Structural Equation Modeling of Vocabulary Size and Depth Using Conventional and Bayesian Methods.

ABSTRACT: In classifications of vocabulary knowledge, vocabulary size and depth have often been separately conceptualized (Schmitt, 2014). Although size and depth are known to be substantially correlated, it is not clear whether they are a single construct or two separate components of vocabulary knowledge (Yanagisawa and Webb, 2020). This issue has not been addressed extensively in the literature and can be better examined using structural equation modeling (SEM), with measurement error modeled separately from the construct of interest. The current study reports on conventional and Bayesian SEM approaches (e.g., Muthén and Asparouhov, 2012) to examine the factor structure of the size and depth of second language vocabulary knowledge of Japanese adult learners of English. A total of 255 participants

SUBMITTER: Koizumi R 

PROVIDER: S-EPMC7187790 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature