Predicting L2 reading proficiency with modalities of vocabulary knowledge: A bootstrapping approach

McLean, Stuart; Stewart, Jeffrey; Batty, Aaron Olaf

doi:10.1177/0265532219898380

Cited by 83 publications

(105 citation statements)

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“…3 https://www.psyctc.org/stats/R/CI_correln1.html; http://vassarstats.net/rho.html Future research should consider using the abovementioned three methods, especially CIs, for comparing previous studies. Additionally, other methods that would provide more precise estimates would be (a) a bootstrapping method (McLean et al, 2020), which is useful when primary data are available, and (b) meta-analysis, which can systematically integrate previous studies while taking sample size and measurement error into account (e.g., Plonsky and Oswald, 2015;In'nami et al, 2020). When researchers obtain a matrix of meta-analyzed correlation coefficients through (b), they can more rigorously examine relationships of size and depth using meta-analytic SEM (Cheung, 2015).…”

Section: Discussionmentioning

confidence: 99%

“…The following shows a sample item from the Vocabulary Size Test Beglar, 2007, JALT2007, p. 2), in which test takers are asked to select the most appropriate meaning of the written target word out of four written choices. While the form-meaning association appears relatively simple to define and assess, research has shown that it is not: Size test scores are affected not only by the intended test construct but also by various factors such as differences in item formats and test takers' test-taking strategies (Gyllstad et al, 2015;Kremmel and Schmitt, 2016;McLean et al, 2020).…”

Section: Measuring Size and Depthmentioning

confidence: 99%

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

Koizumi

In’nami

2020

Front. Psychol.

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Measuring Size and Depthmentioning

confidence: 99%

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

Koizumi

In’nami

2020

Front. Psychol.

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“…Receptive knowledge of the first 3,000 word families has been shown to account for 52% of the variance of Chinese learners’ International English Language Testing System (IELTS) listening scores (Matthews & Cheng, 2015). Additionally, vocabulary knowledge predicted between 38% and 61% of Test of English for International Communication (TOEIC) reading performance depending on the modality of the vocabulary testing and number of test items (McLean et al., 2020). To summarize, research has repeatedly emphasized the relationship between L2 vocabulary knowledge and proficiency across domains.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Despite its importance, vocabulary acquisition, and in turn L2 IVA, has yet to emerge as a clearly defined construct within which consensus has been reached (Schmitt, 2019). This lack of consensus notwithstanding, Nation's (2013) conceptualization of vocabulary knowledge as a multidimensional construct has been one of the most consistently referenced vocabulary knowledge frameworks (e.g., Kremmel & Schmitt, 2016; McLean et al., 2020). In the present study, Nation's framework was utilized to identify what vocabulary was and what a vocabulary acquisition intervention entails.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Using these effects, a set of suggested sample sizes was calculated for the most common statistical procedures utilized by L2 instructed vocabulary researchers. While there are areas within L2 teaching research other than instructed vocabulary, few areas are as topical considering the observed strong relationship with L2 proficiency (e.g., Matthews & Cheng, 2015; McLean, Stuart, & Batty, 2020) and its popularity and emphasis among teachers (Lewis, 1993; Wilkins, 1972).…”

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confidence: 99%

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Effect‐Driven Sample Sizes in Second Language Instructed Vocabulary Acquisition Research

Nicklin

Vitta

2021

The Modern Language Journal

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The present study involved the analysis of 81 second language instructed vocabulary acquisition (L2 IVA) studies over 2 phases. In Phase I, we categorized and coded the effect sizes of the studies. Observing that the basic between-and within-subject design dichotomy lacked the sensitivity to capture the heterogeneity of observed effects, we employed a more granular approach. In both between-and withinsubject designs, treatment versus comparison contrasts best represented comparisons of most interest in L2 IVA experiments, with median effect sizes (g) of .62 (between-subject) and .25 (counterbalanced within-subject). In Phase II, the aggregated effect sizes observed in Phase I were utilized in a priori power simulations to suggest approximate sample sizes for common L2 IVA analyses. For conservatively powered between-subject designs, the simulations suggested sample sizes ranging from 292 to 492 participants. Counterbalanced within-subject designs required 95 to 203 subjects depending on the assumed correlation between the repeated measures. The overarching implication of these simulations suggests that future L2 IVA experiments require larger samples that reference effect sizes from previous research, and we offer 3 potential solutions to the problem of obtaining larger samples.

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Incidental vocabulary learning in a Japanese university L2‐English language classroom over a semester

Brown

2021

TESOL Journal

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This article reports on a study that combined two strands of research: studies of incidental vocabulary learning and studies of the classroom lexical environment (in which vocabulary exposure is measured and the potential for learning estimated). The study looked at the orthographic lexical environment in a language classroom over a semester and examined incidental vocabulary learning resulting from exposure to that environment. The investigated class, with N = 29 first language (L1)–Japanese English learners, met twice a week over a 15‐week semester. Because the same courses had been taught to successive cohorts of learners for several years, it was possible to predict orthographic exposure to vocabulary in advance and thus to identify target words for investigation. In a pretest/posttest design, with the learners’ actual exposure to the words monitored over the semester, word knowledge (K = 32) was gauged at two levels: word recognition and meaning recall. Significant gains were observed in each and so the influence of several factors was explored: frequency of occurrence within the class and variation in word form were found to have significant positive effects on gains, while distribution of occurrences (massed or distributed) had no effect.

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Predicting L2 reading proficiency with modalities of vocabulary knowledge: A bootstrapping approach

Cited by 83 publications

References 38 publications

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

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

Effect‐Driven Sample Sizes in Second Language Instructed Vocabulary Acquisition Research

Incidental vocabulary learning in a Japanese university L2‐English language classroom over a semester

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