Answer

Introduction

                The subject of cheating among college students is attracting attention from practitioners and scholars in the educational and business industries. In fact, it is alleged that the Adelphia and Enron scandals emerged as a result of the cheating culture among business students. According to Batool, Abbas, and Naemi (2011), a survey on American universities revealed shocking results. Specifically, the survey revealed that the rate of cheating in American universities varies from fifteen percent to eighty one percent. Critical to the discussion is the fact that different studies have examined the reasons explaining cheating among college students. For instance, Batool, Abbas, and Naemi (2011) cite reasons such as poor preparation for exams and students’ involvement in extracurricular activities as the reasons why students copy. Apparently, the level of cheating among business students is alarming (Scanlon and Neumann, 2002), which implies that educational institutions should implement policies to thwart this nightmare.

                The dean of the College of Business at Rocky University realizes that cheating among students has been its concern for several years. Some faculty members in the college believe that cheating is more widespread at Rocky than at other universities, while other faculty members think that cheating is not a major problem at the college. To resolve some of these issues, the dean commissioned a study to assess the current ethical behavior of business students at Rocky. As part of this study, an anonymous exit survey was administered to a sample of 90 business students from this year’s graduating class. Responses to the following questions were used to obtain data regarding three types of cheating. Critical to the discussion is the fact that this paper reports findings from the study and recommends that a further study be conducted to identify why business students from Rocky University cheat.

Q1: Summary of the Data

                It is crucial to highlight that Microsoft office Excel played a rudimental role in summarizing the data. Specifically, the software was used to organize the data before analysis. For instance, the software was used to count and tabulate the data appropriately. Critical to the discussion is the fact that the study intended to identify the proportion of all Rocky business school students who copied. It is important to highlight that the copying behavior was categorized into three groups. These behaviors include, copying from the internet, copying in exam, and collaborating with other students on individual projects.

The Proportion of All Students:

In order to tabulate the count, Microsoft Excel was used. Specifically, the following functions were used to tabulate the counts, COUNTIF(B2:B91, “YES”), COUNTIF(B2:B91, “NO”), COUNTIF(C2:C91, “YES”), COUNTIF(C2:C91, “NO”), COUNTIF(D2:D91, “YES”), and  COUNTIF(D2:D91, “NO”). The total number of students was obtained using the function SUM(B93:B94) SUM(B93:B94), and SUM(B93:B94). As Santa, Pradhan, Senchaudhuri, Mehta, and Tamhane (2007) argue, the proportions were calculated by dividing the observed values by the sample size. The results obtained after using the functions are shown in table one below.

Table 1: A table indicating the count and proportions for students who copied.

The Proportion of Male Students

The study also intended to identify the proportions of male students who admitted to copying from the internet, copying in exam, or collaborating with other students. Consequently a different Microsoft Excel function was used to tabulate the outcomes based on whether the responding student was male or female. Particularly, the following Microsoft Excel functions were used COUNTIFS (B2:B91,”YES”,E2:E91,”Male”), COUNTIFS (C2:C91,”YES”,E2:E91,”Male”), and COUNTIFS (D2:D91,”YES”,E2:E91,”Male”). The totals were obtained using the functions SUM(B102:C102), SUM(B103:C103), and SUM(B104:C104). The results are shown in table two below.

Table 2: A two way table showing the count of male students who cheated

                Black (2012) argues that in order to calculate the proportions, take the observation and divide it by the sample size. In this case, the observed values were 42, 48, and 45. Therefore, to obtain the proportions the following formula was used in Microsoft Excel: B102/D102, B103/D103, B104/D104. The proportions obtained are shown in table three below.

Table 3: A table of proportions for male students who copied

The Proportion of Female Students

The study also intended to identify the proportions of female students who admitted to copying from the internet, copying in exam, or collaborating with other students. Consequently, a function similar to the one above was used to tabulate the outcomes based on whether the responding student was male or female. Particularly, the following Microsoft Excel functions were used COUNTIFS  (B2:B91,”YES”,E2:E91,”Female”), COUNTIFS (C2:C91,”YES”,E2:E91,”Female”), and COUNTIFS (D2:D91,”YES”,E2:E91,”Fmale”) (McCullough, B.A. and David 2008). The totals were obtained using the functions SUM(B102:C102), SUM(B103:C103), and SUM(B104:C104). The results are shown in table four below.

