1887
Volume 8, Issue 2
  • ISSN 2215-1478
  • E-ISSN: 2215-1486
USD
Buy:$35.00 + Taxes

Abstract

Abstract

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 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.

Loading

Article metrics loading...

/content/journals/10.1075/ijlcr.21007.lar
2023-01-26
2024-02-28
Loading full text...

Full text loading...

References

  1. Anderson, J. C., & Gerbing, D. W.
    (1982) Some methods for respecifying measurement models to obtain unidimensional construct measurement. Journal of Marketing Research, 19(4), 453–460. 10.1177/002224378201900407
    https://doi.org/10.1177/002224378201900407 [Google Scholar]
  2. Biber, D.
    (1988) Variation across speech and writing. Cambridge University Press. 10.1017/CBO9780511621024
    https://doi.org/10.1017/CBO9780511621024 [Google Scholar]
  3. Bollen, K. A.
    (1989) Structural equations with latent variables. Wiley. 10.1002/9781118619179
    https://doi.org/10.1002/9781118619179 [Google Scholar]
  4. (2002) Latent variables in psychology and the social sciences. Annual Review of Psychology, 531, 605–634. 10.1146/annurev.psych.53.100901.135239
    https://doi.org/10.1146/annurev.psych.53.100901.135239 [Google Scholar]
  5. Bollen, K. A., & Lennox, R.
    (1991) Conventional wisdom on measurement: A structural equation perspective. Psychological Bulletin, 1101, 305–314. 10.1037/0033‑2909.110.2.305
    https://doi.org/10.1037/0033-2909.110.2.305 [Google Scholar]
  6. Doughty, C. J.
    (2019) Cognitive language aptitude. Language Learning, 691, 101–126. 10.1111/lang.12322
    https://doi.org/10.1111/lang.12322 [Google Scholar]
  7. Gilquin, G.
    (2021) The Process Corpus of English in Education: Going beyond the written text. Research in Corpus Linguistics, 10(1), 31–44. 10.32714/ricl.10.01.02
    https://doi.org/10.32714/ricl.10.01.02 [Google Scholar]
  8. Götz, S.
    (2019) Filled pauses across proficiency levels, L1s and learning context variables: A multivariate exploration of the Trinity Lancaster Corpus Sample. International Journal of Learner Corpus Research, 5(2), 159–180. 10.1075/ijlcr.17018.got
    https://doi.org/10.1075/ijlcr.17018.got [Google Scholar]
  9. Gries, S. Th., & Wulff, S.
    (2021) Examining individual variation in learner production data: A few programmatic pointers for corpus-based analyses using the example of adverbial clause ordering. Applied Psycholinguistics, 421, 279–299. 10.1017/S014271642000048X
    https://doi.org/10.1017/S014271642000048X [Google Scholar]
  10. Hancock, G. R., & Schoonen, R.
    (2015) Structural equation modeling: Possibilities for language learning researchers. Language Learning, 65 (Supp. 1), 160–184. 10.1111/lang.12116
    https://doi.org/10.1111/lang.12116 [Google Scholar]
  11. Henseler, J.
    (2021) Composite-based structural equation modeling: Analyzing latent and emergent variables. Guilford Press.
    [Google Scholar]
  12. Hu, L.-T., & Bentler, P. M.
    (1999) Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. 10.1080/10705519909540118
    https://doi.org/10.1080/10705519909540118 [Google Scholar]
  13. In’nami, Y., & Koizumi, R.
    (2011) Structural equation modeling in language testing and learning research: A review. Language Assessment Quarterly, 81, 250–276. 10.1080/15434303.2011.582203
    https://doi.org/10.1080/15434303.2011.582203 [Google Scholar]
  14. Kline, R. B.
    (2013) Reverse arrow dynamics: Feedback loops and formative measurement. InG. R. Hancock & R. O. Mueller (Eds.), Structural equation modeling: A second course (2nd ed.) (pp.41–79). Information Age Publishing.
    [Google Scholar]
  15. (2016) Principles and practice of structural equation modeling (4th ed.). The Guilford Press.
    [Google Scholar]
  16. Larsson, T., Plonsky, L., & Hancock, G. R.
    (2021) On the benefits of structural equation modeling for corpus linguists. Corpus Linguistics and Linguistic Theory, 17(3), 683–714. 10.1515/cllt‑2020‑0051
