Missing data imputation in PLS-SEM

Huiwen Wang, Shan Lu () and Yide Liu
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Huiwen Wang: Beihang University
Shan Lu: Central University of Finance and Economics
Yide Liu: Macau University of Science and Technology

Quality & Quantity: International Journal of Methodology, 2022, vol. 56, issue 6, No 40, 4777-4795

Abstract: Abstract As a useful tool for business research, PLS-SEM is widely adopted for the assessment of causal-predictive relationships of models when developing and testing theories. Nevertheless, the less error-prone techniques for handling missing data are routinely ignored by PLS-SEM researchers. In this paper, we propose an imputation method, called EM PLS-SEM, to deal with missing values in PLS-SEM. The method takes advantages of the estimation procedure of PLS-SEM to reach the goal of filling the missing elements with values that are most likely to appear. Numerical studies verify that the proposed method outperforms other alternatives in data completion and model fitting.

Keywords: Missing values; Imputation; PLS-SEM; EM PCA (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11135-022-01338-4

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