The Korean Association for the Study of English Language and Linguistics

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

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