Our Data Works

We are able to provide fresh data due to collecting
data from people throughout the world who complete our salary survey.


PayScale methodology made simple


Historically, decisions about salary involved heavy binders full of data and some serious guesswork. Today, compensation analysis should be a science, not an art. As PayScale unites fresh detailed data and clever statistical modeling in an easy to use software service, we’re bringing modern science to the process of compensation planning.

Gathering a compensation data set that is both broad and deep is only half the battle. What makes PayScale different from its competitors, who rely on old-school, less specific, employer-submitted data, is that we use modern data mining and predictive modeling techniques to yield the most highly accurate compensation predictions possible, across a huge range of circumstances.

Where Does Our Data Come From?

We crowdsource it from visitors who come to our site looking for real answers about what they should be getting paid. Whether they are prepping to ask for a raise, evaluating a job offer, or just want to know how they stack up against others, they have an intrinsic motivation to not only complete our online salary survey but also to give us honest answers.

We then run the data through verification filters to confirm its validity:

Weed Out the Outliers
We discard data that is too far outside of expectations.

Defend Against Attempts to “Stuff the Ballot Box”
We automatically detect and reject too much data coming from any one person.

Standardize the Data
You say “computer programmer,” I say “software developer.” We have the technology to realize we’re talking about the same job.

Augment the Data
PayScale knows a lot more than just what we receive from our surveys. For example, if you tell us you’re in Kuna, Idaho, we know that is in the Boise, Idaho metro area. Plus, we know that the last census placed 606,376 people in the Boise area, and how being in a city of that size affects compensation.

Once all that sifting and sorting is done, we still have more than 54 million profiles — and counting. That’s a huge pot of statistically relevant data.


PayScale finds the data that matters to you for pay, whether that’s showing you data that is near your location or that has the extra skill that is really driving compensation for the position you are pricing. PayScale’s MarketMatchâ„¢ algorithm is kind of like online dating: we look at over 250 compensable factors and the relationships between those factors when finding the ideal matches for positions. For example, we know that most employers pay more for employees that have more experience or who are located in large urban areas.

Even with as much data as we have (over 15,000 job titles), there can still be gaps and in those cases, MarketMatch makes sophisticated mathematical predictions to get the answers you need. Say you want to know what a Java programmer with 7 years of experience in Harlan, KY should be paid and we don’t have an exact match in our data. We can determine how software developers are paid, in general and then adjust that prediction to account for having seven years of experience, for being in a small town in the upper South, and for programming in Java rather than C++.

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