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DataIDSalaryCompa-ratioMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GradeDo not manipuilate Data set on this page, copy to another page to make changes154.50.956573485805.70METhe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 228.30.913315280703.90MBNote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.334.11.100313075513.61FB460.91.06857421001605.51METhe column labels in the table mean:549.21.0254836901605.71MDID – Employee sample number Salary – Salary in thousands 674.11.1066736701204.51MFAge – Age in yearsPerformance Rating - Appraisal rating (employee evaluation score)741.41.0344032100815.71FCService – Years of service (rounded)Gender – 0 = male, 1 = female 822.80.992233290915.81FAMidpoint – salary grade midpoint Raise – percent of last raise9731.089674910010041MFGrade – job/pay gradeDegree (0= BS\BA 1 = MS)1023.31.014233080714.71FAGender1 (Male or Female)Compa-ratio - salary divided by midpoint1124.31.05723411001914.81FA1259.71.0475752952204.50ME1341.81.0444030100214.70FC14251.08523329012161FA1522.60.983233280814.91FA1648.51.213404490405.70MC1763.11.1075727553131FE1836.21.1673131801115.60FB1923.91.039233285104.61MA2035.51.1443144701614.80FB2178.91.1786743951306.31MF2257.61.199484865613.81FD2322.20.964233665613.30FA2453.41.112483075913.80FD2523.61.0282341704040MA2622.30.971232295216.20FA2746.21.156403580703.91MC2874.41.111674495914.40FF2975.61.129675295505.40MF3047.50.9894845901804.30MD3122.90.995232960413.91FA3228.10.906312595405.60MB3363.71.117573590905.51ME3426.90.869312680204.91MB3522.70.987232390415.30FA3624.41.059232775314.30FA3723.81.034232295216.20FA3864.61.1335745951104.50ME3937.31.202312790615.50FB4023.71.031232490206.30MA4140.31.008402580504.30MC4224.41.0592332100815.71FA4372.31.0796742952015.50FF4465.91.1565745901605.21ME4549.91.040483695815.21FD4657.41.0075739752003.91ME47560.982573795505.51ME4868.11.1955734901115.31FE4966.21.1615741952106.60ME5061.71.0835738801204.60ME Week 1Week 1: Descriptive Statistics, including ProbabilityWhile the lectures will examine our equal pay question from the compa-ratio viewpoint, our weekly assignments will focus onexamining the issue using the salary measure.The purpose of this assignmnent is two fold:1. Demonstrate mastery with Excel tools.2. Develop descriptive statistics to help examine the question.3. Interpret descriptive outcomesThe first issue in examining salary data to determine if we - as a company - are paying males and females equally for doing equal work is to develop somedescriptive statistics to give us something to make a preliminary decision on whether we have an issue or not.1Descriptive Statistics: Develop basic descriptive statistics for SalaryThe first step in analyzing data sets is to find some summary descriptive statistics for key variables. Suggestion: Copy the gender1 and salary columns from the Data tab t.

