Structural equation modeling (SEM) is a widely used technique for studies involving latent constructs. While covariance-based SEM (CB-SEM) permits estimating the regression relationship among latent constructs, the parameters governing this relationship do not apply to that among the scored values of the constructs, which are needed for prediction, classification and/or diagnosis of individuals/participants. In contrast, the partial-least squares approach to SEM (PLS-SEM) first obtains weighted composites for each case and then estimates the structural relationship among the composites. Consequently, PLS-SEM is a preferred method in predicting and/or classifying individuals. Nevertheless, properties of PLS-SEM still depend on how the composites are formulated. Herman Wold proposed to use mode A to compute the scores for constructs with reflective indicators. However, Yuan and Deng recently showed that composites under mode B enjoy better psychometric properties. The authors thus proposed a structured transformation from mode A to mode B, denoted as mode BA. This chapter further studies properties of the three modes of PLS SEM. Analytical and numerical results show that 1) Mode A does not possess any solid statistical or psychometric properties, 2) Mode B possesses good theoretical properties but is over sensitive to sampling errors, and 3) Mode BA possesses good theoretical properties as well as numerical stability. The performances of the three modes are also illustrated with two real data examples. [This chapter was published in: "Partial least squares path modeling: Basic concepts, methodological issues, and applications, 2nd edition," edited by H. Latan et al., Springer.]