Last week, Team B investigated the pay scales of certain positions between manufacturing and other industries. Team B sought to provide data and statistical analysis to assist human resource departments create more competitive pay scales in order to recruit and retain higher quality employees. Reliable information regarding pay scales will guide human resource departments within an organization in setting reasonable, competitive, salaries. According to Must Be the MoneyÃ¢ÂÂ¦and the Job Security (2006), salary level is the most important consideration for job candidates, above stability, advancement opportunities, and work environment. Furthermore, "businesses thatÃ¢ÂÂ¦offer competitive compensation are at an advantage in the hiring process" (Must Be the MoneyÃ¢ÂÂ¦and the Job Security, 2006, p. 6).
Team B conducted an ANOVA test last week to determine if employees who held clerical, management or sales positions in the manufacturing industry would make a higher or lower salary if they were to hold the same positions in other industries.
For the purposes of testing Team B used data provided by Wages and Wage Earners Data Set (2008). The ANOVA test results failed to reject the following null hypothesis: Ho = Employees in the manufacturing field, who hold clerical, management, or sales positions, have a mean salary that is higher than employees in the same positions in other industries.
This week, Team B will conduct further research to provide more testing on the null hypothesis by conducting a nonparametric hypothesis test. If a null hypothesis is not rejected in one hypothesis test, performing additional tests may provide further support and command more respect for the tested theory. Although, no matter how many tests are performed on a certain hypothesis, it will never be accepted as true, rather fail to be rejected (Doane & Seward, 2007). Therefore, relying on just one test may not produce the most accurate...