Assessing the prediction of employee productivity: a comparison of OLS vs. CART
by Ina S. Markham
International Journal of Productivity and Quality Management (IJPQM), Vol. 8, No. 3, 2011

Abstract: This research compares the results of utilising an ordinary least squares (OLS) approach vs. a classification and regression tree (CART) approach for identifying employees with a high likelihood of being productive. Relevant performance data were collected from 378 employees of a large garment manufacturer. Past research (Markham et al., 2006) has shown that a combined genetic algorithm with an artificial neural network substantially outperformed (R² = 0.30) an equivalent OLS solution (R² = 0.14) when predicting individual level productivity. The current research compares the use of CART to OLS using the same data set. With an R² of 0.43, the CART results were even more powerful in identifying and classifying high performance employees. The implications of this finding for the field of productivity research and employee selection are discussed.

Online publication date: Wed, 14-Sep-2011

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