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- DataIDSalaryCompa-ratioMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GradeDo not manipuilate Data set on this page, copy to another page to make changes154.50.956573485805.70METhe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 228.30.913315280703.90MBNote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.334.11.100313075513.61FB460.91.06857421001605.51M EThe column labels in the table mean:549.21.0254836901605.71MDID – Employee sample number Salary – Salary in thousands 674.11.1066736701204.51MFAge – Age in yearsPerformance Rating - Appraisal rating (employee evaluation score)741.41.0344032100815.71FCService – Years of service (rounded)Gender – 0 = male, 1 = female 822.80.992233290915.81FAMidpoint – salary grade midpoint Raise – percent of last raise9731.089674910010041MFGrade – job/pay gradeDegree (0= BSBA 1 = MS)1023.31.014233080714.71FAGender1 (Male or Female)Compa-ratio - salary divided by midpoint1124.31.05723411001914.81FA1259.71.047575295220 4.50ME1341.81.0444030100214.70FC14251.08523329012161F A1522.60.983233280814.91FA1648.51.213404490405.70MC17 63.11.1075727553131FE1836.21.1673131801115.60FB1923.91. 039233285104.61MA2035.51.1443144701614.80FB2178.91.178 6743951306.31MF2257.61.199484865613.81FD2322.20.964233 665613.30FA2453.41.112483075913.80FD2523.61.0282341704 040MA2622.30.971232295216.20FA2746.21.156403580703.91 MC2874.41.111674495914.40FF2975.61.129675295505.40MF3 047.50.9894845901804.30MD3122.90.995232960413.91FA3228 .10.906312595405.60MB3363.71.117573590905.51ME3426.90. 869312680204.91MB3522.70.987232390415.30FA3624.41.0592 32775314.30FA3723.81.034232295216.20FA3864.61.13357459
- 51104.50ME3937.31.202312790615.50FB4023.71.03123249020 6.30MA4140.31.008402580504.30MC4224.41.0592332100815.7 1FA4372.31.0796742952015.50FF4465.91.1565745901605.21M E4549.91.040483695815.21FD4657.41.0075739752003.91ME47 560.982573795505.51ME4868.11.1955734901115.31FE4966.21. 1615741952106.60ME5061.71.0835738801204.60ME Week 1Week 1: Descriptive Statistics, including ProbabilityWhile the lectures will examine our equal pay question from the compa-ratio viewpoint, our weekly assignments will focus onexamining the issue using the salary measure.The purpose of this assignmnent is two fold:1. Demonstrate mastery with Excel tools.2. Develop descriptive statistics to help examine the question.3. Interpret descriptive outcomesThe first issue in examining salary data to determine if we - as a company - are paying males and females equally for doing equal work is to develop somedescriptive statistics to give us something to make a preliminary decision on whether we have an issue or not.1Descriptive Statistics: Develop basic descriptive statistics for SalaryThe first step in analyzing data sets is to find some summary descriptive statistics for key variables. Suggestion: Copy the gender1 and salary columns from the Data tab to columns T and U at the right.Then use Data Sort (by gender1) to get all the male and female salary values grouped together.a. Use the Descriptive Statistics function in the Data Analysis tab Place Excel outcome in Cell K19to develop the descriptive statistics summary for the overall group's overall salary. (Place K19 in output range.)Highlight the mean, sample standard deviation, and range.b.Using Fx (or formula) functions find the following (be sure to show the formula and not just the value in each cell) asked for salary statistics for each gender:Mean:Sample Standard Deviation:Range:2Develop a 5-number summary for the overall, male, and female SALARY variable.For full credit, use the excel formulas in each cell rather than simply the numerical answer.Max3rd QMidpoint1st QMin3Location Measures: comparing Male and Female midpoints to the overall Salary
- data range.For full credit, show the excel formulas in each cell rather than simply the numerical answer.Using the entire Salary range and the M and F midpoints found in Q2MaleFemalea. What would each midpoint's percentile rank be in the overall range?Use Excel's =PERCENTRANK.EXC functionb. What is the normal curve z value for each midpoint within overall range?Use Excel's =STANDARDIZE function 4Probability Measures: comparing Male and Female midpoints to the overall Salary data rangeFor full credit, show the excel formulas in each cell rather than simply the numerical answer.Using the entire Salary range and the M and F midpoints found in Q2, findMaleFemalea. The Empirical Probability of equaling or exceeding (=>) that value forShow the calculation formula = value/50 or =countif(range,">="&cell)/50b. The Normal curve Prob of => that value for each groupUse "=1-NORM.S.DIST" functionNote: be sure to use the ENTIRE salary range for part a when finding the probability.5Conclusions: What do