Single missing data imputation in PLS-based structural equation modeling

An important source of bias in structural equation modeling (SEM) employing the partial least squares method (PLS) is missing data. Deletion methods, such as listwise and pairwise deletion, have traditionally been used to deal with missing data. These methods are perceived as leading to selective loss of data and significant related biases. Missing data imputation methods, on the other hand, do not resort to deletion.  Our study suggests that single missing data imputation methods perform better with PLS-SEM than expected based on past research on their performance with other multivariate analysis techniques such as multiple regression and covariance-based SEM:

Kock, N. (2018). Single missing data imputation in PLS-based structural equation modeling. Journal of Modern Applied Statistical Methods, 17(1), 1-23.

http://cits.tamiu.edu/kock/pubs/journals/2018/Kock_2018_JMASM_MissDataImputationPLS.pdf