Dataset Information

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.

SUBMITTER: Koizumi R

PROVIDER: S-EPMC7187790 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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

Koizumi Rie R In'nami Yo Y

Frontiers in psychology 20200421

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 separatel ...[more]

PMID: 32373013

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Project description:This study investigated Chinese English-as-a-foreign-language (EFL) learners' use of vocabulary learning strategies (VLSs) and its relationship with vocabulary knowledge (VK), especially in relation to proficiency, gender, and discipline. Structural equation models were established following exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) procedures, and mediation analyses and multiple-group analyses, as well as analyses of variance, were conducted. Four hundred nineteen sophomores' strategy use frequency, Vocabulary Size Test (VST) scores (indicative of breadth of VK), Word Associates Test (WAT) scores (indicative of depth of VK), College English Test Band-4 scores, and gender and discipline categories were used as data. Proficiency significantly predicted Attention and Guessing positively but was a negative predictor of Socializing (asking others for help). Girls liked making notes while using dictionaries (DictNote) and Socializing, and students of arts also took more notes. Attention and Guessing significantly predicted VST and WAT positively, but Socializing significantly predicted the breadth and depth of VK negatively, and DictNote, Association, and Repetition had no significant relationship. The predictive power of Attention, Guessing, and Socializing, however, was achieved mainly, or for an important part, via the mediating or indirect effects of proficiency. Gender moderated the predictive power of Attention, Socializing, and DictNote over VST, greater for male students, whereas discipline moderated the relationship between Guessing and WAT, stronger for arts students. The findings are related to strategy features, gender characteristics, disciplinary influence, the EFL context and culture, and effective learning. This study reveals the complex relationships among use of VLSs, VK, and learner variables. Attention is called for to third-party factors in understanding VLSs-VK relationships. Given the important mediating effects of proficiency, it is proposed that vocabulary learning be strategically integrated into the accumulative process of English learning.

Dataset Information

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

Publications

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

Similar Datasets

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