Easton Technology

     

     

    y, a specialized software company that serves a large metropolitan area has been in business for the past
    several years. It currently has 140 service representatives on its payroll. It recently came to the notice
    of Mr. Johnson, a minority service rep of Easton Technology, that the salary of minority reps is
    considerably less than that of their white counterparts. This is despite the fact that the company claims to
    have been following a voluntary affirmative action program for the past two years. He and some other
    minority employees have threatened to take the case to Equal Employment Opportunity Commission (EEOC) and to
    file a case of racial discrimination in salaries against Easton Technology.
    The company president is genuinely concerned and insists that Easton Technology does not discriminate
    against any body, and that it only rewards performance and loyalty. He does not want the excellent
    reputation of his company to be tarnished in a lawsuit. He has talked to the disgruntled employees and has
    requested them to be patient until he has done his own investigation into the matter. He has hired your
    group as his consultants to investigate the salary discrimination charge.
    The President has put the employment and pay history data of the 140 service reps at your disposal. These
    data are contained in the Case 4 Data file. You will need to examine the employment and pay history data in
    this file in your attempt to resolve the discrimination charge brought forth by the minority employees.
    Based upon your analysis and interpretation of the employment and salary data, write a comprehensive report
    to the company president summarizing your analysis and findings. You will also have to present your findings
    to the management group of the company and the representatives of the minority employees. Remember that your
    charge is to investigate and report if salary differences based on race exist in the company. The questions
    given on the next page will guide your thinking. Use important details from your analysis of the data to
    support your conclusion and recommendations. Remember the president of Easton Technology is not a
    statistician but otherwise can digest technical reports easily.
    Data Description:
    ID: Sequential ID number assigned to employees
    PAYGRADE: Pay grade (=1, 2, or 3)
    GENDER: 1, if female, 0, if male
    RACE: 1, if white, 0, if minority
    MARRIED: 1, if married, 0, if not married
    AGE: Age in years at last birthday
    TENURE: Number of years employed as a service representative by Easton Technology
    RATING: Employee performance rating, on a 10-point scale; where 1 = poor
    1. What is the difference between the average salary of minorities and non-minorities? Does this evidence
    support the claims of the Mr. Johnson?
    2. What proportion of minority and non-minority employees are found in each pay grade? How do these
    proportions influence Mr. Johnson’s claim of salary discrimination? Do these proportional differences
    suggest discrimination in promotions across racial groups?
    3. What is the average tenure of minority and non-minority employees in each pay grade? Do these results
    suggest discrimination in promotions across racial groups?
    4. When was the company’s Affirmative Action program started? Does this information help explain why more
    minorities are in lower pay grades? Explain.
    5. What data should be examined to see whether Easton Technology rewards performance and loyalty, as claimed
    by the company President?
    6. What type of analyses would indicate if pay was dependent upon performance and loyalty? Do these results
    suggest that Easton Technology rewards performance and loyalty? Explain.
    7. Do your analyses support Mr. Johnson’s allegation of discrimination? Briefly explain why the salary data
    suggest apparent discrimination and what will "remedy" this salary discrepancy? Could you explain
    salary differences in terms of factors other than race?
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