you make of these results?Be sure to include findings from this week's lectures as well.In comparing the overall, male, and female outcomes, what relationship(s) see, to exist between the data sets? Your findings:The lecture's related findings:Overall conclusion:What does this suggest about our equal pay for equal work question? Week 2Week 2: Identifying Significant Differences - part 1To Ensure full credit for each question, you need to show how you got your results. This involves either showing where the data you used is located or showing the excel formula in each cell.Be sure to copy the appropriate data columns from the data tab to the right for your use this week.As with our examination of compa-ratio in the lecture, the first question we have about salary between the genders involves equality - are they the same or different?What we do, depends upon our findings.1As with the compa-ratio lecture example, we want to examine salary variation within the groups - are they equal?Use Cell K10 for the Excel test outcome location.aWhat is the data input ranged used for this question:b Which is needed for this question: a
- one- or two-tail hypothesis statement and test ?Answer:Why:c. Step 1:Ho:Ha:Step 2:Significance (Alpha):Step 3:Test Statistic and test:Why this test?Step 4:Decision rule:Step 5:Conduct the test - place test function in cell k10Step 6:Conclusion and InterpretationWhat is the p-value:What is your decision: REJ or NOT reject the null?Why?What is your conclusion about the variance in the population for male and female salaries?2Once we know about variance quality, we can move on to means: Are male and female average salaries equal?Use Cell K35 for the Excel test outcome location.(Regardless of the outcome of the above F-test, assume equal variances for this test.)aWhat is the data input ranged used for this question:b Does this question need a one or two-tail hypothesis statement and test?Why:c. Step 1:Ho:Ha:Step 2:Significance (Alpha):Step 3:Test Statistic and test:Why this test?Step 4:Decision rule:Step 5:Conduct the test - place test function in cell K35Step 6:Conclusion and InterpretationWhat is the p-value:What is your decision: REJ or NOT reject the null?Why?What is your conclusion about the means in the population for male and female salaries?3Education is often a factor in pay differences.Do employees with an advanced degree (degree = 1) have higher average salaries?Use Cell K60 for the Excel test outcome location.Note: assume equal variance for the salaries in each degree for this question.aWhat is the data input ranged used for this question:b Does this question need a one or two-tail hypothesis statement and test?Why:c. Step 1:Ho:Ha:Step 2:Significance (Alpha):Step 3:Test Statistic and test:Why this test?Step 4:Decision rule:Step 5:Conduct the test - place test function in cell K60Step 6:Conclusion and InterpretationWhat is the p-value:Is the t value in the t-distribution tail indicated by the arrow in the Ha claim?What is your decision: REJ or NOT reject the null?Why?What is your conclusion about the impact of education on average salaries?4Considering both the compa- ratio information from the lectures and your salary information, what conclusions can you reach about equal pay for equal work?Your findings:The lecture's related findings:Overall
- conclusion:Why - what statistical results support this conclusion? Week 3Week 3: Identifying Significant Differences - part 2Data Input Table:Salary Range GroupsGroup name:ABCDEFTo Ensure full credit for each question, you need to show how you got your results. This involves either showing where the data you used is located List salaries within each gradeor showing the excel formula in each cell.Be sure to copy the appropriate data columns from the data tab to the right for your use this week.1A good pay program will have different average salaries by grade. Is this the case for our company?aWhat is the data input ranged used for this question:Use Cell K08 for the Excel test outcome location.Note: assume equal variances for each grade, even though this may not be accurate, for purposes of this question.b. Step 1:Ho:Ha:Step 2:Significance (Alpha):Step 3:Test Statistic and test:Why this test?Step 4:Decision rule:Step 5:Conduct the test - place test function in cell K08Step 6:Conclusion and InterpretationWhat is the p- value:What is your decision: REJ or NOT reject the null?Why?What is your conclusion about the means in the population for grade salaries?2If the null hypothesis in question 1 was rejected, which pairs of means differ?(Use the values from the ANOVA table to complete the follow table.)Groups ComparedMean Diff.T value used+/- TermLowto HighDifference Significant?Why?A-BA-CA-DA-EA-FB-CB- DB-EB-EC-DC-EC-FD-ED-FE-F3One issue in salary is the grade an employee is in - higher grades have higher salaries.This suggests that one question to ask is if males and females are distributed in a similar pattern across the salary grades?aWhat is the data input ranged used for this question:Use Cell K54 for the Excel test outcome location.b. Step 1:Ho:Ha:Step 2:Significance (Alpha):Step 3:Test Statistic and test:Place the actual distribution in the table below.Why this test?ABCDEFSumStep 4:Decision rule:Male0Step 5:Conduct the test - place test function in cell K54Female0Sum:0000000Step 6:Conclusion and
