Assessing compensation and organizational variation with imperfect data: An application of count-based indices of variation
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Abstract
An accurate assessment of human capital or labor force variation in organizations is predicated upon collecting error-free data. When organizations report imprecise human capital or labor force data, problematic data analytic issues arise because the application of frequency-based indices of variation obtain questionable measures of variation. This article addresses the assessment of vertical pay variation in organizations with count-based indices of heterogeneity. In doing so, this article demonstrates how imprecise categorical pay dispersion data negatively impacts the ability of logarithm-, mode-, and probability-based indices of variation to obtain accurate and reliable measures of pay variation. More importantly, this article demonstrates how count-based indices of variation overcome data analytic issues presented by imprecise data reported by organizations. In demonstrating their appropriateness to assess variation in organizations, the article assesses the measurement validity and reliability of unstandardized and generalized scores of pay dispersion obtained with count-based indices. By applying count-based indices to imprecise pay dispersion data reported by New York City municipal departments, this article addresses an important data analytic issue and shows that count-based indices are a viable alternative method for assessing variation in organizations when imprecise data limit the use of logarithm-, mode-, or probability-based indices. As an alternative method to frequency-based indices of variation, count-based indices provide additional data analytic techniques for assessing how pay and other forms of variation directly or indirectly affect organizational stability and performance.
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