On learner characteristics and why we should model them as latent variables

Tove Larsson, Luke Plonsky, Gregory R. Hancock

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)237-260
Number of pages24
JournalInternational Journal of Learner Corpus Research
Issue number2
StatePublished - Dec 31 2022


  • latent variable path analysis
  • latent variables
  • learner characteristics
  • metadata
  • observed variables
  • structural equation modeling

ASJC Scopus subject areas

  • Language and Linguistics
  • Education
  • Linguistics and Language


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