Structural equation modeling in language assessment

Abstract

Structural equation modeling (SEM) has been shown to be an effective and powerful tool in language testing and assessment as it offers advantages over fundamental statistical techniques such as regression and correlation analysis. Specifically, SEM is capable of confirming multiple hypothesized direct and indirect relationships among different variables in one analysis. For example, it can be used to identify the relationship of task characteristics with test takers' performance or to explore the relationship of a test and test takers. It can also be used to confirm the factor structure of tests. This chapter provides an overview of the key concepts in SEM analysis, identifies five stages in SEM analysis, and provides an overview of language assessment studies that have utilized this method. An application of this method is shown through a sample study that aims to determine whether test scores on an English diagnostic test of receptive skills can predict performance in an English proficiency test of receptive and productive skills and whether academic background impacts these test scores. The chapter concludes with a discussion on the benefits and limitations of SEM in language testing and assessment research.