Advantages And Disadvantages Of Large Sample Size

A scenario of Case Study

In this case, the population size was 50,000, while only 12,500 were selected as a sample size of 25% of the total population from UK market to know the understanding of internal and external customers of company towards their services. The sampling method was Stratification random sampling that was implemented with the help of the survey data collection method. The researcher has used selected samples from 200 grocery stores based on a 95% confidence level and a 5% scope of error. To collect data from the target population, the researcher used a survey questionnaire to collect information.

Answer 1:

Advantages and disadvantages of Larger sample Size

The larger sample size has many advantages as well as the drawbacks that are explained below:

Advantages of Larger Sample Size

A large sample size is chosen to preserve the credibility of an average sample as a parameter estimation approach. A larger sample size is typically required to reflect the relevant average demographic adequately. The considerable sample size includes a considerably broader population. A large sample size helps uncover data irregularities. On the other hand, smaller samples are more prone to aberrations that might skew the test result. Larger research samples have more chance of abnormalities (Bairagi et al. 2019). Anomalies might seem to disturb statistical computations; yet planning for them helps create a reasonable representation of the sample and its attributes. Big sample size also allows us to determine a standard and accurate average value. It is because numerous sample components would have surpassed the average. The average is needed to eliminate outliers from the findings. The sample’s anomalies must be dealt with since they deviate significantly from the norm and create a false population image.

Disadvantages of Larger Sample Size

Collecting a larger sample size is also challenging and costly as it needs extended time, budget, effort, and human resources. Hence, collecting data from a large sample takes longer than tiny samples. The cost of data collection is also higher than that of a small sample size owing to its more extensive reach (Devi, 2017). Further, the analysis of more significant sample data needs the expertise of the data analysis tool that is not easy for everyone.

Factors for Selection of Sample

As per the above analysis, it can be stated that there are a few essential elements that the researchers must consider during the selection of the sample size:

• Target population and their geography;
• Availability of the budget and time for finishing the research process;
• Accuracy of population statistics (Engwa and Ozofor, 2015);

Answer 2:

Advantages of Current Sampling Methods

The stratification sample approach delivers higher precision to the same sample group than simple random sampling. The sample parameter prediction model will calculate the statistics of each stratum and their closeness to each other. The approach is very efficient since it simply involves random selection of different financial businesses and personnel from the overall population. It is also flexible in that you may choose any number of responses. The approach looks more precise since it needs less judgment from the scientist (Pruzan, 2016.). It is also easier to screen than other sampling approaches since it does not involve extensive and complicated processes. Also, since probability sampling requires simply random number allocation across specified groups, it is simple enough for non-experts to do.

Disadvantages of Current Sampling Methods

• A comprehensive list of the population being studied must be available to get an accurate statistical estimate of a significant population. The organizations connected with the selected population have limited information of data related to their employees (Sessler and Imrey, 2015).
• A simple random sample is required when a comprehensive list of the whole population cannot be compiled. A thorough list of an entire population may be collected from smaller selections; however, this method is time-intensive and error-prone. Researchers may not access the most current information on the whole population because of the lengthy retrieval processes mandated by organizations that save data on employees and consumers.
• For a company or a person, the process of acquiring information may take a long time. It is pretty costly. It is conceivable that the researcher has a right to set a price for each item of data requested to collect when using a third-party data source (Sessler and Imrey, 2015). It is especially true if the sample size is too small to accurately represent all of the people in a given group when utilizing simple random sampling.
• However, despite the notion that simple random sampling is intended to be unbiased, sample selection bias may take place. When a sample from a larger population is selected due to the sample not being representative of the whole population, additional sampling procedures need to be used for the compensation (Basias and Pollalis, 2018).

Answer 3

Methods to improve Sampling Methods

The above-selected sampling method has an impact on the outcomes of the sample, which may affect the process of data collection. It is because that the error in the data collection may raise the possibility of data biases that may affect not only the research conclusion but also its outcome (Kumar, 2018). In this regard, the researcher must use a random selection of the sample from the larger size population that will help in replacing the possibilities of fractured data. 

Further, the researcher should use the strategy of data stratification to divide the entire selected population into divisions based on their age, local, age, and location. 

Further, the researcher should select the most reliable source of data sharing with the established research participants. This strategy will replace the possibility related to the delay in the delivery of the survey questionnaire and help collect participants’ responses within the expected time (Kumar, 2018).

Answer 4

Problems in the process of data collection and methods to address them in future study

Data collection is a crucial method to collect reliable data in regard to research studies (Flick, 2015). In relation to this, the researcher faced a few critical problems that are presented below:

• Selection of demographics questions;
• Inadequate option to give response to participants;
• The communication gap between researcher and research participants;
• Lack of explanations to each selected question (Flick, 2015);

Methods to Address Problems related to Data collection Process

Addressing the concerns related to the data collection process, the followings are some methods that the researcher uses to mitigate the problem:

A survey questionnaire must be developed with the closed-ended question as such question does not need an explanation, and the participant can early give an answer in the form of yes or know.

Further, the researcher should develop a survey questionnaire after analyzing the geography and nature of the participants (Flick, 2015). This method will help the researcher replace the problem related to the inaccuracy of the questions.

Answer 5.

Secondary Dataset

Regarding the above-defined situation for a research study, it will be necessary to acquire information from the retail stores of the UK concerning its customers and employees. It might be utilized as a checkpoint for the study data that has been developed—also included in the collection of crucial information from various sources such as the prior report. Compared to primary sources, secondary sources give informative data that may be utilized to confirm the current analysis, hence aiding with information in the development of the study. In some instances, the parameters used in the study are evaluated to see whether there is a connection between them, which helps construct the model (Engwa and Ozofor, 2015). A standard method of data collection for big sample sizes is to rely on secondary information, which involves collecting massive amounts of data from previous research to determine a pattern applied to the current study. Secondary data is often employed in the identification of primary sources and a variety of other applications, such as the elimination of plagiarism.

References

Bairagi, V. and Munot, M.V. eds., 2019. Research methodology: A practical and scientific approach. US: CRC Press.

Basias, N. and Pollalis, Y., 2018. Quantitative and qualitative research in business & technology: Justifying a suitable research methodology. Review of Integrative Business and Economics Research, 7, pp.91-105.

Devi, P.S., 2017. Research methodology: A handbook for beginners. Chennai: Notion Press.

Engwa, G.A. and Ozofor, N.M., 2015. Fundamentals of Research Methodology. US: Nova Science Publishers, Inc.

Flick, U., 2015. Introducing research methodology: A beginner’s guide to doing a research project.

Kumar, R., 2018. Research methodology: A step-by-step guide for beginners. US: Sage.

Pruzan, P., 2016. Research methodology: the aims, practices and ethics of science. Springer.

Sessler, D.I. and Imrey, P.B., 2015. Clinical research methodology 2: observational clinical research. Anesthesia & Analgesia, 121(4), pp.1043-1051.