Advantages and Disadvantages Of Sampling Method Free Samples

Questions: 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? Answers: Introduction A certain proportion of data that has normally been used to represent the entire population is referred to as a sample. It has been used because it is not possible to collect data from the entire population (Moher et al, 1994). The parameter of the population is estimated from the calculated statistic of the sample taken for use. The number of elements in a set of study forms the population size while the number of elements available in the subset forms sample size. More often, researchers prefer large sample size to small ones because of their characteristics of covering the larger proportion of the population hence increasing the accuracy of the sample statistic (Bacchetti, 2002). In our scenario, the population size is sixty nine thousand from which a sample size of fifteen thousand was selected for use in the study. This sample size represented the number of bank workers who will be sampled. According to the standards of sample sizes, the suitable sample size for this study is 38 3 with 95 percent confidence level and a margin of error of 5 percent. Probabilistic sampling method was also used by the research institutions in ensuring for the fairness of sample selection. 1.Advantages of large sample size Large sample sizes help in obtaining the mean that is quality and precise. The precision of obtained mean will help greatly in the prediction of the population parameter since it covers larger fraction of the population. Outliers present or that might be present in the data are dealt with through the determination of the sample mean. It is important to deal with outliers because they totally differ with the mean and may give deceitful image about the sample or population (MacCallum et al, 1999). Larger sample sizes guarantee for the reliability of the sample mean that is used as the estimator of the population parameter. In order for a sample to fully reflect the population parameter, large sample size is always is normally preferred by the researchers. The quantity need to be pinned down as the standard error (Se) of the mean is therefore used to quantify the reflection of population mean. In each of the sample mean calculated, the standard error is essential. Due to the wider coverage of the population elements, this has been taken as one of the large sample advantages. Disadvantages of large sample size Large sample size is found to be so much involving and time consuming since it covers a wider proportion of the population of study. This makes the researchers to try to avoid it though its results are reliable and precise. Working with large sample size is also expensive to work with, like for example, in this our case, taking a sample of 15,000 bank workers in Belgium will take a lot of time and also the expense involved in the process will be high. A lot of time is required since the larger sample size is spread in the manner that the population is spread and thus collecting data from the entire sample will involve much time compared to smaller sample sizes. Due to its wider coverage, the expense that is involved in data collection process is also higher compared to expense that could be incurred in a small sample size. Factors to consider when choosing sample size When deciding on which sample size to work on, it is important to consider the size of the population so that the sample does not over represent it or under represent it. In this scenario, the research institutions assigned the duty will consider the total bank workers in Belgium (i.e. 69,000) that will later used to calculate the suitable sample size. If the surveyor wants to incur low cost in sampling, he or she will prefer small sample size. it will also help in determining how precise we should be with our data. Sampling whole lot of 15,000 Belgian bank workers will mean high cost incurred in the data collection process. Another factor to consider when selecting the sample size is prior information about the subject of study that will help in determining the size of the sample for use in the study. Prior information can be considered in making decision whether to increase or reduce the sample size. Prior mean and variance are some of the elements that are considered from prior information. Additionally, when choosing the sample size, practicality of the chosen sample size is important in that the size of the sample must be making sense and can be exercised. Among other factors is marginal error because of its reliability in the determination of the perfection of the sample. Marginal error will be affecting the breadth in which the calculate mean will lie on. Also it is important in the construction of the confidence interval level ones the confidence level is available. 2.Equal chances were provided for the bank workers to be selected in the sample by employing probability sampling methods. When applying probability, the chances will be ensured that they are greater than zero. In the process, human biasness is reduced since judgment of the researcher in the selection process is eliminated making the process fair for inclusion of all banks and the bank workers in the sampling process. Research institutions employed stratified sampling method. In this process, the research institutions first do the random selection of the banks that formed the strata then randomly select workers from the randomly selected banks, this ensured fairness in the selection process. Advantages Stratified sampling method gives more precision of the same sample size as compare to other probability sampling methods like the simple random sampling. In the estimation of the population parameter, precision is deemed important. Each stratums statistic will be calculated and their closeness compared to one another. The process is found to be cost effective as it only involves random selection of different baking institutions and workers over the entire population which makes it half completed because of its precision. The method is also flexible that it allows for the selection of any number of participants efficiently and with a lot of ease. Because lesser degree of judgment of the researcher is required, the process tends to be more effective and also results to accuracy in data selection. Compared to other sampling methods, it forms easier way of sampling since it does not involve complicated process. Additionally, technicality is not required in probability sampling methods; t his therefore provides room for anybody to being in a position to conduct the process. The only requirement is random assignment of numbers over the already identified strata. Disadvantages Stratified probability sampling method of selecting the sample results to the selection of specific class of samples to be used. The process is time consuming since the researcher is required to follow all due procedure such as first identifying strata and also going down to the strata to do the selection of individuals that will now participate in the process. The researcher may be filled with monotony when using this sampling method since the same procedure will be repeated several times for the required information to be obtained which in turn may reduce the efficiency of the surveyor. 