- InterpretationPlace the expected distribution in the table below.What is the p-value:ABCDEFWhat is your decision: REJ or NOT reject the null?Male0Why?Female0What is your conclusion about the means in the population for male and female salaries?Sum:00000004What implications do this week's analysis have for our equal pay question?Your findings:The lecture's related findings:Overall conclusion:Why - what statistical results support this conclusion? Week 4Week 4: Identifying relationships - correlations and regressionTo Ensure full credit for each question, you need to show how you got your results. This involves either showing where the data you used is located or showing the excel formula in each cell.Be sure to copy the appropriate data columns from the data tab to the right for your use this week.1What is the correlation between and among the interval/ratio level variables with salary? (Do not include compa-ratio in this question.)a. Create the correlation table.Use Cell K08 for the Excel test outcome location.i.What is the data input ranged used for this question:ii. Create a correlation table in cell K08.b. Technically, we should perform a hypothesis testing on each correlation to determine if it is significant or not. However, we can be faithful to the process and save some time by finding the minimum correlation that would result in a two tail rejection of the null.We can then compare each correlation to this value, and those exceeding it (in either a positive or negative direction) can be considered statistically significant. i. What is the t- value we would use to cut off the two tails?T = ii. What is the associated correlation value related to this t-value? r =c. What variable(s) is(are) significantly correlated to salary?d. Are there any surprises - correlations you though would be significant and are not, or non significant correlations you thought would be?e. Why does or does not this information help answer our equal pay question?2Perform a regression analysis using salary as the dependent variable and all of the variables used in Q1. Add thetwo dummy variables - gender and education - to your list of independent variables. Show the
- result, and interpret your findings by answering the following questions.Suggestion: Add the dummy variables values to the right of the last data columns used for Q1.What is the multiple regression equation predicting/explaining salary using all of our possible variables except compa-ratio?a.What is the data input ranged used for this question:b. Step 1: State the appropriate hypothesis statements:Use Cell M34 for the Excel test outcome location.Ho:Ha:Step 2:Significance (Alpha):Step 3:Test Statistic and test:Why this test?Step 4:Decision rule:Step 5:Conduct the test - place test function in cell M34Step 6:Conclusion and InterpretationWhat is the p-value:What is your decision: REJ or NOT reject the null?Why?What is your conclusion about the factors influencing the population salary values?c.If we rejected the null hypothesis, we need to test the significance of each of the variable coefficients.Step 1: State the appropriate coefficient hypothesis statements:(Write a single pair, we will use it for each variable separately.)Ho:Ha:Step 2:Significance (Alpha):Step 3:Test Statistic and test:Why this test?Step 4:Decision rule:Step 5:Conduct the testNote, in this case the test has been performed and is part of the Regression output above.Step 6:Conclusion and InterpretationPlace the t and p-values in the following tableIdentify your decision on rejecting the null for each variable. If you reject the null, place the coefficient in the table.MidpointAgePerf. Rat.SeniorityRaiseGenderDegreet- value:P-value:Rejection Decision:If Null is rejected, what is the variable's coefficient value?Using the intercept coefficient and only the significant variables, what is the equation?Salary = d.Is gender a significant factor in salary?e.Regardless of statistical significance, who gets paid more with all other things being equal?f.How do we know? 3After considering the compa- ratio based results in the lectures and your salary based results, what else would you like to knowbefore answering our question on equal pay? Why?4Between the lecture results and your results, what is your answer to the questionof equal pay for equal work for males and females? Why?Your findings:The
