WageWatch Ibrief Blog


Archive for August, 2014


Data in its raw form, a string or table of numbers, is of little to no value in compensation. Data has to be summarized, described, and presented with particular analysis so that a decision can be made. The key to understanding compensation market data is to report data in a way that is easy to understand.

In the WageWatch Market Competitive Survey, subscribers select their competitors which create a custom set of data from peer organizations. The PeerMark Report™ contains useful statistics to measure the location of data with the set. The report shows where the central part of the data is in the form average and percentiles.

In prior blogs, we have presented central tendency in part as a discussion of average (simple mean) and median. Since then we have also introduced weighted average to the report which is another measure of central tendency. These points are provided on the report because all are needed to answer the question, ‘Which statistic to use for benchmarking?’ The answer is ‘It depends’.

The weighted average is used when the number of incumbents in the competitive set needs to be taken into account such as when comparing data sets that contain both very small and very large companies.  In an extreme example, if we had a city with only two hotel companies one with 10 housekeepers and the other with 1,000 housekeepers – then the one with 1,000 housekeepers dominates the marketplace and would determine the prevailing rate for this job. Here, weighted average would best represent the market rate.

The simple mean, also called average, is an unweighted calculation. Weighing competitors equally is a hedge against the unknown and is the recommended calculation when there are other competitors in the marketplace that have not participated in the survey. We do not know if the missing companies are big or small, pay low or high. This is why the simple mean is considered representative of market values.

There is an advanced metric called the Combination Mean which is the average of the simple mean and the weighted average. This gives equal importance to the number of incumbents and number of companies in the set.

We introduced percentiles in Part 1 of this blog topic. The percentiles and averages together describe what the shape of the data distribution of the competitive set “looks” like. The bell shaped distribution with a peak in the middle and tails on both ends, is called a normal distribution. While a true normal distribution is purely theoretical, there are many examples in the data are approximately normal.

The closer the market median and the market average wages are to each other, the more normal the distribution. In most reports, the median and averages rates will be within a dollar of each other. If the average is much greater than the median, the hump of the curve is skewed, leaning to the right creating a long tail on the left. This indicates a “hot job”, where the trend is to pay above market median. If the average is less than the median, the skew is to the left creating a long tail to the right indicating the job is cooling down.

WageWatch reports provide data tools and report statistics for analysts of all experience levels. Please contact WageWatch if you need assistance with interpretation the statistics reported, help building custom reports, or have a need for our wide range of consulting services. For more information on our services and surveys please call WageWatch at 888-330-9243 or contact us online .

Posted in Uncategorized on August 27th, 2014 · Comments Off on STATISTICS FOR COMPENSATION: PART 2


The purpose of statistics in compensation is to provide mathematical tools to objectively identify and describe how much jobs are worth and how to pay employees in the context of organizational goals. Compensation professionals are tasked with making a business case for a recommendation to change pay or pay program by analyzing the underlying assumptions, pulling data from the right source, and preparing concise conclusions for the management team.

WageWatch reports its data in several ways. The Benchmark report is a fast and easy way to report on national, regional, state, and city market cuts of data. The PeerMark report is our advanced tool that allows the survey subscriber to build custom competitive sets at a granular or niche market level.

When analyzing data from either report, there are many analytic tools to choose from and it can be overwhelming for those preparing a compensation project for the first time. A straightforward and highly effective statistic to use to describe a situation and develop a solution is to use percent (%). In basic terms, a percent is a measure of relative value.

The percentage forms the basis of the WageWatch reports. Percent is used in many places in the survey in several different applications.

  1. Percent Difference – The most common application of percent is its use in describing the difference between two numbers. The percent difference can be used to compare how wages change year over year as well as the difference between external to internal rates. There are two ways to describe differences using percent.

For example, your average rate of pay for a select service hotel Front Desk Manager is $45,000 per year and the market average rate is $55,000. It is easy to calculate that you pay $10,000 less per year on average for this job. It can be expressed as a percent two ways.

  1. You pay 18.2% less than the market average

i.     ($45,000-$55,000)/ $55,000 * 100 = -18.2 or 18.2% less

  1. The market average is 22.2% more than your rate.

i.      ($55,000-$45,000) / $45,000 *100 = 22.2%

Both calculations are correct, so which to use? You could use both. “Our Front Desk Manager is 18.2% below market average. We need to raise our rate by 22.2% percent to match market average.” You can avoid having to explain the figures by restating this conclusion as “Our Front Desk Manager is 18.2% below market average. We need to raise our rate by $10,000 to match market average”.

  1. Percent Position – The percent position is used to state your overall market position relative to the competition in the data set. WageWatch allows subscribers to build custom reports by including a target percentile statistic that ranges from 11% to 89%. Percentile is a position that a given percentage of the data is less than or equal too. For example, if your target percentile is 60th, than that is a market position where 60% of the competitors are less than or equal to and 40% are greater than or equal to.
  2. Percent as Ratio – A percent is a ratio multiplied by 100. We can see this when looking at dashboard metrics such as Turnover Rates. If a company fills one front desk agent position out of ten for the year, then the turnover rate can be expressed as 1:10 or 10%.

For these reasons, it is critical that compensation and HR professional understand how to use percent to communicate the story behind the data in a simple way. Please contact WageWatch if you need assistance with interpretation the statistics reported, help building custom reports, or have a need for our wide range of consulting services. For more information on our services and surveys please call WageWatch at 888-330-9243 or contact us online .