3.Sampling method chosen may have influence on the outcome data for use in the analysis. For example, the method that was used in sampling the banks was found with biasness that will affect the results obtained from the study and the drawn conclusions (Mann, 2003). Dealing with such short comings, the researcher is supposed to ensure that they reduce biasness as much as possible to save on the results and their dependability. Biasness can be reduced through randomization which will require the researcher to ensure for equal chances of selection of the sample of study. This sampling technique so far is more effective since it ensures equality of selection of samples for use in the study. To improve sampling technique, the sample is divided into groups referred to as strata that must be showing relationship that is meaningful in the study. Differences in the response from the strata are eradicated through stratification for the data to reflect the entire population and ensuring the representation of each stratums opinion. In most of the cases, stratification is done by gender in order to take care of the divergent opinions and have all of them represented. Because each sampling method is concerned with precision in the analysis thereafter, testes methods are supposed to be conducted. This is done with the aim of ensuring that each sampling method chosen for use to satisfy research goals. Potential method is determined through precision and the cost associated. Standard error will be used in this study to measure the level of precision. In the process, smaller standard error will mean higher precision while larger standard error will mean lower precision of the used sa mple. 4.Most of the researchers have been using questionnaires in the collection of data from the respondents. Being that it is the preferred method of data collection does not mean that I is perfect since it is always associated with problems. One major disadvantage of using questionnaire for data collection is dishonesty of the respondents. Dishonesty has been arising as a result of respondents hiding the truth from the surveyor when answering the questions as provided in the questionnaires. Being that the questionnaires were sent to the respondents in our case, clarity of the questions may be lacked in the case a question is not clear hence may lead to misunderstanding of the questionnaire questions (Zaza et al, 2000). This problem is encountered as a result of considering some of the answers private by the respondents and fear of disclosure and desirability bias. This problem can be combated by assuring the respondents of their privacy and also keeping their identification confidential. In our scenario, questionnaires were not presented to the respondents face-to-face this may lead to the difficulty in understanding the questions and also interpreting them since they will lack clarity of the questions and guidelines on how to answer the questions. As a result, there will be variation of responses from the respondents since each one will have understood and interpret the questions differently, the provided answers may not have answered the question according the subject of study. Skewed results can be can be eliminated by ensuring that questions in the questionnaire are well structured, easy to read, understand and interpret. In the case where respondent have their agenda; biased information can be achieved in this case. They can have their opinions about the questions manipulated due to one reason or another. For instance, a questionnaire with many open ended questions may make it difficult for respondents to analyze the questions. In most cases, answers obtained from these questions are always individualized ad express personal opinions that cannot be quantified for analysis. 5.Representativeness of the data is checked using the secondary data that will be collected from the National Bank of Belgium in conjunction with Employment industry in Belgium. This data will act as the reference point for the data collected for use in the study. At the same time, the dataset will as well be used to collect data that will be termed relevant from other sources in the previous studies. Descriptive information is provided by secondary data that are important in offering support to the study that is being done and also offering them with dependable facts. Additionally, the variables used in the study are tested if there is a relationship that exists between them and also helping in building up the model. Secondary data are as well used in data mining where computer technology is used in studying the trend for the previous research by visiting large volumes of data. Among other uses of the secondary data, they are as well used in the identification of relevant sources in order to do away with plagiarism (Cooper, 2003). This helps in maintaining the quality and integrity of the studies. References Cooper, D. R., Schindler, P. S., Sun, J. (2003). Business research methods. Moher, D., Dulberg, C. S., Wells, G. A. (1994). Statistical power, sample size, and their reporting in randomized controlled trials.Jama,272(2), 122-124. Bacchetti, P. (2002). Peer review of statistics in medical research: the other problem.British Medical Journal,324(7348), 1271. Mann, C. J. (2003). Observational research methods. Research design II: cohort, cros sectional, and case-control studies.Emergency medicine journal,20(1), 54-60. Zaza, S., Wright-De Agero, L. K., Briss, P. A., Truman, B. I., Hopkins, D. 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