BUACC5931 Research and Statistical Methods for Business | Q&A
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Research and Statistical Methods for Business (BUACC 5931)
Added on 2020-03-13
BUACC5931 Research and Statistical Methods for Business | Q&A
Research and Statistical Methods for Business (BUACC 5931)
Added on 2020-03-13
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Q1 Sample SizeSample size is a proportion or a subset of a population. The choice of correct sample size is important in gathering accurate information Kühberger et al (2014). This therefore makes samplesize a very fundamental factor in the reliability of the information collected. When making a choice of which sample size to use, the following formula should always be put to use;Sample size = populationdistributionpercentagepick(marginerror%confidencelevelscore)2 = 0.5∗(1−0.5)(0.051.96)2=384.16≈384Therefore from the above calculation, the correct sample size for this population (69,000) was supposed to be 384 at 95% confidence level with a marginal error of .05. In the calculation, the population percentage pick was made at 50% due to its conservativism in the calculation of larger sample size. As a result, being that the bank workers population was 69,000, the recommended and appropriate sample size was supposed to be 384 bank workers. The choice of 15,000 bankers to participate in the survey resulted to large sample size since it surpassed the recommended sample size. Such size of the sample has its advantages and disadvantages as discussed below;Advantages, disadvantages of large sample size and factors to consider when choosing a sample sizeOne of the advantages of a large sample size is that it helps in reducing the margin of error henceimproving the accuracy of the results from the sample. According to Cleary et al (2014), this willalways make the population parameter to lie within the confidence interval of the point estimator. Being that the sample size chosen for the survey was 15,000, it was most likely to obtain more accurate information from the bank workers pertaining stress as compared to when
smaller sample size would have been used by the two research institutions i.e. Katholike Universteit Leuven and the private company in Belgium. Additionally, large sample sizes are important in that they are representative of the wider range of elements contained in the population. Due to this therefore, all or most of the outliers will be captured in the sample unlike when the sample is size is small Belli et al (2014).One major disadvantage of large sample is its costly nature. Reaching and covering a wider proportion of the population involves high expense incurred in the process Goodman et al (2013). The Union of Belgian Banks would therefore incur much through the assigned research institutes in the data collection from the targeted 15,000 bank workers. Being that the participants were not found from the same bank institution, the process would also be time consuming to get to various parts of the country for other bank workers.Cost that will be involved in obtaining the sample is one among other factors that should be considered when coming up with which sample size to use in a survey. The risk involved in the values collected from the sample will also act as the determinant of the sample size i.e. if only the risk value matters in the collected values then as a result therefore, low risk values will call for large sample sizes. Prior information about the topic of study will also help in either reducingor increasing the sample size since prior estimates of means and variances will be used to help dealing with variation that could be found within groups (Button et al, 2013).Q2 Sampling MethodsSampling method is the process by which representative groups are selected from the population that is being studied. In the selection of the 15,000 bank workers for the sample by the research institutions, they used stratified sampling method. One of the advantages of the currently used
sampling method by the institutions is that the sampling method minimizes sampling errors Ye etal (2013). This is achieved though dividing the population into subgroups called strata. The strataare spread to ensure that each characteristic of the population is represented by the strata and having the elements in each stratum selected by simple random sampling hence reducing sample selection bias. Also, stratified sampling method ensures that the targeted population is highly represented in the sample. Difficulty to identify ways of subdividing the population into subpopulations makes it at times unusable by the researchers, where this forms one of the major disadvantages. Additionally, stratified sampling method is time consuming where a lot of time is spent in the identification of the strata and then later select the sample from each strata through simple random sampling method (Acharya et al, 2013). In relation to the situation at hand, the research institutions i.e. the private specialized company in stress at work and Katholike Universteit Leuven, they first had to identify all the bank institutions in Belgium then divide the workers according to their bank institutions to form strata where further, the workers were now to be selected from the bank institutions through simple random sampling method to provide equal chances of obtaining the workers that will form the useable subset of the population. In order to improve the effectiveness of this sampling method (stratified sampling method), I therefore suggest that the number of strata to be increased. This will increase from where sampling of the individuals in the population will be selected hence representing almost the entire population thus reducing the marginal error in sampling.Q3 Research DesignCross-sectional design is a tool used by the researchers to obtain specific point time information from the collected data. It has some of the advantages and disadvantages. One of the advantages as noted by (Shen and Björk, 2015) is that the cross-sectional research design through cross-
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