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Compensation specialists use mostly basic statistical analysis methods when building and adjusting a competitive pay structure. Regression analysis is among the most widely applied analytical and evaluating models for salary structure management across all existing industries. Using this technique, labor economists model an organizations compensation system based on data regarding factors expected to influence pay and determine to what extent gender or other protected characteristics may influence employees compensation (Miller and Massey). The popularity of regression analysis lies in the fact that it allows one to consider many industrial, market, and societal factors.
The current regression analysis paradigm creates at least two critical issues for one designing a fair and relevant compensation structure for their business company. The first one is that this approach of data modeling does not provide one with enough efficient means to deal with missing data and values. When applying regression, one can fix the data only with the help of imputation, regression equation, and data removal. The effectiveness of these techniques is highly dependent on the quality and type of sources. In an era of data overabundance, the inability to fix data reduces its quality and analysis results significantly (Corrales et al. 1). Consequently, a companys compensation structure becomes less relevant and competitive.
The problem of the lack of analytical tools for fixing data creates one more big issue. It is a need for data cleaning in compensation management. In current times of rapid flow and updating of information, this procedure becomes time-consuming for labor economists. It leads to such severe physiological consequences as exhaustion and burnout of compensation professionals and a decrease in their analytical abilities and attention.
Works Cited
Corrales, David Camilo, et al. How to Address the Data Quality Issues in Regression Models: A Guided Process for Data Cleaning. Symmetry, vol. 10, no. 4, 2018, pp. 1-20.
Miller, Barry J., and Hillary J. Massey. How Employers Can Use Regression Analyses In Their Favor In Pay Equity Cases. Seyfarth, 2019, Web.
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