Your browser does not support the PubReader view.
Go to this page to see a list of supporting browsers.
The Korean Association for the Study of English Language and Linguistics
[ Article ]
Korea Journal of English Language and Linguistics - Vol. 19, No. 1, pp.76-94
ISSN: 1598-1398 (Print)
Print publication date 31 Mar 2019
Received 14 Feb 2019 Revised 10 Mar 2019 Accepted 19 Mar 2019
DOI: https://doi.org/10.15738/kjell.19.1.201903.76

혼합효과모형(Mixed-Effects Model)을 이용한 실험언어학 데이터 분석 방법 고찰: 자기조절읽기 실험 데이터를 중심으로

신정아
교수, 동국대학교, 영어영문학부, 서울특별시 중구 필동로1길 30, Tel: 02) 2260-3167 jashin@dongguk.edujashin@gmail.com
How to analyze experimental linguistic data using a mixed-effects model in R: Focusing on data from a self-paced reading experiment
Shin, Jeong-Ah
Professor, Dongguk Univ., Division of English, 30 Pildong-ro-1-gil, Jung-gu, Seoul, Tel: 02) 2260-3167 jashin@dongguk.edujashin@gmail.com

Abstract

Shin, Jeong-Ah. 2019. How to analyze experimental linguistic data using a mixed-effects model in R: Focusing on data from a self-paced reading experiment. Korean Journal of English Language and Linguistics 19-1, 76-94. This study examined a practical use of mixed-effects models in R, analyzing accuracy and reading time data from a self-paced reading experiment. It discussed the applications of logistic mixed-effects model for binary data (e.g., accuracy data) and the use of a mixed-effects model for reading time (RT) data, effectively removing outliers within the data set. A sample for mixed-effects model analyses was collected from a previously conducted self-paced reading experiment, involving English reduced relative clauses for 30 advanced and intermediate second language learners. Rationales and guidelines toward selecting the most appropriate mixed-effects model and checking model assumptions were also discussed.

Keywords:

mixed-effects model, linear mixed model, logistic mixed model, experimental linguistics, psycholinguistics, self-paced reading, reading time, RT data, accuracy

Acknowledgments

이 논문은 2016년 한국연구재단의 국제협력사업(NRF-2016K2A9A2A19939367)과 2018년 동국대학교 우수연구자 지원사업(S-2018-G0001-00022)의 지원을 받아 연구되었음.

References

  • 이준규(Lee, J.). 2011. 반응시간을 이용한 제2 언어 어휘의 연결강도 추정(Estimating the strength of second language word association with reference to reaction times). ≪언어학≫(Journal of the Linguistic Society of Korea) 61, 243-261.
  • 이준규(Lee, J.). 2016. 제2 언어 머릿속 사전에 관한 심리언어학적 탐색(A psycholinguistic inquiry of the second language mental lexicon). ≪언어학≫(Journal of the Linguistic Society of Korea) 74, 51-70.
  • 최재웅·홍정하(Choe, J-W. and J. Hong). (역). 2013. 『언어학자를 위한 통계학-R 활용』(Statistics for Linguistics with R: A Practical Introduction). 고려대학교출판문화원(Korea University Press).
  • Baayen, R. H., Davidson, D. J., and D. M. Bates. 2008. Mixed-effects modeling with crossed random effects for subjects and items. Journal of Memory and Language 59(4), 390-412. [https://doi.org/10.1016/j.jml.2007.12.005]
  • Barr, D. J., R. Levy, C. Scheepers and H. J. Tily. 2013. Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of Memory and Language 68(3), 255-278. [https://doi.org/10.1016/j.jml.2012.11.001]
  • Bates, D., M. Maechler, B. Bolker and S. Walker. 2019. lme4: Linear mixed-effects models using Eigen and S4. R package version 1.1-21. http://CRAN.R-project.org/package=lme4
  • Clark, H. H. 1973. The language-as-fixed-effect fallacy: A critique of language statistics in psychological research. Journal of Verbal Learning and Verbal Behavior 12(4), 335-359. [https://doi.org/10.1016/S0022-5371(73)80014-3]
  • Drummond, A. 2013. Ibex Farm. Retrieved from http://spellout.net/ibexfarm
  • Gelman, A. and J. Hill. 2007. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge: Cambridge University Press.
  • Gries, S. 2013. Statistics for Linguistics with R: A Practical Introduction. Berlin: De Gruyter. [https://doi.org/10.1515/9783110307474]
  • Jaeger, F. 2008. Categorical data analysis: Away from ANOVAs (transformation or not) and towards logit mixed models. Journal of Memory and Language 59, 434-446. [https://doi.org/10.1016/j.jml.2007.11.007]
  • Jaeger, F. 2009. Centering several variables. Retrieved from https://hlplab.wordpress.com/2009/04/27/centering-several-variables
  • Jaeger, F. 2008. Modeling self-paced reading data: Effects of word length, word position, spill-over, etc. Retrieved from https://hlplab.wordpress.com/2008/01/23/modeling-self-paced-reading-data-effects-of-word-length-word-position-spill-over-etc/
  • Juffs, A. 1998. Main verb versus reduced relative clause ambiguity resolution in L2 sentence processing. Language Learning 48(1), 107-147. [https://doi.org/10.1111/1467-9922.00034]
  • Kuznetsova A., P. B. Brockhoff and R. H. B. Christensen. 2019. lmerTest: Tests in Linear Mixed Effects Models. R package version 3.1-0. http://CRAN.R-project.org/package=lmerTest
  • Lee, J-H. and J-A. Shin. 2016. Syntactic reanalysis and lingering misinterpretations in L2 sentence processing. Linguistic Research 33(S). 53-79.
  • Marsden, E., Thompson, S., and L. Plonsky. 2018. A methodological synthesis of self-paced reading in second language research. Applied Psycholinguistics 1-44. [https://doi.org/10.1017/S0142716418000036]
  • Matuschek, H., R. Kliegl, S. Vasishth, H. Baayen and D. Bates. 2017. Balancing Type I error and power in linear mixed models. Journal of Memory and Language 94, 305-315. [https://doi.org/10.1016/j.jml.2017.01.001]
  • R Core Team. 2014. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from http://www.R-project.org
  • Rah, A. and D. Adone. 2010. Processing of the reduced relative clause versus main verb ambiguity in L2 learners at different proficiency levels. Studies in Second Language Acquisition 32(1), 79-109. [https://doi.org/10.1017/S027226310999026X]
  • Sarkar, D. 2018. lattice: Trellis Graphics for R. R package version 0.20-38. http://CRAN.R-project.org/package=lattice
  • Seo, H-J. and J-A. Shin. 2016. L2 processing of English pronouns and reflexives: An eye-tracking study. Korean Journal of English Language and Linguistics 16(4), 879-901. [https://doi.org/10.15738/kjell.16.4.201612.879]
  • Winter, B. 2013. Linear models and linear mixed effects models in R with linguistic applications. arXiv:1308.5499 [http://arxiv.org/pdf/1308.5499.pdf, ]
  • Wurm, L. H. and S. A. Fisicaro. 2014. What residualizing predictors in regression analyses does (and what it does not do). Journal of Memory and Language 72, 37-48. [https://doi.org/10.1016/j.jml.2013.12.003]