The Korean Association for the Study of English Language and Linguistics

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Korea Journal of English Language and Linguistics - Vol. 19 , No. 1

[ Article ]
Korea Journal of English Language and Linguistics - Vol. 19, No. 1, pp.76-94
Abbreviation: KASELL
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.edu)(jashin@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.edu)(jashin@gmail.com)
Funding Information ▼

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
1. 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.
2. 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.
3. 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).
4. 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.
5. 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.
6. 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
7. 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.
8. Drummond, A. 2013. Ibex Farm. Retrieved from http://spellout.net/ibexfarm
9. Gelman, A. and J. Hill. 2007. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge: Cambridge University Press.
10. Gries, S. 2013. Statistics for Linguistics with R: A Practical Introduction. Berlin: De Gruyter.
11. 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.
12. Jaeger, F. 2009. Centering several variables. Retrieved from https://hlplab.wordpress.com/2009/04/27/centering-several-variables
13. 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/
14. Juffs, A. 1998. Main verb versus reduced relative clause ambiguity resolution in L2 sentence processing. Language Learning 48(1), 107-147.
15. 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
16. Lee, J-H. and J-A. Shin. 2016. Syntactic reanalysis and lingering misinterpretations in L2 sentence processing. Linguistic Research 33(S). 53-79.
17. Marsden, E., Thompson, S., and L. Plonsky. 2018. A methodological synthesis of self-paced reading in second language research. Applied Psycholinguistics 1-44.
18. 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.
19. 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
20. 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.
21. Sarkar, D. 2018. lattice: Trellis Graphics for R. R package version 0.20-38. http://CRAN.R-project.org/package=lattice
22. 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.
23. 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]
24. 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.