PayScale administers the largest real-time salary survey in the world with more than 150,000 new survey
records added every month. The database of more than 54 million total salary profiles is updated nightly
to reflect the most detailed, up-to-date compensation information available. Our data collection is strongly
correlated with the size of the pool being considered, representing the diversity of the general workforce.
People complete a salary profile on PayScale’s website for many reasons, but mostly to prepare to ask for
a raise, evaluate a job offer, or just to know how they stack up against others in similar positions. Upon
completing PayScale’s salary survey, individuals receive a series of reports that show how their salary
compares to other people with similar education, skills and work experience. Individuals can also explore
how changes such as moving to a different city, getting a promotion and going back to school can affect
their future earning potential.
Data Standardization & Matching
Accurate compensation reporting is highly dependent on the ability to normalize and classify titles,
industries, locations and other compensable factors into consistent groups. Knowing that “C++ Developer”
is a kind of “Software Engineer” requires a deep understanding of the semantics of these terms as well as
the core tasks performed by employees with these titles. PayScale leverages proprietary internal taxonomies
as well as proprietary mappings to third party data sources to assure accurate mapping. The breadth and
depth of the data assets used to standardize and match data is unparalleled in the industry.
PayScale applies a set of propriety algorithms to assure the consistency and accuracy of every data point
used in our compensation models and reports. Our data team regularly compares PayScale compensation data
with external sources of data, both publically and privately available. This research has shown that our
market data is strongly correlated with other sources of compensation data, including employer submitted
data. This research has also shown the breadth and depth of our data is wider than other sources due to
our collection methods and software product, where users are able to more precisely describe and price
positions, including both the type and size of the organization, and the skills and experience of the position.
Our software does not need to modify or blend profile data, use inflation or cost-of-living adjustments,
or age data. This way, we help our customers avoid the shortcomings of traditional salary surveys that dilute
the market data using “averages of averages” or “surveys of surveys” approaches.
The MarketMatch algorithm uses a two-step process for producing compensation data in a PayScale report.
The first step is to understand which of our more than 250 compensable factors are important when it
comes to pricing a job and how that job’s pay is affected by these compensable factors. This is done
in order to define a pay distribution for this job. The mix of compensable factors and their effect on
pay is highly dependent upon the job. For example, coding languages and locations are important compensable
factors for a Software Developer, while average sales prices and annual sales are important for an Account
Executive. The second step is to then find the recent profiles that best match the described position in
order to tighten the overall distribution from representing the job overall to the specific position
described in the PayScale report.
Data Analysis and Reporting
Businesses using PayScale’s subscription software have access to extensive analytics about their workforce.
They can see exactly how the salaries for their workforce match to market pay ranges, adjust salaries of
overpaid or underpaid employees, identify flight risks, determine raises, and prepare both employee total
compensation statements and executive reports.