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You will prepare the SHRM case analysis on “Designing a Pay Structure,” which consists of your completion of Tasks A–J that simulate the creation of a compensation system for an organization in meeting its goals and supporting its mission. In your analysis, respond to the following tasks found in the case study by using Excel.

Your case analysis should consist of:

Task A: Create a complete job description for the benefits manager position using O*NET.

Task B: Calculate the job evaluation points for the administrative assistant, payroll assistant, operational analyst, and benefits manager jobs. Provide a rationale for assigning specific degrees to the various jobs.

Task C: If there were any outliers (i.e., extreme data points) in the data, what would you recommend doing with them? From this point forward, assume no extreme data points exist in the dataset.

Task D: Conduct a simple regression in Excel to create a market pay line by entering the job evaluation points (on the X axis) and the respective weighted average market base pay (on the Y axis) for each benchmark job.

Task E: What is your R squared (variance explained)? Is it sufficient to proceed?

Task F: Calculate the predicted base pay for each benchmark job.

Task G: Because your company wants to lead in base pay by 3%, adjust the predicted pay rates to determine the base pay rate you will offer for each benchmark job.

Task H: Create pay grades by combining any benchmark jobs that are substantially comparable for pay purposes. Clearly label your pay grades and explain why you combined any benchmark jobs to form a grade.

Task I: Use your answer to Task H to determine the pay range (i.e., minimum and maximum) for each pay grade.

Task J: Given the pay structure you have generated, consider the following: Does this pay structure make good business sense? Do you think it is consistent with the organization’s business strategy? What are the implications of this pay structure for other HR systems, such as retention and recruiting?

Your analysis of this case and your written submission should reflect an understanding of the critical issues of the case, integrating the material covered in the text, and present concise and well-reasoned justifications for the stance that you take. You are to complete this case analysis using Excel in a spreadsheet analysis format.

You may discuss your case analysis assignment with the class, but you must submit your own original work.

Case analysis tips: Avoid common errors in case analyses, such as:

Focusing too heavily on minor issues.

Lamenting because of insufficient data in the case and ignoring creative alternatives.

Rehashing of case data — you should assume the reader knows the case.

Not appropriately evaluating the quality of the case’s data.

Obscuring the quantitative analysis or making it difficult to understand.

Typical “minus (–)” grades result from submissions that:

Are late.

Are not well integrated and lack clarity.

Do not address timing issues.

Do not recognize the cost implications or are not practical.

Get carried away with personal biases and are not pertinent to the key issues.

Are not thoroughly proofread and corrected.

Make sure your document includes:

Your name

Date

Course name and section number

Unit number

Case name

Page numbers

The case analysis should contain Tasks A–J stated in the case in Excel. Check for correct spelling, grammar, punctuation, mechanics, and usage. Citations should be in APA style.

The case study presented asks you to work through calculations for a pay structure involving 5 different positions. This is a pretty heavy set of tasks to accomplish in one week. Fortunately, I have been able to readjust this a bit so that the work load would be manageable in a week’s time.

FOR THE UNIT 4 ASSIGNMENT, YOU ONLY NEED TO CALCULATE FOR THE FRONT DESK RECEPTIONIST AND THE BENEFITS MANAGER POSITIONS. You do NOT have to calculate for the other 3 positions: Administrative Assistant, Payroll Assistant, Operations Analyst.

Additionally, since this involves math (and a little statistics), I have put together supportive material (see below) to help guide you through how to do this. PLEASE just follow the guidance below and you will be able to move through this easily.

PLEASE be sure to not wait until the last minute to do the Unit 4 Assignment. There’s a lot to it, so I don’t want anyone to be surprised.

I can’t provide a sample since that would contain the answers… But that’s okay, you can take this piece by piece..! PLEASE READ THESE DETAILS BELOW CAREFULLY. If you take it slowly, this goes pretty well. (And! If you get to the math portion below and you feel out of your element, PLEASE touch base with the Math Tutor (see separate announcement about Academic Support Centers for links). They offer wonderful assistance!

First, let’s take this steps..!

In Task A, you have to create one job description for the Benefits Manager. There are details in how to approach this and in the Appendix there are other job descriptions for the other positions so you can see how these should look. The case study provides suggestions about where to go to get info on this job description. So please read through those details for more guidance.

