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 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 show that a correlated two-factor model of size and depth with three and four indicators, respectively, fit better than a single-factor model of size and depth. In the two-factor model, vocabulary size and depth were strongly correlated (r = 0.945 for conventional SEM and 0.943 for Bayesian SEM with cross-loadings), but they were distinct. The implications of these findings are discussed.

Conceptualizing L2 Vocabulary Knowledge

“…In addition, employing CFA methodologies, Kremmel et al (2017) found that an L2 phraseological-knowledge factor correlated at .90 with a general vocabulary factor while measuring different word sets. Similarly, Koizumi and In'nami (2020) found a striking correlation of .94-.95 between an L2 form-meaning link factor and a depth factor comprised of polysemy, collocations, and associations. The unidimensional model revealed in the current study provides evidence that the word-knowledge aspects do not behave as entirely separable entities and explains why the correlations exhibited by much previous research are so high that they can barely distinguish the lexical aspects.…”

“…In a related study, Koizumi and In'nami (2020) investigated the factor structure of vocabulary knowledge, understood as the combination of size (form-meaning link) and depth (polysemy, collocation, and word association) of knowledge, in Japanese L2 English learners. As in Kieffer and Lesaux (2012), the study employed readily available written tests designed for a different research purpose and, thus, each task measured distinct target words across each word-knowledge component.…”

To what extent does productive derivational knowledge of adult L1 speakers and L2 learners at two educational levels differ?

“…This may indicate that some of the headwords from lower frequency word families in the test were unknown to the EFL undergraduate students, making the test more challenging for them. Research has shown that vocabulary size and depth of vocabulary knowledge are closely related (González‐Fernández & Schmitt, 2020; Koizumi & In’nami, 2020). Given that receptive affix knowledge and vocabulary size are likely to be closely related to each other (Mochizuki & Aizawa, 2000), future research should directly investigate the effect of vocabulary size on productive knowledge of derivatives.…”

The contributory role of grammar vs. vocabulary in L2 reading: An SEM approach

“…In a recent study, Vafaee and Suzuki (2020) found that although the correlation is not weak between vocabulary size and depth, they are separate constructs. Likewise, Koizumi and In'nami (2020), using both conventional and Bayesian SEM, reported a strong correlation between them, but identified them to be separate constructs. In the main, although teasing them apart as two separate constructs may be virtually difficult, it is safe to say that their inclusion results in a more accurate measurement of the vocabulary construct (Vafaee & Suzuki, 2020).…”