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Structural Equation Modeling of Vocabulary Size and Depth Using Conventional and Bayesian Methods by Koizumi, Rie; Inânami, Yo is a Psychology article available to read on EtoBox.

What is Structural Equation Modeling of Vocabulary Size and Depth Using Conventional and Bayesian Methods about?

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 took five vocabulary tests. One test was designed to measure vocabulary size in terms of the number of words known, while the remaining four were designed to measure vocabulary depth in terms of word association, polysemy, and collocation. All tests used a multiple-choice format. The size test was divided into three subtests according to word frequency. Results from conventional and Bayesian SEM

Who reads Structural Equation Modeling of Vocabulary Size and Depth Using Conventional and Bayesian Methods?

Author

Koizumi, Rie; Inânami, Yo

Publisher

Frontiers; Frontiers Media SA; Frontiers Media S.A.; Lausanne: Frontiers Media S.A., 2010- (ISSN 1664-1078)

Published

2020

Language

EN

Field

Psychology (Social Sciences)