Next, in Task B, you will calculate the job evaluation points for positions. If you look just above the Task B item (on the previous page), you’ll see info and a sample of how to do this based on the receptionist position. Don’t forget to provide your rationale for the job evaluation points assigned. Use those same items as in the sample chart – Skills, Responsibility, Effort (and their subcategories). You can change up the percentages these are worth as you see fit for each job… Please be sure to remember that you have to take into consideration what would be required for each of the elements in the job evaluation – again read the sample that gets you started. For instance, in the education area, please remember that the weight would be more for a job that needs a Bachelor’s degree rather than a high school diploma/GED. Please also be sure that you multiple the Degree times the Weight to get the Points for each line (far right item). Then total that Points column at the bottom. This needs to be done for each Benchmark job. And each one should have a different Points total as they have different requirements from each other.

And in Task C, here’s a little further help/guidance with regard to completing this. The first part deals with what to do with outliers. That would require you to address this via text in your document. (Again your readings will support this, and you can also do more research online if you’d like. Don’t forget to use supporting citations when you can – these strengthen your academic work..!)

The second part deals with calculating weighted means. (This isn’t as bad as it sounds – I promise..!)

Weighted means of base pay should be calculated for each benchmark job from the survey data. Weighted means, as compared to simple means, are calculated to better represent the market data (Milkovich & Newman, 2008). A simple mean would be calculated by adding up the average base pay rates and dividing by the number of organizations (six in this case); but small and large companies would both be given the same weight if using a simple mean. A weighted mean gives equal weight to each job incumbent’s wage and thus is more representative of the data. For example:

Mean # of employees

Co. A 30,000 2

Co. B 15,000 10

The simple mean salary is $22,500.

[(30000 + 15000) / 2 = 22500]

But the weighted mean salary is $17,500.

[(2/12 * 30000) + (10/12 * 15000) = 17500]

For each position, you take the number of employees in Co. A, which is 2. Divide that into the total number of employees in all companies, which is 12. Or 2/12… Which equals 0.16667.

Then multiply that by the mean salary in Co. A, which is $30,000.

So, 30,000 times 0.16667 equals 5,000.

Then for the next company, Co. B, you do the same with those numbers. You take the number of employees in Co. B, which is 10. Divide that into the total number of employees in all companies, which is 12. Or 10/12… Which equals 0.833333.

Then multiply that by the mean salary in Co. B, which is $15,000.

So, 15,000 times 0. 0.833333 equals 12,500.

Do this for the Front Desk Receptionist and the Benefits Manager companies.

For Task D, you are asked to do some statistics with a regression analysis. Don’t worry..! Keep reading and you will see a link to an online calculator that can help you with this! 😉

Regression analysis is “the statistical tool for the investigation of the relationship between variables” (Sykes n.d.). It is used when data is analyzed to determine the causal effect of one variable upon another variable. For example, the effect of the increased cost of a gallon/litre of gasoline/petrol on the demand for that product is determined via “regression analysis”.

If you want to do the regression analysis calculation in Excel (rather than using the online calculator link that is below), you can go to:

https://chicagounbound.uchicago.edu/law_and_economics/51/ – here you will find the article “An introduction to Regression Analysis” by Dr. Alan Sykes that may help you understand regression analysis more clearly and help you in answering the discussion questions below.

Video for how to run the regression analysis in Excel:

http://www.wikihow.com/Run-Regression-Analysis-in-Microsoft-Excel

NOTE: I have Excel 2010, so getting the Regression Toolpak added in was easy. You may have to add this Excel Analysis Toolpak in – no matter what version of Excel you may have. Here is link to how to add that toolpak, for the various Excel versions:

https://support.office.com/en-us/article/load-the-analysis-toolpak-in-excel-6a63e598-cd6d-42e3-9317-6b40ba1a66b4

AND FINALLY, IF YOU WANT TO JUST USE AN ONLINE SIMPLE REGRESSION CALCULATOR FOR TASKS D AND E (GETTING THE R SQUARED NUMBER BUT STILL YOU HAVE TO ANSWER THE QUESTION IN TASK E AS WELL…) (AND SKIP USING EXCEL), YOU CAN GO HERE:

http://www.graphpad.com/quickcalcs/linear1/

Here you would plug in your Job Evaluation Points for each position in Task B (under the X column), and also the corresponding weighted average salary for each position in Task C (under the Y column). It would look something like this:

Regression Analysis

Job Evaluation Weighted

Points Avg. $

X Y

Recept. 120 19944.44

Admin Asst. 145 29458.33

Pay Asst. 175 34000

Ops Analyst 215 56875

Ben Mgr. 245 62900

NOTE: THE JOB EVALUATION POINTS YOU HAVE WILL BE DIFFERENT FROM THE EXAMPLE ABOVE. EVERYONE WILL HAVE SLIGHTLY DIFFERENT POINT VALUES, AND THAT IS PERFECTLY OKAY. THE WEIGHTED AVERAGE SALARIES THOUGH MUST MATCH THE ONES IN THIS EXAMPLE. SO IF YOU DIDN’T QUITE GET THE ANSWERS RIGHT FOR TASK C, PLEASE GO AHEAD AND USE THESE WEIGHTED AVERAGE SALARY FIGURES.

Once you run your simple regression through the calculator link (http://www.graphpad.com/quickcalcs/linear1/ ), you will get results that will look something like this (yours will be different since everyone will have different job evaluation points that they created in Task B – again, that’s perfectly okay):

Best-fit values

Slope 360.33 ± 36.29

Y-intercept -24324.19 ± 6737

X-intercept 66.31

1/Slope 0.002798

95% Confidence Intervals

Slope 241.9 to 472.9

Y-intercept -45137 to -2262

X-intercept 9.211 to 96.90

Goodness of Fit

R square 0.9700

Sy.x 3683

Is slope significantly non-zero?

F 96.99

DFn,DFd 1,3

P Value 0.0022

Deviation from horizontal? Significant

Data

Number of XY pairs 5

Equation Y = 360.33*X – 24324.19

Note: I’ve gone through this material and it really does provide useful info that can basically hold your hand through this process. So I encourage you to take a look and follow along – I hope it you find this helpful! (I really think you will!)

Now, let’s focus on the next bit to get you started..! The first item that Task D asks for is: Identify the slope and y-intercept and write the equation for the market pay line.

Regression creates a “line of best fit” by merging the job evaluation points (X) and the external salary data (Y). The resulting regression line is used to predict the base pay (Y) for a specific number of job evaluation points (X). The equation for the simple regression line (as it is for any line) can be represented as: y=mx+b; in which:

y =the predicted base pay

m =the slope of the line

x =the job evaluation points

b =the y-intercept

So, for example, if the regression results show that m = 400 and b is -20000, then the equation is y=400(x) – 20000 and the predicted pay rate for a job assigned 100 points would be y= 400(100)-20000, or $20,000.

The regression output will also show information about how good the regression line fits the data. Specifically, look at the “R squared” in the regression output. Generally, the R squared, referred to as variance explained, should be .95 or higher. If R squared is significantly lower than this, there may be problems stemming from the job evaluation step. For example, the points assigned to certain benchmark jobs may be off – i.e., not make sense given the level of tasks, duties and responsibilities required for the job and the knowledge, skills and abilities needed by the job incumbent. If this is the case, re-examine the job descriptions and reconsider the points assigned to the benchmark jobs. Alternatively, there may be errors in the weighted average calculations. After conducting the regression again, examine the new R squared.

To calculate the slope of the market pay line, look in the Excel regression output for the “Coefficient of the X Variable.” The y-intercept is located in the regression output as the “Coefficient of the Intercept.” Be sure to write out the regression equation appropriately. Here’s an example:

Y = m(x)+b

Y = 360.33(x) -24324.19

Sample Solution

Text review of this exposition: This page of the exposition has 2111 words. Download the full form above. The United States is home to probably the most infamous and productive chronic executioners ever. Names, for example, Ted Bundy, Gary Ridgeway, and the Zodiac Killer have become easily recognized names because of the terrible idea of their wrongdoings. One of the most productive chronic executioners in American history is John Wayne Gacy. Nicknamed the Killer Clown due to his calling, Gacy assaulted and killed at any rate 33 adolescent young men and youngsters somewhere in the range of 1972 and 1978, which is one of the most elevated realized casualty checks. Gacy's story has become so notable that his wrongdoings have been included in mainstream society and TV shows, for example, American Horror Story: Hotel and Criminal Minds. Scientific science has, and keeps on playing, a significant function in the understanding of the case and recognizable proof of the people in question. John Wayne Gacy's set of experiences of sexual and psychological mistreatment was instrumental in arousing specialist's curiosity of him as a suspect. John Wayne Gacy was conceived on March 17, 1942, in Chicago, Illinois. Being the main child out of three youngsters, Gacy had a stressed relationship with his dad, who drank vigorously and was regularly oppressive towards the whole family (Sullivan and Maiken 48). In 1949, a contractual worker, who was a family companion, would pet Gacy during rides in his truck; in any case, Gacy never uncovered these experiences to his folks because of a paranoid fear of requital from his dad (Foreman 54). His dad's mental maltreatment proceeded into his young grown-up years, and Gacy moved to Las Vegas where he worked quickly in the rescue vehicle administration prior to turning into a funeral home specialist (Sullivan and Maiken 50). As a morgue chaperon, Gacy was intensely engaged with the preserving cycle and conceded that one night, he moved into the final resting place of a perished adolescent kid and stroked the body (Cahill and Ewing 46). Stunned at himself, Gacy re-visitations of Chicago to live with his family and graduates from Northwestern Business College in 1963, and acknowledges an administration student position with Nunn-Bush Shoe Company. In 1964, Gacy is moved to Springfield and meets his future spouse, Marlynn Myers. In Springfield, Gacy has his subsequent gay experience when a colleague unsteadily performed oral sex on him (London 11:7). Gacy moves to Waterloo, Iowa, and starts a family with Myers. In any case, after routinely undermining his significant other with whores, Gacy submits his originally known rape in 1967 upon Donald Vorhees. In the coming months, Gacy explicitly mishandles a few different young people and is captured and accused of oral homosexuality (Sullivan and Maiken 60). On December 3, 1968, Gacy is indicted and condemned to ten years at the Anamosa State Penitentiary. Gacy turns into a model detainee at Anamosa and is conceded parole in June of 1970, an only a short time after his condemning. He had to migrate to Chicago and live with his mom and notice a 10:00PM check in time. Not exactly a year later, Gacy is accused again of explicitly attacking a high school kid however the young didn't show up in court, so the charges were dropped. Gacy was known by numerous individuals in his locale to be a devoted volunteer and being dynamic in network governmental issues. His part as "Pogo the Clown" the comedian started in 1975 when Gacy joined a neighborhood "Happy Joker" jokester club that routinely performed at raising support occasions. On January 3, 1972, Gacy submits his first homicide of Timothy McCoy, a 16-year old kid going from Michigan to Omaha. Asserting that McCoy went into his room employing a kitchen blade, Gacy gets into an actual squabble with McCoy prior to cutting him over and over in the chest. In the wake of understanding that McCoy had absentmindedly strolled into the stay with the blade while attempting to get ready breakfast, Gacy covers the body in his creep space. Gacy conceded in the meetings following his capture that slaughtering McCoy gave him a "mind-desensitizing climax", expressing that this homicide was the point at which he "understood passing was a definitive rush" (Cahill and Ewing 349). Right around 2 years after the fact, Gacy submits his second homicide of a unidentified young person. Gacy choked the kid prior to stuffing the body in his storeroom prior to covering him (Cahill 349). In 1975, Gacy's business was developing rapidly and his hunger for youngsters developed with it. Gacy regularly baited youngsters under his work to his home, persuading them to place themselves in cuffs, and assaulting and tormenting them prior to choking them (Cahill 169-170). The vast majority of Gacy's homicides occurred somewhere in the range of 1976 and 1978, the first of this time occurring in April 1976. Huge numbers of the adolescents that were killed during this time were covered in a creep space under Gacy's home. For the rest of the killings, Gacy confessed to losing five bodies the I-55 scaffold into the Des Plaines River; notwithstanding, just four of the bodies were ever recuperated (Linedecker 152). In December 1978, Gacy meets Robert Jerome Piest, a 