Table 4: A two way table showing the count of female students who cheated

                Based on Quadrianto Smola, Caetano, and Quoc (2009) argument, 18, 24, and 26 were used to determine the proportion of female students who copied in exams. As a result, the following formulas were used in Excel B114/D114, B115/D115, and B116/D116. The results are shown in table five below.

Table 5: A table of proportions for female students who copied

Comments

The results indicate that 47 percent of business students from Rocky University admitted to copying from the internet, 53 percent admitted to copying in exams, and 50 percent admitted to collaborating on individual projects (Peacock,  and Peacock, 2011). The results indicate that male students from the university are more likely to copy from the internet and in exams than female students. On the contrary, female students are likely to collaborate on individual projects than male students. Figure one below displays a grouped bar graph that summarizes the results.

Figure 1: A Grouped bar Graph Showing the Proportions of Students from Rocky Business School who admitted to Copying

Q2: Confidence Interval for Some Type of Cheating

                It is notable that the study intended to identify the confidence interval for all students, male students, and female students who admitted to some type of cheating. As a result, a two-way table was created from the data collected during the survey. Specifically, the table was created using the COUNTIFS function in Excel. Critical to the discussion is the fact that the function was used to identify students who never cheated. Particularly, the functions COUNTIFS(B2:B91,”NO”,C2:C91,”NO”,D2:D91,”NO”,E2:E91,”Male”) and COUNTIFS(B2:B91,”NO”,C2:C91,”NO”,D2:D91,”NO”,E2:E91,”Female”) were used to obtain the number of male and female students who never cheated. As a result the number of male and female students that cheated was obtained by subtracting the number of students who never cheated from the total number of students. For example, the number of male and female students who admitted to some type of cheating was obtained as shown below.

Number of male students who cheated = total number of male students – number of males who never cheated.

Number of female students who cheated = total number of female students – number of females who never cheated.

The count obtained was used to tabulate a two-way table six shown below.

Table 6: A two way table displaying the count of students who were involved in some type of copying

In order to find the confidence interval for the proportions of for all students, male students, and female students who admitted to some type of cheating, their proportions was first calculated. As argued by Bland and Butland (n.d), a proportion of population is obtained by dividing the observed value by the sample size. This was done in Excel using the following formulas C97/$C97, C98/$C98, C99/$C99. The results obtained are depicted in table seven below.

Table 7 A table of proportions used to find the confidence intervals for students who were involved in some type of copying

It is also notable that additional constants used to calculate the confidence intervals were used. These constants are displayed in table eight below.

Table 8: A displaying constants used to calculate confidence intervals

According to Pfenning (2011), the confidence interval of a proportion is obtained using the formula

 ……………………………………………………………………………………………………………Equation 1

Where P is the proportion and N is the sample size, and Z is the Z statistic. As a result, the Z statistic was obtained in Excel using the formula ABS(NORMSINV(B118)). This led to calculation of the confidence intervals using the following formulas in Excel C103+B119*(SQRT((C103*(1-C103))/90)) and C103-B119*(SQRT((C103*(1-C103))/90)). The results obtained are shown in table nine below.

Table 9: A table showing the confidence intervals for students who admitted to some type of copying

Q3: Confidence Interval for Copying on the Internet

                A two-way table was created from the data collected during the survey in order to calculate the confidence interval for students who admitted to copying from the internet. Specifically, the table was created using the COUNTIFS function in Excel. Critical to the discussion is the fact that the function was used to identify students who never cheated. Particularly, the functions COUNTIFS(‘Q2′!B2:B91,”YES”,’Q2’!E2:E91,”Male”) and COUNTIFS(‘Q2′!B2:B91,”YES”,’Q2’!E2:E91,”Female”) admitted to copying from the internet.  As a result the number of male and female students that copied from the internet was obtained using the SUM function in Excel. This function was used as SUM(B3:B4). The count obtained was used to tabulate a two-way table ten shown below.

