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Percentile Ranks

Solid business decisions require data. Are you comfortable providing and interpreting it? This article dives into what percentile ranks are and the role they play in helping HR make decisions.

Percentile ranks measure (or rank) an employee in comparison to other employees in a sample.This can be used in hiring or other HR practices.

Data-driven decisions help HR succeed in the short and long term. Percentile ranks are just one way HR uses data to make decisions. Here are a few reasons why percentile ranks are important to HR.

**Efficient.**Percentile ranks are a fast and effective way for HR to decide the best option. It shouldn’t be the end-all, but it is effective in ranking different options.**Comparisons.**Percentile ranks make it easier for HR to compare, for example, where a candidate is a better fit based on test scores, or how your compensation ranks in comparison to other companies. It is a simple way to say how an employee or candidate ranks in comparison to another employee or candidate for a given test or exercise.**Provides context.**Percentile ranks make it easier to interpret a score or a data point within a data set. Percentile ranks help conceptualize a data point you might get from a feedback survey, test, or compensation survey.

Percentile ranks and percentages often get confused. In reality, they are related but different. Percentages refer to how someone scores on something, while percentile refers to how they do *in comparison* to others who take the same test. Here are a few reasons why that differentiation is important and makes percentile ranking better at measuring than percentages.

Let's use pre-employment testing as an example of the insight percentile ranking can provide. It not only tells an employee how they do in comparison to other employees, but can show you how an employee is more proficient than others. The data is more reliant on how an employee does in comparison to others they are competing with as opposed to what the employee thinks they already know.

Percentile ranks provide context and insight, which means it is easier to understand as opposed to a percentage. For example, say a pre-employment test is conducted and a candidate gets a 95%. The candidate would feel confident about their score. However, say instead of a percentage they are given a percentile rank, and they get only an 80% percentile rank despite scoring a 95% on the test. The percentile rank makes it easier for the candidate (and you) to understand how they did in relation to the other candidates.

Having discussed the importance of percentile ranks and how they provide context and insights into data, here are the steps to take when calculating percentile ranks. (Excel can do this for you with the PERCENTRANK function.) The equation to calculate percentile ranks is (L/N) X 100 where L is the number of data points that are equal to or less than the number you are calculating the percentile rank for and N is the total number of data points.

The first step in figuring out how one score compares to others within the data points is to count how many data points there are. This is the number you will eventually divide by. Let's imagine that you give a test to 12 job applicants—this is the N part of the equation—and you want to percentile rank one of the applicants, whose score was 80.

The next step is to put each data point in order from highest to lowest and find where that specific data point falls with the data set and then count how many of the numbers are less than or equal to that number. The 12 scores from your test-takers are 95, 94, 88, 87, 85, 81, **80,** 79, 75, 72, 66, 44. To calculate the L part of the equation, you would count how many are equal to or less than 80. In this case it would be 6.

The final step is to take the number of where a data point falls within the data (how many data points it’s above or equal to) and divide it by the total number of data points. Multiply that number by 100, and that is your percentile rank. For the applicant who scored 80, the calculation would be 6/12 x 100 = 50 That person would rank in the 50th percentile.

Having explained percentile ranking, it is important to understand how it can apply in HR. Here are a few examples.

Depending on the job, some employers test candidates as part of the screening process. This test may measure their technical skills, experience, or how they might react in a certain work situation. Their score would then be compared to other candidates by using a percentile rank.

HR departments often perform compensation analysis to see how their wages compare amongst departments as well as to the labor market in general. While there are many ways to compare wages and many factors that go into calculating them, percentile ranks can be one way to see how your company's wages compare to similar roles at other companies.

While this is not as commonly used as other examples, the HR department might decide to use percentile ranks related to giving promotions or raises. Say HR decides to promote or give raises to employees within a division based on how they do in comparison to other employees (this is not a best business practice, but may still be used by some companies) based on performance reviews by managers. If the HR department decides to promote or give raises to employees in the 70th percentile or better, an employee's performance review score would have to be better than 70 percent of the other employees.

Here is a bit more context on how to calculate each of the examples above.

This calculation depends on what kind of test you are administering. You need to decide how you score the test. That could be done by weighted answers or simply the percentage of correct answers from the test. Once you have all the scores, you would calculate percentile ranks as above.The number one score is the 100th percentile. From there you calculate the percentile rank for each data point. This gives you an idea of your percentile rank spread and how each test-taker compares to the others.

Say you want to analyze how your company's wages compare to employees throughout the company or to other companies. The most important thing will be to decide which wages will be part of the data. You can decide if you want to compare all jobs, jobs with the same title, or jobs within the same department. If you want to compare your wages to other companies in the market, you could compare all jobs, jobs with the same title, or jobs with similar titles. Best practice is to perform a few different compensation analyses to make sure you get accurate data. If you decided to start comparing their wages to other companies with similar job titles—for example, all sales positions—you could then find wages of sales positions for companies in your area (or a similar area). You can do as many comparisons as you want, but the more data points, the more accurate the data will be. After collecting compensation data for each of these positions at each company, you would follow the calculation process provided above. The highest wage will be the 100th percentile. From there, determine the percentile ranks for each wage. Next, organize the ranks by job titles and see how each job title at your company compares to job titles at other companies. There will be higher-paying sales jobs, such as Account Executive, so it is important to compare within job titles or roles. You can also organize by company and see how a company in general pays its sales team.

This example probably has the most variance, as how a company decides to use percentile ranks for promotions will be different. Let's go back to our previous example (which, again, is not best practice) and say that your company only gives promotions or raises to employees in the 70th percentile rank or higher on their performance reviews. After managers complete their reviews, you would follow the calculation procedure and end up with a list of employees who are in the 70th percentile rank or higher. Those employees would be given a raise or promotion.

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Tanner Pierce, PHR

Tanner has over 4 years of HR professional experience in various fields of HR. He has experience in hiring, recruiting, employment law, unemployment, onboarding, outboarding, and training to name a few. Most of his experience comes from working in the Professional Employer and Staffing Industries. He has a passion for putting people in the best position to succeed and really tries to understand the different backgrounds people come from.

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