the large population of the sampling includes a similar probability of being selected.
· Also, the advantages include the simplicity as the name implies that the collection of samples is an easy and less intricate task than the rest of the methods of sampling like stratified random sampling. · It also helps to represent a large group of the population and provides a balanced subset (Pal et al., 2018).
|
· Although the bias is eradicated the sample selection on a random basis includes researcher bias during selection.
· The process includes the full list of the population for few random samples, and the small subset lists, which makes the process more expensive. The hiring of a third-party data provider for the collection of the sample includes the requirement of adequate financial resources, thus the higher cost is also a disadvantage of random sampling. · The gathering of information from other sources in the case when the large group of the population is not present tends to be more time-consuming. · Also, it is a complex task for the researcher to access the lists of the full population for random sampling. |
How can you improve a simple random sampling method?
Simple random sampling is a form of probability sampling wherein a researcher picks a subset of the population randomly. Each person in the population has the same probability of getting chosen. The data is collected from as significant a percentage of this random group as feasible. A simple random sample is referred to as the subset for the selected population at a random basis. Each individual in the selected population contains an equal chance of being chosen in the procedure of sampling.
The simple random sampling method can be improved by following the simple steps listed below:
Step 1: Identify the target audience – Begin by selecting the demographic that is to be researched (Etikan & Bala, 2017). It is critical to have access to every person in the population, so it becomes difficult to gather information. So, proper identification of the targeted audience is necessary for effective sampling.
Step 2: Select a sample size – The upcoming step is to figure out how big the sample will be. Larger samples give statistical confidence. However, they are referred to as more costly and time-demanding.
Step 3: Choose your sample at random – This may be accomplished in two ways, viz. through a lottery or by using a random number generator.
Step 4: Gather information from the sample – Information should be obtained only from the sample and not from any external source to increase the efficiency of data obtainment.
Following these basic procedures will increase the efficiency of the approach and eliminate the possibility of errors in the results.
Discuss some of the problems in data collection and how to address them in the future study?
Some of the problems that are associated with the process of data collection are as follows:
1. Data collecting is often referred to as not a necessary part of the business process. Still, the type and quality of data collected by an organization significantly impact the organization’s main tasks and result in time constraints in service delivery.
2. Customer information assortment can occur in various situations and areas where acquiring far-reaching and exact data is testing and measuring data changes depending upon the circumstance.
3. Data collection standards that are inconsistent can render sampling ineffective. Data standards specify methods to acquire everyday data items and demographic information. Data standardized questions, definitions, and acceptable response alternatives are examples of established standards governing consistent data gathering processes.
Improving data collection and the quality of data holdings can help solve the data collecting challenge. Still, it will need a determined effort from the whole organization, starting with a top-down commitment to change (Horvát & Modes, 2021). Moreover, by providing appropriate training to the researcher, as it is critical to teach those involved in data collecting, his or her data collection abilities can be enhanced. The need for data collection should be emphasized in training and the advantages of data for planning, operations, evaluation, and research. If researchers understand why specific data is being collected, they will be more confident in asking for it and explaining why it is vital.
Dataset can be used to check the representativeness of the sample and how can it be used?
A data set is a permanently recorded set of information containing case-level data, case-level aggregation, or case-level statistical adjustments. A set of data is a collectively known asset of information. A data set always corresponds to the tables in the database, where every column of the table depicts a specific variable. Each row represents a specific record of the data set used in the question (Kobayashi et al., 2021). The dataset plays a vital part in a sample since it assists the user in gathering information and applying the appropriate procedure to the dataset to obtain the best results. A representative sample refers to the one that correctly reflects, represents, or embodies the targeted audience. A representative sample has to be considered as a fair presentation of the whole population. Gender, age, financial position, occupation, education, chronic disease, even personality can all be used to assess representativeness. Samples have to be representative of the general population under investigation. They must be picked at random, which means that every person in the selected population has a chance of choosing. They must be broad enough not to skew the results (Yang & Day, 2021). Sampling is especially beneficial when dealing with data sets that are too vast to analyze as their wholes, such as extensive data analytics surveys or applications. It is more convenient and cost-effective to identify and analyze a representative sample than to survey the total population or data.
References
Abt, G., Boreham, C., Davison, G., Jackson, R., Nevill, A., Wallace, E., & Williams, M. (2020). Power, precision, and sample size estimation in sport and exercise science research. Journal Of Sports Sciences, 38(17), 1933-1935. https://doi.org/10.1080/02640414.2020.1776002
Etikan, I., & Bala, K. (2017). Sampling and sampling methods. Biometrics & Biostatistics International Journal, 5(6), 00149.
Horvát, S., & Modes, C. D. (2021). Connectedness matters construction and exact random sampling of connected networks. Journal of Physics: Complexity, 2(1), 015008.
Kobayashi, Y., Todoroki, H., Nakano, K., Narumi, T., & Yasuda, H. (2021). A Modified Random Sampling Method Using Unidirectionally Solidified Specimen: Solute Partition Coefficients in Fe–Cr–Ni–Mo–Cu Alloys. ISIJ International, 61(6), 1879-1888.
Pal, S. K., Singh, H. P., Kumar, S., & Chatterjee, K. (2018). A family of efficient estimators of the finite population mean in simple random sampling. Journal of Statistical Computation and Simulation, 88(5), 920-934.
Yang, S., & Day, G. M. (2021). Exploration and optimization in crystal structure prediction: Combining basin hopping with quasi-random sampling. Journal of Chemical Theory and Computation, 17(3), 1988-1999.