Posted in Uncategorized on August 19th, 2014 · Comments Off on STATISTICS FOR COMPENSATION: PART 1


Median and mean are useful measures of distribution that identify central values and tendencies of a data set. In a WageWatch compensation survey, median is the middle wage in a set of ranked market wages, which separates the data set in half. When an even number of wages are ranked, with no true middle value, the average of the two middle data points is the median wage. Median is also called the 50th percentile.

The arithmetic mean which is also called the simple average or mean is one of the most commonly used statistics in business. One of the reasons is that it is very easy to calculate. It is the sum of all values of a data set divided by the number of values in that set.

In a normal distribution of data, the median and mean wages will be within a few cents of each other. When the mean is greater than the median, this indicates the data set is skewed towards higher wages. Similarly, when the mean is less than the median, this indicates the data set is skewed towards lower wages. Skewing typically occurs for one of two reasons: either the data set is not normally distributed, or the mean is affected by outliers or errors in the data set. The first situation is not uncommon and occurs when there is either wage compression or the data is bimodal, meaning that the job description may be too broad and need to be bifurcated into two job titles. The second reason for the mean and the median being significantly different, due to an outlier or data error, is much more of a concern.

Here is an example of how data affects the median and mean differently when an outlier is added to the data set.

Wages in Data Set A                                                        Wages in Data Set B
$9.25                                                                                      $9.25
$10.11                                                                                    $10.11
$10.56                                                                                   $10.56
$11.98                                                                                    $11.98
$12.50                                                                                   $12.05
$12.88                                                                                    $12.31

Set A Median Wage $11.27                                          Set B Median Wage $11.98
Set A Mean Wage $11.21                                              Set B Mean Wage $27.32

In this example, it is clear that once the $125.00 value is added as an outlier, the median wage becomes the better indication of the central value. Comparing the median with the mean from Set B tells us that while the middle wage or median is $11.98, there appear to be employers paying at the top of the market, skewing the mean up sharply. The only way to know for certain if the difference between the median and mean is due to an error is a more thorough analysis of the data. If more data analysis is not possible, then the median is your better statistic to use.

WageWatch, Inc. is the leading compensation survey provider for over 5,000 hotels, non-profits organizations, healthcare organizations and management companies. The PeerMark™ Wage Survey is the only Web-based custom survey tool that allows individual survey participants to select their competitive set for comparison purposes.   

At WageWatch, our expert evaluators provide businesses in a large range of industries with accurate and beneficial benefits survey data, compensation surveys and salary reports to ensure that payment and benefits plans are on par with those in the industry. For more information on market compensation data, please call WageWatch at 888-330-9243 or contact us online (https://www.wagewatch.com/Contact/ContactUs.aspx).

Posted in Uncategorized on August 13th, 2014 · Comments Off on MARKET ANALYSIS: MEDIAN OR MEAN


With traditional annual salary surveys, the process of data collection starts when the survey opens. The opening is followed by a window of time that is typically two to four months, and sometimes as long as six months. This survey window depends on the industry and number of positions surveyed, for which survey participants would report their payroll data for incumbents. At the end of the collection period, the survey closes and no additional survey participation can occur until the following year when the survey cycle is completed and the survey reopens. The date the survey closes to participation is referred to as the survey’s effective date.

Once the survey closes, the wage data is manually cleaned, analyzed, and the findings formatted into a compensation benchmark report. Building the report in this manner can take an additional two to three months and for some compensation surveys up to six months. Once the report is complete, it is made available to participants and is on sale until next year’s compensation report is published.

The traditional annual compensation survey, by design, reports last year’s data. Compensation professionals know the value of using the most update-to-date market data available to conduct their benchmarking and wage analysis.  WageWatch has responded to this need with salary surveys and benefit surveys that collect and report the most current data – never last year’s data. HR directors and compensation managers know the effective date for each participant. This approach creates a survey platform that is dynamic, never closes, and reports the most current market data available.

WageWatch uses the next generation methodology based on a 365-day subscription period that allows participants to continually update data and report findings during the year. WageWatch defines the effective date as the date on which wages are internally updated in an organization’s payroll system. WageWatch’s survey platform is dynamic and not static as are traditional annual salary surveys. While Wagewatch does have a close date for its compensation surveys, which would normally be referred to as the effective date in a traditional survey, this is a soft close date. 

Because our surveys are dynamic compensation surveys, we continue to accept participants’ wage data after the close date of the survey.  Users can subscribe after the soft close date, enter their data and create their own custom reports all in the same day.  WageWatch reports allow you to select an entire market to compare your in house salary data, or you can select as few as five competitors you select to compare your salary data. This report is known as the WageWatch PeerMark™ Survey report.

At WageWatch our compensation consultants can assist with your organization’s compensation needs and help you ensure that your compensation programs are supporting your company’s business strategy and objectives.  WageWatch also offers accurate, up-to-date benefit survey data, market compensation data and salary reports that will allow you to stay current with the times. This information is highly beneficial in creating the best salary and benefits packages that meet or rival the industry standards. For more information on our services, including consulting, salary survey data, benefit survey data and market compensation reports, please call WageWatch at 888-330-9243 or contact us online .

Posted in Uncategorized on August 6th, 2014 · Comments Off on NOT ALL SALARY SURVEYS ARE CREATED EQUAL