Benchmark – Ethical Behavior Of Business Students At Bo Diddley Tech
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 that suggested that part of the reason for such unethical business behavior may have stemmed from the fact that cheating had become more prevalent among business students, according to a February 10, 2009, article in the Chronicle of Higher Education. The article reported that 56% of business students admitted to cheating at some time during their academic career as compared to 47% of nonbusiness students.
Cheating has been a concern of the dean of the college of business at Bo Diddley Tech (BDT) for several years. Some faculty members in the college believe that cheating is more widespread at BDT than at other universities, whereas other faculty members think that cheating is not a major problem in the college. To resolve some of these issues, the dean commissioned a study to assess the current ethical behavior of business students at BDT. As a former college athlete herself, the dean believed that the spirit of fair play students develop as part of participating in athletics would make them less likely to cheat. As part of this study, an anonymous exit survey was administered to a sample of 240 students from this year’s graduating class, half of whom were business students and half of whom were not. The survey asked various questions, including the student’s college and if the student was an athlete or not. Responses of the various questions were fed into a computer algorithm that made a quantitative determination as to whether the student should be considered a “cheater” or not. The results are in the attached Excel spreadsheet, “Benchmark – Bo Diddley Tech Data Set.”
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Order Paper NowPrepare a managerial report as part of your submission to the dean of the college that summarizes your assessment of the nature of cheating at BDT. Be sure to include the following items in your written report.
Utilize the data set provided by the instructor in the Excel spreadsheet, “Benchmark – Bo Diddley Tech Data Set” (60 records per student).
Submit the Excel data calculations (Alpha 0.05).
- Make a pivot table with: Business Student (Rows), Athlete (Rows), Cheated (Columns), and Cheated (Summed Value).
- Create a bar chart showing cheating by athletes and business students.
- Determine if there is a statistical difference between nonathlete BDT business students and the national average for business students as reported by the Chronicle of Higher Education.
- Determine if there is a statistical difference between athlete BDT business students and the national average for business students as reported by the Chronicle of Higher Education.
- Determine if there is a statistical difference between BDT business students and the national average for business students as reported by the Chronicle of Higher Education.
- Determine if there is a statistical difference between BDT nonbusiness students and the national average for nonbusiness students as reported by the Chronicle of Higher Education.
Utilizing the data you have analyzed, write a managerial report of 500-800 words for the dean. The managerial report needs to include an introduction, analysis, conclusion, and a minimum of three supporting references.
- Introduction (Define): Explain, in your own words, why you are providing this report and the problem(s) you are trying to solve.
- Collect: Describe the data set you used.
- Organize: Describe your pivot table.
- Visualize: Include and describe your bar chart.
- Analyze: Provide a summary of your conclusions based on the four population proportion hypothesis tests.
- Ethical Summary: The dean has expressed a concern related to the amount of cheating currently taking place at BDT and has strongly suggested that you “tweak” the statistical data such that they favor the image of the university. Discuss the potential use of unethical manipulation of statistical data to provide a biased outcome as well as the ethical counter proposal you would offer the dean in this scenario.
- Conclusion: What advice would you give to the dean based upon your analysis of the data?
You are required to submit your Excel data analysis along with your written report.
Prepare this assignment according to the guidelines found in the APA Style Guide, located in the Student Success Center. An abstract is not required.
This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.
You are required to submit this assignment to LopesWrite. Refer to the LopesWrite Technical Support articles for assistance.
Benchmark Information
This benchmark assignment assesses the following programmatic competencies:
BS Business Information Systems,
2.2 Use quantitative techniques and tools to analyze data relevant to business decision making. [MC3]
BS Business Administration, BS Business Information Systems, BS Accounting, BS Business Analytics, BS Business Management, BS Entrepreneurial Studies, BS Finance, BS Finance and Economics, BS Hospitality Management, BS Sports Management, BS Supply Chain and Logistics Management
2.3: Use quantitative techniques and tools to analyze data relevant to business decision making. [MC3]
BS Business for Secondary Education
7.3: Use appropriate computer applications to perform mathematical calculations relevant to solving business problems.