Table 10 A two way table showing the count for students who copied from the internet

In order to find the confidence interval for the proportions of for all students, male students, and female students who admitted to some type of cheating, the proportions calculated above were tabulated together as shown in table eleven below. It should be noted that additional constants used to find the confidence interval are present in the table. All the constants were obtained from the problem apart from the Z statistic which was found using an Excel function. In particular, the function NORMSIN and the function ABS were used. This owes to the reality that the functions were combined to give the formula ABS(NORMSINV(B11)), which produced the Z statistic in the table.

Table 11: A table of proportions used to find the confidence intervals for students who copied from the internet

It follows that the confidence intervals were calculated using equation 1 above. Critical to the discussion is the fact that the following formulas were used to obtain the confidence intervals (Bovey, R., and Bullen, 2009). The formulas are B9+($D$12*(SQRT((B9*(1-B9)/90)))) and B9-($D$12*(SQRT((B9*(1-B9)/90)))). This formula was used to calculate the confidence intervals for male students, implying the confidence for female and all students were calculated using the same formula, but changing the proportions appropriately. A closer look at the formulas above reveals that the cells containing the proportions were not fixed. It follows that the confidence intervals for female and all students were obtained by dragging the two formulas down.  The results obtained are tabulated in table twelve below.

Table 12: A table indicating the confidence intervals for students who copied from the internet

Q4: Confidence Interval for Copying in Exam

                A two-way table was created from the data collected during the survey in order to calculate the confidence interval for students who admitted to copying in exams. Specifically, the table was created using the COUNTIFS function in Excel. Critical to the discussion is the fact that the function was used to identify students who never cheated. Particularly, the functions COUNTIFS(‘Q2′!C2:C91,”YES”,’Q2’!E2:E91,”Male”) and COUNTIFS(‘Q2′!C2:C91,”YES”,’Q2’!E2:E91,”Female”) admitted to copying from the internet.  As a result the number of male and female students that copied from the internet was obtained using the SUM function in Excel. This function was used as SUM(B3:B4). The count obtained was used to tabulate a two-way table thirteen shown below.

Table 13: A two way table indicating the count of students who copied in exams

In order to find the confidence interval for the proportions of for all students, male students, and female students who admitted to some type of cheating, the proportions calculated above were tabulated together as shown in table eleven below. It should be noted that additional constants used to find the confidence interval are present in the table. All the constants were obtained from the problem apart from the Z statistic which was found using an Excel function. In particular, the function NORMSIN and the function ABS were used. This owes to the reality that the functions were combined to give the formula ABS(NORMSINV(B11)), which produced the Z statistic in the table.

Table 14: A table of proportions used to find the confidence intervals for students who copied in exams

It follows that the confidence intervals were calculated using equation 1 above. Critical to the discussion is the fact that the following formulas were used to obtain the confidence intervals. The formulas are B9+($D$12*(SQRT((B9*(1-B9)/90)))) and B9-($D$12*(SQRT((B9*(1-B9)/90)))). This formula was used to calculate the confidence intervals for male students, implying the confidence for female and all students were calculated using the same formula, but changing the proportions appropriately. A closer look at the formulas above reveals that the cells containing the proportions were not fixed. It follows that the confidence intervals for female and all students were obtained by dragging the two formulas down.  The results obtained are tabulated in table twelve below.

Table 15: A table indicating the confidence intervals for students who copied in exams

Q5: Confidence Interval for Collaborating on Individual Projects

                A two-way table was created from the data collected during the survey in order to calculate the confidence interval for students who admitted to collaborating on individual projects. Specifically, the table was created using the COUNTIFS function in Excel. Critical to the discussion is the fact that the function was used to identify students who never cheated. Particularly, the functions COUNTIFS(‘Q2′!D2:D91,”YES”,’Q2’!E2:E91,”Male”) and COUNTIFS(‘Q2′!D2:D91,”YES”,’Q2’!E2:E91,”Female”) admitted to copying from the internet.  As a result the number of male and female students that copied from the internet was obtained using the SUM function in Excel. This function was used as SUM(B3:B4). The count obtained was used to tabulate a two-way table thirteen shown below.

