Covariance-based SEM (CB-SEM) has become one of the most prominent statistical analysis techniques in understanding latent phenomena such as students and teachers’ perceptions, attitudes, or intentions and their influence on learning or teaching outcomes. This chapter introduces an alternative technique for SEM, variance-based partial least squares SEM (PLS-SEM), which has multiple advantages over CB-SEM in several situations commonly encountered in social sciences research. A case study in the English Medium Instruction (EMI) context is also demonstrated as an example to facilitate comprehension of the method. The chapter concludes with a discussion of potential applications for other EMI-related contexts and lines of inquiry.

Structural equation modeling (SEM) is a family of statistical techniques and methods for testing hypotheses about causal effects among observed or proxies for latent variables. There are increasing numbers of SEM studies published in the research literatures of various disciplines, including psychology, education, medicine, management, and ecology, among others. Core types of structural equation models are described, and examples of causal hypotheses that can be tested in SEM are considered. Requirements for reporting the results of SEM analyses and common pitfalls to avoid are reviewed. Finally, an example of evaluating model fit is presented along with computer syntax so that readers can reproduce the results.