Student Instructions BM
| Overview – Benchmark | |
| Your group has been given a dataset containing 240 records, located in the Student_BM tab of this spreadsheet.. | |
| Each student is only responsible for analyzing 60 of these record records – the specifics of which will be assigned by the instructor. | |
| It is important that each student has a unique 60 records, as the results will be an input into the CLC, and duplication of | |
| results is not helpful. Note that the data have been randomized, so the data given to your group are likely different than the | |
| data given to other groups. | |
| The intent of this assignment is for students to organize their data using a pivot table, get a graphical understanding | |
| of the data through a bar chart, then do hypothesis testing comparing Bo Diddly Tech results versus the national average. | |
| All of your analysis should be done in the Student_BM tab of this spreadsheet and submitted as part of the assignmemt. | |
| The location where the pivot table, bar chart, and relevant information should be placed in the Student_BM tab is | |
| indicated by RED instructions. | |
| Once completed, the Student_BM tab will serve as the basis for writing your management report. It is expected that any | |
| conclusions you draw in the management report will be consistent with the data and analyses contained in the spreadsheet. | |
| Instructions Data Analysis Component: | |
| 1. Make a pivot table with: Business Student (Rows), Athlete (Rows), Cheated (Columns), and Cheated (Summed Value). | |
| 2. Create a bar chart showing cheating by athletes and business students. | |
| 4. Determine if there is a statistical difference between nonathlete BDT business students and the national average for business | |
| students as reported by the Chronicle of Higher Education. | |
| 5. Determine if there is a statistical difference between athlete BDT business students and the national average for business | |
| students as reported by the Chronicle of Higher Education. | |
| 6. Determine if there is a statistical difference between BDT business students and the national average for business students | |
| as reported by the Chronicle of Higher Education. | |
| 7. Determine if there is a statistical difference between BDT nonbusiness students and the national average for nonbusiness | |
| students as reported by the Chronicle of Higher Education. | |
| Instructions Data Interpretation Component: | |
| Utilizing the data you have analyzed, write a managerial report of 500-800 words to the dean. The managerial report needs to | |
| include an introduction, analysis, conclusion, and a minimum of three supporting references. | |
| 1. Introduction (Define): Explain in your own words why you are providing this report and the problem(s) you are trying to solve. | |
| 2. Collect: Describe the data set you used. | |
| 3. Organize: Describe your pivot table. | |
| 4. Visualize: Include and describe your bar chart. | |
| 5. Analyze: Provide a summary of your conclusions based upon the four population proportion hypothesis tests. | |
| 6. The Dean has expressed a concern related to the amount of cheating currently taking place at Bo Diddley Tech and has strongly | |
| suggested that you “tweak” the statistical data such that it favors the image of the university. | |
| Discuss the potential use of unethical manipulation of statistical data to provide a biased outcome as well as the ethical counter | |
| proposal you would offer the dean in this scenario. | |
| 7. Conclusion: What advice would you give to the dean based on your analysis of the data? |
Student_BM
| College | Athlete | Cheated | 1. Pivot Table | Nationwide Average | % Cheated | |||||||||||
| Insert pivot table in this cell – F2 | Business | 56% | ||||||||||||||
| Nonbusiness | 47% | |||||||||||||||
| 2. Bar Chart | ||||||||||||||||
| Bar chart starts in this cell – F20 | ||||||||||||||||
| Insert the appropriate numbers into the hypothesis testing calculations below based upon your pivot table results. Note the results. | ||||||||||||||||
| 3-6 Hypothesis Test | ||||||||||||||||
| Business Nonathlete vs. National Average | Business Athlete vs. National Average | Business vs. National Average | Nonbusiness vs. National Average | |||||||||||||
| Proportion | Proportion | Proportion | Proportion | |||||||||||||
| Sample Size (n) =count(range) | Sample Size (n) =count(range) | Sample Size (n) =count(range) | Sample Size (n) =count(range) | |||||||||||||