Table 16 : A two way table indicating the count of students who collaborated on individual projects

In order to find the confidence interval for the proportions of for all students, male students, and female students who admitted to some type of cheating, the proportions calculated above were tabulated together as shown in table eleven below. It should be noted that additional constants used to find the confidence interval are present in the table. All the constants were obtained from the problem apart from the Z statistic which was found using an Excel function. In particular, the function NORMSIN and the function ABS were used. This owes to the reality that the functions were combined to give the formula ABS(NORMSINV(B11)), which produced the Z statistic in the table.

Table 17: A table of proportions used to find the confidence intervals for students who collaborated on individual projects

It follows that the confidence intervals were calculated using equation 1 above. Critical to the discussion is the fact that the following formulas were used to obtain the confidence intervals. The formulas are B9+($D$12*(SQRT((B9*(1-B9)/90)))) and B9-($D$12*(SQRT((B9*(1-B9)/90)))). This formula was used to calculate the confidence intervals for male students, implying the confidence for female and all students were calculated using the same formula, but changing the proportions appropriately. A closer look at the formulas above reveals that the cells containing the proportions were not fixed. It follows that the confidence intervals for female and all students were obtained by dragging the two formulas down.  The results obtained are tabulated in table twelve below.

Table 18: A table indicating the confidence intervals for students who collaborated on individual projects

Q6: A Hypothesis Test

                During the global recession of 2008 and 2009, there were many accusations of unethical behavior by Wall Street executives, financial managers, and other corporate officers. At that time, an article appeared suggesting that part of the reason for such unethical business behavior may stem from the fact that cheating has become more prevalent among business students. The article reported that 86% of business students admitted to cheating at some time during their academic career as compared to 77% of non-business students. It can be deduced that the rate of copying among business students from other higher learning institutions. Consequently, a hypothesis tests was conducted to determine if the proportion of all business students at Rocky University who were not involved in some type of cheating is less than that of all business students elsewhere.

                A two way table indicating the number of students from Rocky Business School who never copied and those who admitted to copying at some point is displayed in table six above. Critical to the discussion is the fact that, as opposed to question two above, this question focused on the number students who were not involved in cheating.  Consequently, the values under the NO column were used to obtain the proportions for testing the hypothesis. These proportions were calculated by dividing the observed value by the sample size. As a result, the following formulas were used in Excel to obtain the proportions B97/$D97, B98/$D98, and B98/$D98. The proportions obtained are shown in table below.

Table 19: A table indicating the proportions of business students who never cheated.

After obtaining the proportions a null hypothesis was stated as below

H₀: The proportion of all business students at Rocky University who were not involved in some type of cheating is not less than that of all business students elsewhere.

H₁: The proportion of all business students at Rocky University who were not involved in some type of cheating is less than that of all business students elsewhere.

                The p value was obtained using the Z-value, the proportion, and the percentage of students who were reported to have cheated in other institutions (Newcombe,1998). Vital to the debate is the fact that table six above was used to find the p value for the proportion of business students from Rocky Business School who denied ever being involved in some type of copying. This p value was obtained using the formula below.

 Duchesne (2003) .…………………………………………………………………………Equation 2

Where the Z value was obtained using the formula

 Talha (2008) ………………………………………………Equation 3

Where the phat (0.1) was the proportion of students from Rocky business school who never cheated and p (0.14) is the proportion of students from other institutions who never cheated.

N is the sample size which is equal to 90

This was done in Excel using the following formulas, which resulted in a p-value of 0.862940024. Since 0.862940024 is greater than 0.05, we reject the null hypothesis (In Fox, In Negrete-Yankelevich,  and In Sosa, 2015) and conclude that the proportion of all business students at Rocky University who were not involved in some type of cheating is less than that of all business students elsewhere.

Table 20: A table indicating how the p-value was calculated

Q7: Conclusion

                Considering the results obtained from the study, it is evident that the rate of copying at Rocky Business School is alarming. This owes to the reality that 90 percent of the surveyed graduates from Rocky Business School admitted to some type of cheating. It is notable that such a level of cheating will lower the confidence of employers in our graduates. It follows that the dean should implement measures to eliminate or reduce the rate of cheating at Rocky Business School.  Imperative to the discussion is the fact that this would only be possible after understanding factors promoting cheating among Rocky Business School. It follows that a study should be conducted to identify the factors that promote copying among students at Rocky Business School.

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