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Statistical Study of Cheating Rates Among Business Students at Bo Diddey Tech

October 25, 2023
Dr. Nakamura
Dr. Nakamura
🇯🇵 Japan
Statistical Analysis
Dr. Nakamura holds a PhD in Statistical Engineering from the University of Tokyo. With over 350 homework completed, he has extensive experience in statistical reliability analysis. His academic journey and practical work make him a seasoned expert on developing and applying advanced statistical models for engineering systems. Dr. Nakamura's insights help students grasp complex concepts effectively.
Statistical Analysis
Key Topics
  • Problem Description:
    • Solution
    • Conclusion:
    • Recommendations:
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Academic integrity and ethics are fundamental values that underpin the educational landscape. In an age where information is easily accessible, concerns about cheating have become a prevalent issue. In the context of Bo Diddey Tech (BDT), this study delves into the ethical concerns surrounding academic integrity and cheating rates among business students.

Problem Description:

The 2008 financial crisis was partly attributed to unethical business behavior, which raised concerns about the prevalence of cheating among business students. This study, which can be instrumental in providing Data analysis homework, aims to investigate whether business students at Bo Diddey Tech (BDT) have a significantly higher rate of cheating compared to non-business students. Additionally, it explores whether being an athlete influences the cheating rate among business students. The association between being a business student or not, and being an athlete or not with cheating is also examined.

Solution

Data Collection: The dataset comprises data from 240 graduating students at BDT, including 117 business students and 123 non-business students. An anonymous exit survey collected data on the students' college, their status as athletes, and other relevant questions to determine whether a student should be considered a cheater.

Data Organization:

  • Among the 117 business students, 84 (71.79%) admitted to cheating, while 33 (28.29%) did not cheat.
  • Among the 123 non-business students, 61 (49.59%) admitted to cheating, while 62 (50.41%) did not cheat.
  • Of the business students who cheated, 38 (45.24%) were athletes, and 46 (54.76%) were non-athletes.
  • Among non-cheating business students, 24 (72.73%) were athletes, and 9 (27.27%) were non-athletes.
  • Of the non-business students who cheated, 39 (63.93%) were athletes, and 22 (36.07%) were non-athletes.
  • Among non-cheating non-business students, 28 (45.16%) were athletes, and 34 (54.84%) were non-athletes.

Pivot Table: The pivot table provides a summarized view of the cheating data across different categories, showing the counts of students who cheated or did not cheat based on their college and athletic participation.

    Count of Cheated        Column Labels        College        No        Yes        Grand Total    
   Bus      33      84      117   
   Ath      24      38      62   
   Non_Ath      9      46      55   
   Non_Bus      62      61      123   
   Ath      28      39      67   
   Non_Ath      34      22      56   
   Grand Total      95      145      240   

Visualization: The bar chart below illustrates the distribution of cheaters across different categories. Notably, the highest number of cheaters is found among non-athlete business students. Among non-business, non-athlete students, non-cheaters outnumber cheaters.

Analysis: Ethical Summary:

Unethical behavior can manifest in various ways, including manipulating data to achieve desired outcomes, as discussed by the dean in this context. Such actions obstruct the data's ability to reveal the truth.

  • The act of tweaking data to present a favorable image of the university is unethical and counterproductive in addressing cheating concerns. Letting the data speak for itself is the ethical approach.
  • The research's purpose is to identify problems and seek solutions. Instead of concealing the fact that cheating is more prevalent among business students, the university should acknowledge the issue and work to eradicate it.

Conclusion:

Based on the analysis, the following conclusions can be drawn:

  1. Business students exhibit a significantly higher rate of cheating compared to non-business students.
  2. Athletic participation appears to mitigate cheating among business students, as cheating rates among business athlete students are not significantly different from the national average. In contrast, cheating rates among business non-athlete students significantly differ from the national average.
  3. College and athletic participation are associated with cheating at BDT.

Recommendations:

To address the issue of cheating among students at BDT, the following recommendations are made:

  • Implement technologies designed to reduce cheating, with a particular focus on business students.
  • Develop initiatives to instill the spirit of fair play, often seen in athletes, among non-athlete students. This can contribute to a more ethical academic environment.

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