PayScale Compensation Data Methodology

For you to trust our compensation data, you need to know where it comes from. That's why we make our collection and filtering methodology completely transparent.

We collect and report all our own salary data. Our compensation data comes from comprehensive salary surveys that span a wide range of industries and geographic locations. Information is voluntarily self-reported by employees.

Our data is clean and unbiased. We do not blend salary data from multiple data sources or salary surveys, which may lead to averaging errors. We do not artificially age our salary data. All data must be less than 365-days old (and is often less than 90-days old). We do not extrapolate results or apply general cost-of-living adjustments to our salary data in order to estimate local market rates.

Finally, we validate every salary data point with both technology and trained data experts. We only report salary data that has been validated by our survey system and compensation data team and falls within our age requirements.

Salary Data Collection

PayScale collects salary data from employees. Through our online salary survey, we administer a comprehensive salary survey to individuals that collects a wide range of pay and job profile information. In return for this information, individuals receive a fair market valuation report that compares them to other people just like them. There are no offers, payments or other incentives made to individuals to encourage them to fill out a personal profile.

We've found that by asking employees questions that they know the answers to, we can capture information that organizations don't track in their talent management systems. Questions like education level, unique skills and certifications, total years in field, as well as job responsibility. These questions uncover the factors that directly impact employee pay, enabling HR professionals like you to determine what employees should be paid based not only on their level, but on their individual skill sets.

Salary Data Integrity

PayScale utilizes a combination of techniques to ensure data integrity and validity. Robust statistical analysis, automated data validation rules, and annual studies with 3rd party surveys ensure that survey records included in your datasets are accurate, reliable, and free from biases. Every submitted profile has to pass through our validation rules and statistical analysis in order for it to be included in our database. Any data profiles that do not pass our validation rules and statistical analysis, or are deemed questionable, incomplete, or duplicate are not used in calculating compensation reports. In addition to these measures, PayScale includes a sampling of anonymous profiles used to build your compensation reports that you can review to determine comparability with your own circumstances.

Salary Data Analysis

Our salary survey-based compensation methodology maintains every individual profile in its entirety, preserving the details that make each employee unique.

Unlike some of our competitors, PayScale does not modify or blend profile data based on general inflation adjustments, or age data based on general wage increase trends. We also don't apply general cost-of-living differentials by region because how pay varies by location is very job and employer specific.

We use the most current data points available and we report the average age of data points reported. This way we avoid erroneous assumptions often made by other data providers commonly known as "averages of averages" and "surveys of surveys."

PayScale by contrast allows users to build their own job definition along with certain compensable factors that are unique to their job, the organization and the incumbent.

Organizational Factors:
Industry
Location
Type of organization (e.g. profit vs. non-profit)
Company size

Position or Employee Specific Factors include:
Relevant experience in Field/Career
Job Skills and Specialties necessary to perform the job(e.g. C++, Pediatrics)
Certifications
Education Level
And additional job specific criteria (e.g., number of employees supervised, number of beds in hospital, Legal Practice Area, etc.)


It is the combination of these factors about the organization, the job, and the incumbent that explain why jobs get paid what they do in the marketplace. PayScale's algorithms search our entire database to find the most current profiles that match your definition and then analyze the relative interaction of all these statistically significant pay influencers to determine the value of YOUR UNIQUE job or employee in the marketplace.

Compensation Data Reporting

Many salary data providers use only job titles when comparing compensation levels, which leads to broad, arbitrary data sets. And that just isn't sufficient research for making business-critical decisions about your company's compensation.

PayScale has established up to 100 comparable data points for each report. Because the matches are precise, they exceed traditional sample sizes in statistical precision for comparisons of this nature.

Additionally, PayScale does not calculate or report data if the number of data points is insufficient to provide a meaningful representation of the market