Elicited imitation (EI) tasks are a practical tool for measuring second language (L2) knowledge and skills. In this study, we implemented a web-based EI task that measures English morphosyntactic knowledge and compared its measurement properties to a traditional laboratory-based EI. A cohort of 149 L2 English learners engaged in the web-based EI task, and 151 participants completed a traditional lab-based EI counterpart. Correlation analyses revealed a significant, comparable relationship between English proficiency and the two EI versions, with the ungrammatical items showing less consistency that neither improved nor harmed the overall EI effectiveness. Factor analyses corroborated the validity of web-programmed EI, with both EI versions relating similarly to time-pressured, implicit knowledge and untimed, explicit knowledge measures. Our results suggest the potential for utilizing web-based EI to substitute lab-based tasks, enabling larger-scale, more diverse sampling. We end with implications for future web-based EI task users and include a coding guideline for customized web-based EI use.

Although lexical diversity is often used as a measure of productive proficiency (e.g., as an aspect of lexical complexity) in SLA studies involving oral tasks, relatively little research has been conducted to support the reliability and/or validity of these indices in spoken contexts. Furthermore, SLA researchers commonly use indices of lexical diversity such as Root TTR (Guiraud’s index) and D (vocd-D and HD-D) that have been preliminarily shown to lack reliability in spoken L2 contexts and/or have been consistently shown to lack reliability in written L2 contexts. In this study, we empirically evaluate lexical diversity indices with respect to two aspects of reliability (text-length independence and across-task stability) and one aspect of validity (relationship with proficiency scores). The results indicated that neither Root TTR nor D is reliable across different text lengths. However, support for the reliability and validity of optimized versions of MATTR and MTLD was found.

A growing body of evidence demonstrates that individual differences in declarative memory may be an important predictor of second language (L2) abilities. However, the evidence comes from studies using different declarative memory tasks that vary in their reliance on verbal abilities and task demands, which preclude estimating the size of the relationship between declarative memory and L2 learning. To address these concerns, we examined the relationship between verbal and nonverbal declarative memory abilities within the same task while controlling for task demands and stimulus modality, to estimate the upper bound of the relationship between verbal and nonverbal declarative memory. Results indicate that when task demands and stimulus modality are controlled, verbal and nonverbal declarative memory abilities shared a medium-to-large amount of underlying variance. However, future studies should exercise caution in appraising associations between declarative memory abilities and L2 learning until a more precise understanding of the underlying mechanisms is achieved.

Researchers in second language (L2) and education domain use different statistical methods to assess their constructs of interest. Many L2 constructs emerge from elements/parts, i.e., the elements define and form the construct and not the other way around. These constructs are referred to as emergent variables (also called components, formative constructs, and composite constructs). Because emergent variables are composed of elements/parts, they should be assessed through confirmatory composite analysis (CCA). Elements of emergent variables represent unique facets of the construct. Thus, such constructs cannot be properly assessed by confirmatory factor analysis (CFA) because CFA and its underlying common factor model regard these elements to be similar and interchangeable. Conversely, the elements of an emergent variable uniquely define and form the construct, i.e., they are not similar or interchangeable. Thus, CCA is the preferred approach to empirically validate emergent variables such as language skills L2 students’ behavioral engagement and language learning strategies. CCA is based on the composite model, which captures the characteristics of emergent variables more accurately. Aside from the difference in the underlying model, CCA consists of the same steps as CFA, i.e., model specification, model identification, model estimation, and model assessment. In this paper, we explain these steps. and present an illustrative example using publicly available data. In doing so, we show how CCA can be conducted using graphical software packages such as Amos, and we provide the code necessary to conduct CCA in the R package lavaan.