>

CMQ for Compensation

> Developing a pay grade system that offers the desired balance of internal- versus external equity is challenging. Fortunately, a strong relationship exists between the pay jobs receive in the market and the job's abstract work dimensions (termed compensable factors in job evaluation).

Using policy capturing, statistical models can be derived to predict the expected compensation for a job (termed points) from the job's compensable factor scores.

CMQ Advantages for Compensation

  • Objective measurement of compensable factors: A major weakness of many systems concerns the vague definitions of the factors (e.g., "responsibility," "complexity," "know how"), and the subjective process used to rate them. CMQ provides a direct "paper trail" between dimension scores and the verifiable item ratings from which they were computed.

  • National Prediction Equations: We offer regularly updated policy-capturing equations that predict national wage averages using the CMQ work dimensions. Organizations that lack the numbers of jobs required to derive their own policy-capturing equations can use these standard equations.

  • Locally Derived Equations: Some organizations prefer to develop their own policy-capturing equations using key jobs of interest to them, using their own market-wage data. If you have a large enough number of jobs, customized local policy-capturing equations can be derived.

For a more detailed description of how CMQ can be used to assess compensable factors and develop compensation systems using policy-capturing, see the following recent chapter:

Harvey, R. J. (2012). Chapter 27: Compensation. In Wilson, Bennet, Gibson, & Alliger (Eds.), The handbook of work analysis: The methods, systems, applications, and science of work measurement in organizations. Psychology Press/Routledge, Taylor and Francis Group (ISBN-10: 1848728700).

Learn more about what we do, review our Frequently Asked Questions (FAQ), or sign up and get started now!