- lecture's related findings:Overall conclusion:5What does regression analysis show us about analyzing complex measures? Running Head: REWARD STRATEGIES FOR ATTRACTING, EVALUATING, AND RETAINING PROFESSIONALS REWARD STRATEGIES FOR ATTRACTING, EVALUATING, AND RETAINING PROFESSIONALS 3 Reward Strategies for Attracting, Evaluating, and Retaining Professionals Carlos I. Macias-Ramirez Dr. Cassandra Shaw Business Ethics September 30, 2018 Reward Strategies for Attracting, Evaluating, and Retaining Professionals I. Introduction A. The reward system is a strategy that used by organization as well as business to motivate their employee to either behave a
- particular way or strive to achieve a particular goal, which is related to the organization. An employee may provide a reward such as money, promotion or any other related benefit for acting in a particular way or attaining the set goal. Most organizations tend to use this system to develop an organizational culture, which defines the organizational capabilities to its clients. B. In order to practice “The Reward System”, it has become essential for organizations to attract quality employees, evaluate and enumerate employees, and retain professionals. II. Body A. Attracting and Recruitment: a. Breaugh (2009) outlines seven principles as the effective model for the recruitment process. b. The target audience in for recruitment is fundamentally the most important factor. Understanding what a company’s requirements will, in contrast, address the target audience (Breaugh, 2000). B. Evaluate and Numerate Employees: a. I will take a two-prong approach to describe evaluation and enumerations in the workforce. i. First, the Apathetic Culture, this “culture is not related to performance” (Glinow, 1985, p. 197). It is virtually non- existent and employees essentially promote automatically. ii. Second, is enumerating employees based on position and grade. This is similar to the United States Army, Evaluation Entry System (EES). 1. Pros and Con of enumerating an evaluation. C. Retain Professionals: a. How to “Keep Me.” A motto utilized by Kochanski (2001), elaborates how to retain technical professionals. In my perspective, similar to technical professionals, the way to retain a professional is by “direct financial reward, indirect financial reward, career reward, work content, and affiliation. b. Further, there is something called a “Caring Culture.” Caring Culture strives to achieve the happiness among working professionals. Companies use a “Caring Culture” to retain
- professionals by promoting internally (Glinow, 1985). III. Conclusion A. As a result, reward strategies become more in demand by the millennial generation, it is important to understand the full cycle of attracting quality employees, evaluating and numerating employees, and retaining professionals. There are many methods to use and strategies to follow, but fundamentally, it is important to understand societal needs and requirements. As society evolves and demands shift, it is important to address considerations when developing a reward strategy. References Trevino, L. K. (2016). Managing Business Ethics: Straight Talk about How to Do It Right. Hoboken: Wiley and sons. Breaugh, J. A.; Starke, M. (2000). Recruiting and Attracting Talent. Society for Human Resource Management. Alexandria, VA. James Kochanski, & Gerald Ledford. (2001). “How to Keep Me”—Retaining Technical Professionals. Research Technology Management, (3), 31. Retrieved from http://vlib.excelsior.edu/login?url=https://search.ebscohost.com/ login.aspx?direct=true&db=edsjsr&AN=edsjsr.24133992&site=e ds-live&scope=site Von Glinow, M. A. (1985). Reward Strategies for Attracting, Evaluating, and Retaining Professionals. Human Resource Management, 24(2), 191–206. Retrieved from http://vlib.excelsior.edu/login?url=https://search.ebscohost.com/ login.aspx?direct=true&db=bth&AN=7234572&site=eds- live&scope=site Running Head: REWARD STRATEGIES FOR ATTRACTING, EVALUATING, AND RETAINING PROFESSIONALS
- Reward Strategies for Attracting, Evaluating, and Retaining Professionals Carlos I. Macias - Rami rez Dr. Cassandra Shaw Business Ethics September 30, 2018
- Running Head: REWARD STRATEGIES FOR ATTRACTING, EVALUATING, AND RETAINING PROFESSIONALS Reward Strategies for Attracting, Evaluating, and Retaining Professionals Carlos I. Macias-Ramirez Dr. Cassandra Shaw Business Ethics September 30, 2018 DataIDSalaryCompa-ratioMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GradeDo not manipuilate Data set on this page, copy to another page to make changes154.50.956573485805.70METhe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)?