| Response of Interest (ROI) | Cheated | Response of Interest (ROI) | Cheated | Response of Interest (ROI) | Cheated | Response of Interest (ROI) | Cheated | |||||||||
| Count for Response (CFR) =COUNTIF(range,ROI) | Count for Response (CFR) =COUNTIF(range,ROI) | Count for Response (CFR) =COUNTIF(range,ROI) | Count for Response (CFR) =COUNTIF(range,ROI) | |||||||||||||
| Sample Proportion (pbar) =CFR/n | Sample Proportion (pbar) =CFR/n | Sample Proportion (pbar) =CFR/n | Sample Proportion (pbar) =CFR/n | |||||||||||||
| Highlight your H0 and Ha | Two Tail H0: p = po Ha: p ≠ po Left Tail H0: p ≥ po Ha: p < po Right Tail H0: p ≤ po Ha: p > po | Highlight your H0 and Ha | Two Tail H0: p = po Ha: p ≠ po Left Tail H0: p ≥ po Ha: p < po Right Tail H0: p ≤ po Ha: p > po | Highlight your H0 and Ha | Two Tail H0: p = po Ha: p ≠ po Left Tail H0: p ≥ po Ha: p < po Right Tail H0: p ≤ po Ha: p > po | Highlight your H0 and Ha | Two Tail H0: p = po Ha: p ≠ po Left Tail H0: p ≥ po Ha: p < po Right Tail H0: p ≤ po Ha: p > po | |||||||||
| Hypothesized | 0.56 | Hypothesized | 0.56 | Hypothesized | 0.56 | Hypothesized | 0.47 | |||||||||
| Confidence Coefficient (Coe) | 0.95 | Confidence Coefficient (Coe) | 0.95 | Confidence Coefficient (Coe) | 0.95 | Confidence Coefficient (Coe) | 0.95 | |||||||||
| Level of Significance (alpha) =1-Coe | 0.05 | Level of Significance (alpha) =1-Coe | 0.05 | Level of Significance (alpha) =1-Coe | 0.05 | Level of Significance (alpha) =1-Coe | 0.05 | |||||||||
| Standard Error (StdError) =SQRT(Hypo*(1-Hypo)/n) | ERROR:#DIV/0! | Standard Error (StdError) =SQRT(Hypo*(1-Hypo)/n) | ERROR:#DIV/0! | Standard Error (StdError) =SQRT(Hypo*(1-Hypo)/n) | ERROR:#DIV/0! | Standard Error (StdError) =SQRT(Hypo*(1-Hypo)/n) | ERROR:#DIV/0! | |||||||||
| Test Statistic (Z-stat) =(pbar-Hypo)/StdError | ERROR:#DIV/0! | Test Statistic (Z-stat) =(pbar-Hypo)/StdError | ERROR:#DIV/0! | Test Statistic (Z-stat) =(pbar-Hypo)/StdError | ERROR:#DIV/0! | Test Statistic (Z-stat) =(pbar-Hypo)/StdError | ERROR:#DIV/0! | |||||||||
| Accept or Reject: Left Tail | ERROR:#DIV/0! | Accept or Reject: Left Tail | ERROR:#DIV/0! | Accept or Reject: Left Tail | ERROR:#DIV/0! | Accept or Reject: Left Tail | ERROR:#DIV/0! | |||||||||
| Accept or Reject: Right Tail | ERROR:#DIV/0! | Accept or Reject: Right Tail | ERROR:#DIV/0! | Accept or Reject: Right Tail | ERROR:#DIV/0! | Accept or Reject: Right Tail | ERROR:#DIV/0! | |||||||||
| Accept or Reject: Two Tail | ERROR:#DIV/0! | Accept or Reject: Two Tail | ERROR:#DIV/0! | Accept or Reject: Two Tail | ERROR:#DIV/0! | Accept or Reject: Two Tail | ERROR:#DIV/0! | |||||||||
| p-value (Lower Tail) =NORM.S.DIST(z,TRUE) | ERROR:#DIV/0! | p-value (Lower Tail) =NORM.S.DIST(z,TRUE) | ERROR:#DIV/0! | p-value (Lower Tail) =NORM.S.DIST(z,TRUE) | ERROR:#DIV/0! | p-value (Lower Tail) =NORM.S.DIST(z,TRUE) | ERROR:#DIV/0! | |||||||||
| p-value (Upper Tail) =1-LowerTail | ERROR:#DIV/0! | p-value (Upper Tail) =1-LowerTail | ERROR:#DIV/0! | p-value (Upper Tail) =1-LowerTail | ERROR:#DIV/0! | p-value (Upper Tail) =1-LowerTail | ERROR:#DIV/0! | |||||||||
| p-value (Two Tail) =2*MIN(LowerTail,UpperTail) | ERROR:#DIV/0! | p-value (Two Tail) =2*MIN(LowerTail,UpperTail) | ERROR:#DIV/0! | p-value (Two Tail) =2*MIN(LowerTail,UpperTail) | ERROR:#DIV/0! | p-value (Two Tail) =2*MIN(LowerTail,UpperTail) | ERROR:#DIV/0! | |||||||||
| Accept or Reject p-value: Left Tail | ERROR:#DIV/0! | Accept or Reject p-value: Left Tail | ERROR:#DIV/0! | Accept or Reject p-value: Left Tail | ERROR:#DIV/0! | Accept or Reject p-value: Left Tail | ERROR:#DIV/0! | |||||||||
| Accept or Reject p-value: Right Tail | ERROR:#DIV/0! | Accept or Reject p-value: Right Tail | ERROR:#DIV/0! | Accept or Reject p-value: Right Tail | ERROR:#DIV/0! | Accept or Reject p-value: Right Tail | ERROR:#DIV/0! | |||||||||
| Accept or Reject p-value: Two Tail | ERROR:#DIV/0! | Accept or Reject p-value: Two Tail | ERROR:#DIV/0! | Accept or Reject p-value: Two Tail | ERROR:#DIV/0! | Accept or Reject p-value: Two Tail | ERROR:#DIV/0! | |||||||||
| p-Lower Limit =pbar-CONFIDENCE.NORM(alpha,StdError,n) | ERROR:#DIV/0! | p-Lower Limit =pbar-CONFIDENCE.NORM(alpha,StdError,n) | ERROR:#DIV/0! | p-Lower Limit =pbar-CONFIDENCE.NORM(alpha,StdError,n) | ERROR:#DIV/0! | p-Lower Limit =pbar-CONFIDENCE.NORM(alpha,StdError,n) | ERROR:#DIV/0! | |||||||||
| p-Upper Limit =pbar+CONFIDENCE.NORM(alpha,StdError,n) | ERROR:#DIV/0! | p-Upper Limit =pbar+CONFIDENCE.NORM(al |