15-year old kid working at a drug store and extends to him an employment opportunity at Gacy's firm. Piest advises his mom regarding this and neglects to restore that night. The Piest family documents a missing individual's report and the drug specialist illuminates police that Gacy would doubtlessly be the man that Jerome addressed about a work. When addressed by the police, Gacy denied any association in Piest's vanishing. Nonetheless, the police were not persuaded, and Gacy's set of experiences of sexual maltreatment and battery incited the police to look through his home. Among the things found at Gacy's home were a 1975 secondary school class ring with the initials J.A.S., different driver's licenses, cuffs, garments that was excessively little for Gacy, and a receipt for the drug store that Piest had worked at. Throughout the span of the following not many days, examiners got numerous calls and tips about Gacy's rapes and the baffling vanishings of Gacy's representatives. The class ring was at last followed back to John A. Szyc, one of Gacy's casualties in 1977. Futhermore, after inspecting Gacy's vehicle, specialists found a little group of filaments looking like human hair, which were shipped off the labs for additional examination. That very night, search canines were utilized to recognize any hint of Piest in Gacy's vehicle, and one of the canines demonstrated that Piest had, indeed, been available in the vehicle. On December 20, 1977, under the pressure of steady police observation and examination, Gacy admits to more than 30 homicides and advises his legal counselor and companion where the bodies were covered, both in the creep space and the waterway. 26 casualties were found in the slither space and 4 in the waterway. Gacy is captured, indicted for 33 killings, and condemned to death by deadly infusion. He endeavored a craziness supplication however was denied, and was executed on May 10, 1994. There were a few scientific markers that examiners used to attach Gacy to the homicides. A portion of these include fiber examination, dental and radiology records, utilizing the disintegration cycle of the human body, and facial recreation in recognizing the people in question. Agents discovered strands that looked like human hair in both Gacy's vehicle and close to the slither space where the bodies were covered. Notwithstanding these hair tests, agents likewise discovered filaments that contained hints of Gacy's blood and semen in a similar region. Blood having a place with the casualties was found on a portion of the strands, which would later legitimately attach Gacy to the violations. The filaments in Gacy's vehicle were investigated by measurable researchers and coordinated Piest's hair tests. Moreover, the pursuit canines that discovered that Piest had been in Gacy's vehicle demonstrated this by a "demise response", which told agents that Piest's dead body had been within Gacy's vehicle. Out of Gacy's 33 known casualties, just 25 were ever convincingly distinguished. A large number of Gacy's casualties had comparative actual depictions and were along these lines hard to recognize by absolutely asking people in general. To recognize the people in question, specialists went to Betty Pat Gatliff, a pioneer in scientific science and facial remaking. Facial remaking is the way toward reproducing the facial highlights of a person by utilizing their remaining parts. Certain facial highlights, for example, facial structures, nasal structure, and by and large face shape can be valuable in distinguishing a casualty even long in the afterlife. By utilizing these highlights, and with the assistance of program, scientific examiners can make a picture of an individual's face, which is instrumental in distinguishing casualties after their bodies have rotted. Facial reproduction should be possible in a few measurements. Two-dimensional facial reproductions is utilized with skull radiographs and depend on pre-passing photos and data. Nonetheless, this isn't really ideal in light of the fact that cranial highlights are not generally noticeable or at the correct scale (Downing). To get a reasonable and more precise portrayal of the casualty's face, a craftsman and a measurable anthropologist are generally important (Downing). Three-dimensional facial remaking is finished by models or high goal, three-dimensional pictures. PC programs can make facial reproductions by controlling filtered photos of the remaining parts and use approximations to reproduce facial highlights. These will in general create results that don't look fake (Reichs and Craig 491). Once in a while, examiners will utilize a strategy called superimposition as a method for facial remaking. Shockingly, it's anything but an ordinarily utilized strategy, as it expects examiners to have some information about the personality of the remaining parts they are managing. By superimposing a photo of a person over the skeletal remaining parts, agents can check whether the facial highlights line up with the anatomical highlights, permitting them to recognize a casualty. On account of John Wayne Gacy's casualties, specialists had the option to utilize facial reproduction to recognize nine of the bodies found in the slither space. The accompanying realistic shows the facial recon>

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