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We can now proceed to co-relate the internal and external data to create our market pay line. (2008) Compensation, McGrawHill Irwin). In other words, 80% of the values fit the model.
To understand your company pay competitiveness and maintain a competitive salary structure, you must participate in salary surveys or at least get the published salary survey reports. A regression will tell you if there is a significant relationship between a number of variables (e.g., analyzing if gender actually impacts pay when you control for other factors that should influence compensation).
(Grades, pay scale, Pay Ranges, Midpoint, Spread and Overlap) Implementation of salary structure.
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. It is an indicator on how well the x-variables can be used to predict the value of the y-variable. Step 7 Use the regression output (the slope and y-intercept) to calculate the predicted market pay rate (using Excel) for each benchmark job.
This analysis can take into account multiple factors that influence salaries (e.g.
Regression Analysis Used in Salary Structure Management Published on July 5, 2016 July 5, 2016 • 33 Likes • 3 Comments. Analyst Ben Mgr. This shows how good the regression line fits the data.
Step 4 We now have internal job point data and externally gathered weighted average base pay for each benchmark job. y=(250*120) –1200 = 28800. It should be .95 or higher. So I would recommend using exponential regression). Simply said,it tells you how many points fall on the regression line, for example, 80% means that 80% of the variation of y-values around the mean is explained by the x-values.
Step 1: Create a scatter chart for all your jobs to illustrate the market rate composite. For simple linear regression, R2 is the same as the correlation, R, squared. Now the regression result can be used to calculate the market pay level for each internal grade.
When there is only one variable (tenure or job grade), the regression is called simple linear regression and is usually represented by a line in the middle of the data points. Step 2 We created an internal hierarchy of jobs (i.e., an internal job structure) by using the point method to evaluate each of our benchmark jobs. Step 1 We used job analysis to create job descriptions for our benchmark jobs.
Any follow-up issue in 3 days will also be answered with no charge. Asst. For example, with a slope of 250 and y-intercept of -1200, the 120 points assigned to the Front Desk Receptionist job would translate into a predicted base pay rate of $28,800. If R squared is significantly lower than this, there may be problems stemming from the job evaluation step.
Enter the job evaluation points (on the X axis) and weighted average base pay rates (on the Y axis) for each benchmark job and generate the regression results. The R value is a measure of correlation between the predicted and observed values of the independent variable. Report this post; Linda Zhang Follow The turnaround is within 24 hours. Alternatively, there may be errors in the weighted average calculations.
], indicating that X (i.e., your job evaluation points) explains about 96% of the variance in Y (i.e., the market pay). and determine if there is a significant correlation between race or gender and compensation. This measures what proportion of the variation in the outcome Y can be explained by the covariate/predictor X.
7)Slot the remaining non-benchmark jobs into the structure.
One thing to remember is that these new pay rates will become the midpoints of the established pay grades. The Salary Regression Analysis is a Multiple Linear Regression analysis method of testing for possible compensation disparity within an organization.
The source for the following article is “Designing a Pay Structure” written by Lisa A. Burke and published by Society for Human Resource Management. If R squared is close to 1, it means that the covariates can jointly explain the variation in the outcome Y. Group data by desired salary grades and internal job titles.
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