    https://doi.org/10.1515/cllt-2020-0051 [Google Scholar]
  17. Li, S.
    (2016) The construct validity of language aptitude: A Meta-Analysis. Studies in Second Language Acquisition, 38(4), 801–842. 10.1017/S027226311500042X
    https://doi.org/10.1017/S027226311500042X [Google Scholar]
  18. Linck, J. A., Osthus, P., Koeth, J. T., & Bunting, M. F.
    (2014) Working memory and second language comprehension and production: A meta-analysis. Psychonomic bulletin & review, 21(4), 861–883. 10.3758/s13423‑013‑0565‑2
    https://doi.org/10.3758/s13423-013-0565-2 [Google Scholar]
  19. MacIntyre, P., Gregersen, T., & Mercer, S.
    (2019) Setting an agenda for positive psychology in SLA: Theory, practice, and research. Modern Language Journal, 1031, 262–274. 10.1111/modl.12544
    https://doi.org/10.1111/modl.12544 [Google Scholar]
  20. Marsh, H. W., Hau, K.-T., & Wen, Z.
    (2004) In search of golden rules: Comment on hypothesis-testing approaches to setting cutoff values for fit indexes and dangers in overgeneralizing Hu and Bentler’s (1999) findings. Structural Equation Modeling: A Multidisciplinary Journal, 111, 320–341. 10.1207/s15328007sem1103_2
    https://doi.org/10.1207/s15328007sem1103_2 [Google Scholar]
  21. Mukherjee, J., & Götz, S.
    (2015) Learner corpora and learning context. InS. Granger, G. Gilquin, & F. Meunier (Eds.), Cambridge Handbook of Learner Corpus Research (pp.423–442). Cambridge University Press. 10.1017/CBO9781139649414.019
    https://doi.org/10.1017/CBO9781139649414.019 [Google Scholar]
  22. Posey, C., Roberts, T. L., Lowry, P. B., & Bennett, R. J.
    (2015) Multiple indicators and multiple causes (MIMIC) models as a mixed-modeling technique: A tutorial and an annotated example. Communications of the Association for Information Systems, 36 (Article 11), 179–204. 10.17705/1CAIS.03611
    https://doi.org/10.17705/1CAIS.03611 [Google Scholar]
  23. R Core Team
    R Core Team (2022) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/
    [Google Scholar]
  24. Rodgers, J. L.
    (2010) The epistemology of mathematical and statistical modeling: A quiet methodological revolution. American Psychologist, 651, 1–12. 10.1037/a0018326
    https://doi.org/10.1037/a0018326 [Google Scholar]
  25. Rosseel, Y.
    (2012) lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1–36. 10.18637/jss.v048.i02
    https://doi.org/10.18637/jss.v048.i02 [Google Scholar]
  26. Sudina, E., & Plonsky, L.
    (2021) Academic perseverance in foreign language learning: An investigation of language-specific grit and its conceptual correlates. Modern Language Journal, 1051, 829–857. 10.1111/modl.12738
    https://doi.org/10.1111/modl.12738 [Google Scholar]
  27. Teimouri, Y.
    (2017) L2 selves, emotions, and motivated behaviors. Studies in Second Language Acquisition, 391, 681–709. 10.1017/S0272263116000243
    https://doi.org/10.1017/S0272263116000243 [Google Scholar]
  28. Teimouri, Y., Goetze, J., & Plonsky, L.
    (2019) Second language anxiety and achievement: A meta-analysis. Studies in Second Language Acquisition, 411, 363–387. 10.1017/S0272263118000311
    https://doi.org/10.1017/S0272263118000311 [Google Scholar]
  29. Tekwe, C. D., Zoh, R. S., Bazer, F. W., Wu, G., & Carrol, R. J.
    (2018) Functional multiple indicators, multiple causes measurement error models. Biometrics, 74(1), 127–134. 10.1111/biom.12706
    https://doi.org/10.1111/biom.12706 [Google Scholar]
  30. Watanabe, M., Hirose, K., Den, Y., & Minematsu, N.
    (2008) Filled pauses as cues to the complexity of up-coming phrases for native and non-native listeners. Speech Communication, 50(2), 81–94. 10.1016/j.specom.2007.06.002
    https://doi.org/10.1016/j.specom.2007.06.002 [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.1075/ijlcr.21007.lar
Loading
/content/journals/10.1075/ijlcr.21007.lar
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was successful
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error