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Understanding the relationship between statistics and compensation

Isn’t pay essentially about statistics, since pay programs are designed and administered around math, which is central to pay, right from the structure to the analysis of data?

Statistics has a vital role to play in compensation. Statistics professionals and practitioners have to possess the nous needed for devising and analyzing concepts relating to pay, such as pay ranges, pay survey data, individual and group pay rates, and other elements of a compensation program.

A learning session that helps understand the relationship between statistics and compensation

David J. Wudyka, SPHR, Managing Principal of Westminster Associates in Wrentham, MA, who is an independent Human Resource Consultant with over 30 years’ experience in the profession, and is one of the first people in the United States certified in the Compensation field by the World At Work; will explain the role of statistics in compensation at a webinar that is being organized by TrainHR, a leading provider of professional trainings for the human resources industry.

Want to understand the nuances of the relationship between statistics and compensation? Then, all that you need to do is log on to TrainHR to enroll for this webinar.

Viewing this webinar, its entirety qualifies for a recertification credit hour that may be counted toward SHRM-CP and SHRM-SCP recertification from SHRM.
Credit is awarded based on the actual educational time spent in the program.

This webinar has been approved for 1.5 HR (General) recertification credit hours toward aPHR, PHR, PHRca, SPHR, GPHR, PHRi and SPHRi recertification through HR Certification Institute (HRCI).

Pay structure unraveled

At this session, the speaker will simplify and demystify the assumed complexities of the pay structure. In unraveling the mysteries of statistics in compensation, David will help participants understand the various elements of the statistics behind the pay structure. He will ask and answer many questions relating to statistics in pay structure. Some of these include:

  • How to calculate minimums and maximums of a pay range, knowing just the new midpoints?
  • What is a compa-ratio, and how is it used?
  • How does one calculate a percentile, and how are percentiles used?
  • Why are weighted averages used by the majority of Compensation practitioners, and not the median?
  • What is the difference between simple regression analysis and multiple regression analysis, and how do practitioners use these concepts in a practical way?
  • What is correlation analysis and how does this analysis help in pay program administration?
  • When you are creating new pay structures, how can you estimate the number of grade levels between new midpoints using selected midpoint to midpoint spread percentages?
  • Why do practitioners divide pay ranges into quartiles?

Understanding of all the measures that go into pay programs

Knowledge of statistical concepts such as these is a great means improve one’s ability to create and analyze one’s compensation program. At this webinar, David will help identify the measures that Compensation professionals use when they design and administer pay programs, from the creation of pay structures through the analysis of pay survey data. This session will offer clear understanding the underlying statistics of the elements of compensation programs.

It will cover the following areas during this session:

  • How to create and analyze pay range widths ("spreads")
  • Why "weighted averages" are more widely used than "medians"
  • Defining "means", "medians" and "modes" (and how to use them)
  • How to understand the statistics of pay structure design
  • Understanding the difference between regression and correlation analysis, and how to apply them effectively in pay program analysis
  • How "percentiles" differ from "percents", and how to calculate them
  • How to calculate rate range minimums and maximums from estimated midpoints
  • Calculating the "penetration" of pay rates in pay ranges
  • The Compa-ratio: what it is, how to calculate it and how to used it
  • Why pay ranges are divided into "quartiles"
  • The difference between "linear regression" and "multiple regression"
  • How to estimate the number of grade levels between two new pay structure midpoints
  • What are "measures of central tendency" and why are they important for analysis?

 

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