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Missing data imputation in PLS-SEM

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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.

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Enquiries about data availability should be directed to the authors.

Notes

  1. The KNN imputation and regression imputation are implicated by R packages “DMwR” and “mice”, respectively.

  2. The generation is completed by mvrnorm function in R software.

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Funding

This research was financially supported by the National Natural Science Foundation of China (Grant Nos. 72021001 and 72001222). S. Lu is a member of Financial Sustainable Development Research Team in Central University of Finance and Economics. S. Lu also thanks the support from the Emerging Interdisciplinary Project of CUFE, Program for Innovation Research in Central University of Finance and Economics, the Disciplinary Funding of Central University of Finance and Economics. Y. Liu also thanks the support from iFRG Grant at Macau University of Science and Technology.

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Authors and Affiliations

  1. School of Economics and Management, Beihang University, Beijing, China

    Huiwen Wang

  2. Key Laboratory of Complex System Analysis, Management and Decision (Beihang University), Ministry of Education, Beijing, China

    Huiwen Wang

  3. School of Statistics and Mathematics, Central University of Finance and Economics, Beijing, China

    Shan Lu

  4. School of Business, Macau University of Science and Technology, Macau, China

    Yide Liu

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  1. Huiwen Wang
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  2. Shan Lu
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  3. Yide Liu
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Correspondence to Shan Lu.

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Wang, H., Lu, S. & Liu, Y. Missing data imputation in PLS-SEM. Qual Quant 56, 4777–4795 (2022). https://doi.org/10.1007/s11135-022-01338-4

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