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Word frequency has a long history of being considered the most important predictor of word difficulty and has served as a guideline for several aspects of second language vocabulary teaching, learning, and assessment. However, recent empirical research has challenged the supremacy of frequency as a predictor of word difficulty. Accordingly, applied linguists have questioned the use of frequency as the principal criterion in the development of wordlists and vocabulary tests. Despite being informative, previous studies on the topic have been limited in the way the researchers measured word difficulty and the statistical techniques they employed for exploratory data analysis. In the current study, meaning recall was used as a measure of word difficulty, and random forest was employed to examine the importance of various lexical sophistication metrics in predicting word difficulty. The results showed that frequency was not the most important predictor of word difficulty. Due to the limited scope, research findings are only generalizable to Vietnamese learners of English. [ABSTRACT FROM AUTHOR]

Word difficulty has long been a contentious topic in language acquisition research. There are several methods for word difficulty estimation, including objective indices like word frequency and length, alongside subjective ones, such as word familiarity. Although objective indices are currently widely used, word familiarity offers a more nuanced estimation as it reflects the genuine perceptions of language users independently of specific tasks. Despite its potential, the efficacy of word familiarity in gauging word difficulty remains unclear, and it is often assessed based on whether individuals know a word. This study explored the overarching concept of word familiarity, incorporating perspectives, such as ‘KNOW’, ‘LISTEN’, ‘SPEAK’, ‘READ’, and ‘WRITE’ to investigate their relationship with word difficulty. The findings indicated that anomaly detection significantly enhances estimation accuracy from 0.66 to 0.95 across elementary, intermediate, and advanced words. Notably, word familiarity in ‘WRITE’, ‘SPEAK’, and ‘LISTEN’ was particularly effective in estimating word difficulty.

We describe a large-scale effort to map English-language vocabulary by U.S. school grade levels. Our motivation is to rapidly expand graded vocabulary resources for work with native English speakers in the USA, while taking into consideration school-related influences rather than relying on just the corpus-frequency approaches. We report on the initial effort of data collection, with mapping of about 22K word forms. We provide comparisons of this mapping to some other recent vocabulary mapping efforts, such as age-of-acquisition. We then describe the efforts to automatically expand this resource by using linguistically motivated variables and corpus-based methods. Our current resource maps more than 126K English word forms to US school grade levels. We also compare a subset of our L1 mapped data to English L2 vocabulary levels, as expressed on the CEFR scale, and find that there is a considerable overlap in the order of vocabulary learning in L1 and L2 English. [ABSTRACT FROM AUTHOR]

This article presents a conceptual replication of Hashimoto and Egbert (https://doi.org/10.1111/lang.12353), a study that featured multivariate models where lexical sophistication variables accounted for word difficulty (yes–no recognition) better than frequency alone among learners of English as a second or foreign language from North America. This current study (nwords = 88; npeople = 128) conceptually replicated Hashimoto and Egbert with data from three Asian university English‐for‐academic‐purposes sites. Methodological differences included a more conservative lexical sophistication operationalization process and avoidance of stepwise regression. Like the original study, the replication's findings favored multivariate models over frequency, which predicted 36% of word difficulty's variance alone. In a multiple regression model accounting for word difficulty, R2 =.52, frequency accounted for 17% of the predicted variance with age of acquisition (AoA: 18%) and word naming reaction time (WN_RT: 16%) also being significant predictors. This replication also extended the testing approach by using a mixed‐effect model, involving person and site intercepts as random effects. The model's ability to predict word difficulty fell, marginal R2 =.22, conditional R2 =.40, but frequency, AoA, and WN_RT remained the strongest predictors. Taken together, this replication successfully supports the original study's more‐than‐frequency conclusion while highlighting the need for further research into the area. [ABSTRACT FROM AUTHOR]

The substantive component of construct validity requires a confrontation between empirical test results and content relevance. The Vocabulary Size Test (VST) has been extensively validated in terms of empirical results. Less is known, however, about expert judgments of content relevance. The VST was constructed and validated according to the principle that frequency underlies vocabulary acquisition. This does not mean, however, that the two are equivalent. To better understand the construct of word difficulty as it is measured in VSTs, the results of two Rasch analyses, one on the VST, the other a Multi Facet Rasch Model (MFRM) of a questionnaire distributed to education practitioners were correlated (n = 80, r = 0.67, p < 0.001, 95% CI = [0.53, 0.77]). Semi-structured interviews were then conducted to explore how practitioners understand the concept of word difficulty. Findings indicate that word difficulty is understood to encompass more than frequency, validating recent research into the predictive power of lexical sophistication variables.

This study examines the application of an IRT analysis of words on lists including the General Service List (GSL), New General Service List (NGSL), Academic Word List (AWL), New Academic Word List (NAWL), and TOEIC Service List (TSL). By comparing line graphs, density distribution graphs, and boxplots for the average difficulty of each word list to related lists, we can get a visualization of the data’s distribution. Japanese EFL students responded to one or more of 84Yes/No test forms compiled from 5,880 unique real words and 2,520 nonwords. The real words were analyzed using Winsteps (Linacre,2005) resulting in IRT estimates for each word. By summing the difficulties of each word, we can calculate the average difficulty of each word list which can then be used to rank the lists. In effect, the process supports the concurrent validity of the lists. The analysis indicates the word family approach results in more difficult word lists. The mean difficulties of the GSL and the BNC_COCA appear to be more divergent and more difficult particularly over the first 4000 words, possibly due to the use of Bauer and Nation’s (1993) Affix Level 6 definition for their compilation. Finally, just as we should expect word lists for beginners to have higher frequency words than subsequent lists, we should also expect them to be easier with more words known to learners. This can be seen with the gradual but marked difference between the different word lists of the NGSL and its supplemental SPs.

Frequency is often the only variable considered when researchers or teachers develop vocabulary materials for second language (L2) learners. However, researchers have also found that many other variables affect vocabulary acquisition. In this study, we explored the relationship between L2 vocabulary acquisition and a variety of lexical characteristics using vocabulary recognition test data from L2 English learners. Conducting best subsets multiple regression analysis to explore all possible combinations of variables, we produced a best‐fitting model of vocabulary difficulty consisting of six variables (R2 =.37). The fact that many variables significantly contributed to the regression model and that a large amount of variance remained yet unexplained by the frequency variable considered in this study indicates that much more than frequency alone affects the likelihood that learners will learn certain L2 words. [ABSTRACT FROM AUTHOR]

The present study investigated preschoolers’ knowledge of vocabulary that appears in first grade basal readers by applying Rasch modeling to data from a researcher-developed receptive picture vocabulary assessment administered to 238 children. Levels of word difficulty for dual language learners (DLLs) and monolingual English learners (MELs) were compared. A total of 60 target words were selected from the glossaries of basal readers, and two test forms of 30 words each were created with four illustrations per word plate. Rasch analyses carried out on the entire preschool sample and on separate samples of DLLs and MELs showed that the ranking of target words by difficulty was similar for DLLs and MELs, but the groups differed in mastery of target words. Language status and level of general vocabulary knowledge were stronger predictors of word difficulty than age for this sample of preschoolers. Findings suggest MELs and DLLs learn English words in a similar order, but further research is needed to support this conclusion. [ABSTRACT FROM PUBLISHER]