Chapter twenty-one of Cooper & Schindler (2013) text discusses the different methods of presenting data in business research. According to the authors, some of the methods of data presentation include tables, graphs, and charts. The choice of one approach over the other depends on the nature of the statistics that the researcher wants to present. However, from a general perspective, the presentation techniques enable one to visualize statistics in ways that the reader understands easily. Furthermore, they summarize the long and detailed theoretical explanations. Thus, an individual may decide to read a summary form of the research article rather than spending lots of time studying the whole document. Therefore, the objective is to apply the knowledge in Cooper and Schindler’s chapter in analyzing the alternative methods that Garfield et al. (2016) could have used in their study.
Garfield et al. (2016) is a study estimating the eligibility of the uninsured people who the “Affordable Care Act” (A.C.A) covers by 2016. The researchers employ different methods of data visualization including the pie chart as the first tool. It contains the statistics about the non-elderly people who are eligible for A.C.A’s coverage.
The alternative method that the authors could use is the bar graph. Firstly, the data is not exact but an estimate. Secondly, the article’s audience needs to visualize the trends. The advantage of bar graphs refers to the easiness of translating the trends through visualization (Cooper & Schindler, 2013). However, the pie chart they use does not show the trends clearly. The pie chart also has the limitation of visualization of the entire information. For example, the researchers cannot present the total number of the nonelderly who are non-insured under the A.C.A. Consequently, the authors include the information-theoretically below the chart. They state that the total number of the non-elderly who the A.C.A does not cover is 27.2 million (Garfield et al., 2016). The reader would however find such data in its visual format if the article used the cumulative bar graph. The cumulative bar graph, according to Cooper & Schindler (2013), shows the sum of the different components of the figures. Some information on the pie chart, for example, 43% of the people eligible for financial assistance is difficult to synthesize. The detail appears beside the bracket next to the chart. Such inaccuracies imply that Garfield et al. (2016) selected an inappropriate procedure for presenting the data. Likewise, the bar graphs would not be suitable because the data is in figures (millions of people) and not a percentage. The circle charts are appropriate for percentages (Cooper & Schindler, 2013).
The second set of statistics displays the relationship between the numbers of the nonelderly who the A.C.A does not cover the Medicaid and non-Medicaid expansion states. Cooper & Schindler (2013) select the cumulative bar graph for the section. However, considering that the data is in percentages, the pie chart is the best alternative way to show it. The advantage of the cumulative (stacked) graph that Garfield et al. (2016) use is that it shows the relationship between two concepts (the Medicaid and non-Medicaid expansion states) next to each other. Consequently, the reader can compare the details and see how they vary (Cooper & Schindler, 2013). However, the traditional (normal) use of the cumulative graph has the motive of showing how the different components of data develop the totals. The graph in the article does not meet this requirement, thus is inappropriate. The guidance that Garfield et al. should use when the arrangement of statistics is in percentages is the pie chart being the most effective method one should choose.
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The difficulty the reader of the article may face is the need to calculate some details. Garfield et al. combine both percentages and numbers in the cumulative bar graph. For example, they state that the total number of nonelderly who lack insurance in the “Medicaid expansion states” is 13.1 million. The percentage of the people who are “eligible for tax credits” is 16%. One must calculate 16% of 13.1 million to get the number of people in this group.
The final strategy is the use of tables. Garfield et al. (2016) use them to summarize long and detailed information. The first table covers information about the nonelderly people who remain uninsured despite qualifying for the A.C.A cover. The table presents information according to different locations. It would be difficult to draw a graph or a chart that summarizes the statistics the way the table does, as the figures and percentages on the tables are exact and not estimations. According to Cooper & Schindler (2013), tables require exact (not approximated) numbers or percentages especially when the researcher wants to summarize long and detailed information. For instance, Garfield et al. would require many pages to write paragraphs that explain the data that they have summarized on the tables. Nonetheless, the disadvantage of the technique is that it would take a long time for the viewer to read and comprehend because of their structure. Tables are non-simplistic because the viewer has to take time connecting the dots between the different data parts. There is no alternative that is more suitable for the long and detailed information than the tables. Although this method has its weaknesses, it is the best tool for presenting such data.
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In conclusion, the researchers must consider the kind of statistics before choosing the method for presenting it. Bar graphs are the appropriate alternative to the pie chart the authors use in their report. The data is numerical; hence, it requires the bar graph. The cumulative graph in the second instance is inappropriate because of the percentages that require a pie chart. However, tables are the best third set of data because they are suitable for summarizing detailed information.