Bias and efficiency loss in regression estimates due to duplicated observations: a Monte Carlo simulation

Francesco Sarracino, Malgorzata Mikucka


Recent studies documented that survey data contain duplicate records. We assess how duplicate records affect regression estimates, and we evaluate the effectiveness of solutions to deal with duplicate records. Results show that the chances of obtaining unbiased estimates when data contain 40 doublets (about 5% of the sample) range between 3.5% and 11.5% depending on the distribution of duplicates. If 7 quintuplets are present in the data (2% of the sample), then the probability of obtaining biased estimates ranges between 11% and 20%. Weighting the duplicate records by the inverse of their multiplicity, or dropping superfluous duplicates outperform other solutions in all considered scenarios. Our results illustrate the risk of using data in presence of duplicate records and call for further research on strategies to analyze affected data.


duplicated observations; estimation bias; Monte Carlo simulation; inference

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Copyright (c) 2017 Francesco Sarracino, Malgorzata Mikucka

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