Rely on Valid Compensation Survey Data

Methodology, salary dates, and study sample allow boards to judge reliable research.

December 09, 2010
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Your board’s compensation decisions will continue to be a point of emphasis in today’s regulatory environment. Consider a policy that details your compensation philosophy and the quality research methods you rely on.

So how can you be sure the information that crosses the boardroom table is valid, regardless of who brought it to your attention—a director, your CEO, or the compensation committee chairman?

Directors NewsletterValid salary studies include:

  • The methodology of the study;
  • The effective date of the salary information; and
  • Information on the study sample.

Here’s a closer look, according to Beth Soltis, CUNA's senior research analyst in the Credit Union Directors Newsletter:

• Methodology. This tells you what methods were used to gather and analyze the salary data in the study. It also will tell you how many surveys were sent out and how many were completed, Soltis says. It clarifies who was asked to provide the salary information. “It’s preferable that managers or human resource staff provide salary information because they’re knowledgeable about compensation policies and practices as well as compensation levels in the organization,” she says.

Methodology also confirms how researchers handled the data, Soltis explains. For instance, outliers—salaries that are at the extreme ends of the spectrum—are often removed from the final data reported in salary studies.

Weighting is a standard survey analysis procedure, designed to increase the reliability of the survey results. It’s a process of adjusting data for the over- or under-represented groups to remove bias by specific groups, she says.

Weighting is often done by credit union asset size. For example, if there’s a higher percentage of credit unions in a particular asset size than there is in reality, the data can be skewed by this group. Since larger credit unions tend to pay higher salaries than smaller credit unions, an over-representation of large credit unions can distort the data, resulting in higher salaries.

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