Learner corpus research has a strong tradition of collecting metadata. However, while we tend to collect rich descriptive information about learners on directly measurable variables such as age, year of study, and time spent abroad, we frequently do not know much about learner characteristics that cannot be measured directly (and that thus need to be measured through questionnaires and tests) such as language aptitude, working memory, and motivation, which have been identified as important variables in neighboring fields such as Second Language Acquisition. In this position piece, we (i) join the proponents of increased focus on learner characteristics in LCR in arguing in favor of collecting information about such variables and (ii) introduce an analytical framework that can be used to model these variables. Specifically, the primary focus of this paper is to discuss the concept of latent variables as it relates to LCR and show how their standard form can be used to model learner characteristics within the structural equation modeling analytical framework.
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Kaatari, Henrik, Tove Larsson, Ying Wang, Seda Acikara-Eickhoff & Pia Sundqvist
2023. Exploring the effects of target-language extramural activities on students’ written production. Journal of Second Language Writing 62 ► pp. 101062 ff.
Xu, Yiran, Jingyuan Zhuang, Ryan Blair, Amy I. Kim, Fei Li, Rachel Thorson Hernández & Luke Plonsky
2023. Modeling quality and prestige in applied linguistics journals: A bibliometric and synthetic analysis. Studies in Second Language Learning and Teaching 13:4 ► pp. 755 ff.
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