- 228.30.913315280703.90MBNote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.334.11.100313075513.61FB460.91.06857421001605.51M EThe column labels in the table mean:549.21.0254836901605.71MDID – Employee sample number Salary – Salary in thousands 674.11.1066736701204.51MFAge – Age in yearsPerformance Rating - Appraisal rating (employee evaluation score)741.41.0344032100815.71FCService – Years of service (rounded)Gender – 0 = male, 1 = female 822.80.992233290915.81FAMidpoint – salary grade midpoint Raise – percent of last raise9731.089674910010041MFGrade – job/pay gradeDegree (0= BSBA 1 = MS)1023.31.014233080714.71FAGender1 (Male or Female)Compa-ratio - salary divided by midpoint1124.31.05723411001914.81FA1259.71.047575295220 4.50ME1341.81.0444030100214.70FC14251.08523329012161F A1522.60.983233280814.91FA1648.51.213404490405.70MC17 63.11.1075727553131FE1836.21.1673131801115.60FB1923.91. 039233285104.61MA2035.51.1443144701614.80FB2178.91.178 6743951306.31MF2257.61.199484865613.81FD2322.20.964233 665613.30FA2453.41.112483075913.80FD2523.61.0282341704 040MA2622.30.971232295216.20FA2746.21.156403580703.91 MC2874.41.111674495914.40FF2975.61.129675295505.40MF3 047.50.9894845901804.30MD3122.90.995232960413.91FA3228 .10.906312595405.60MB3363.71.117573590905.51ME3426.90. 869312680204.91MB3522.70.987232390415.30FA3624.41.0592 32775314.30FA3723.81.034232295216.20FA3864.61.13357459 51104.50ME3937.31.202312790615.50FB4023.71.03123249020 6.30MA4140.31.008402580504.30MC4224.41.0592332100815.7 1FA4372.31.0796742952015.50FF4465.91.1565745901605.21M E4549.91.040483695815.21FD4657.41.0075739752003.91ME47 560.982573795505.51ME4868.11.1955734901115.31FE4966.21. 1615741952106.60ME5061.71.0835738801204.60ME Week 3Week 3: Identifying Significant Differences - part 2Data Input Table:Salary Range GroupsGroup name:ABCDEFTo
- Ensure full credit for each question, you need to show how you got your results. This involves either showing where the data you used is located List salaries within each gradeor showing the excel formula in each cell.Be sure to copy the appropriate data columns from the data tab to the right for your use this week.1A good pay program will have different average salaries by grade. Is this the case for our company?aWhat is the data input ranged used for this question:Use Cell K08 for the Excel test outcome location.Note: assume equal variances for each grade, even though this may not be accurate, for purposes of this question.b. Step 1:Ho:Ha:Step 2:Significance (Alpha):Step 3:Test Statistic and test:Why this test?Step 4:Decision rule:Step 5:Conduct the test - place test function in cell K08Step 6:Conclusion and InterpretationWhat is the p- value:What is your decision: REJ or NOT reject the null?Why?What is your conclusion about the means in the population for grade salaries?2If the null hypothesis in question 1 was rejected, which pairs of means differ?(Use the values from the ANOVA table to complete the follow table.)Groups ComparedMean Diff.T value used+/- TermLowto HighDifference Significant?Why?A-BA-CA-DA-EA-FB-CB- DB-EB-EC-DC-EC-FD-ED-FE-F3One issue in salary is the grade an employee is in - higher grades have higher salaries.This suggests that one question to ask is if males and females are distributed in a similar pattern across the salary grades?aWhat is the data input ranged used for this question:Use Cell K54 for the Excel test outcome location.b. Step 1:Ho:Ha:Step 2:Significance (Alpha):Step 3:Test Statistic and test:Place the actual distribution in the table below.Why this test?ABCDEFSumStep 4:Decision rule:Male0Step 5:Conduct the test - place test function in cell K54Female0Sum:0000000Step 6:Conclusion and InterpretationPlace the expected distribution in the table below.What is the p-value:ABCDEFWhat is your decision: REJ or NOT reject the null?Male0Why?Female0What is your conclusion about the means in the population for male and
- female salaries?Sum:00000004What implications do this week's analysis have for our equal pay question?Your findings:The lecture's related findings:Overall conclusion:Why - what statistical results support this conclusion?