Specifying the Random Effect Structure in Linear Mixed Effect Models for Analyzing Psycholinguistic Data
Linear Mixed Effect Models (LMEM) have become a popular method for analyzing nested experimental data, which are often encountered in psycholinguistics and other fields. This approach allows experimental results to be generalized to the greater population of both subjects and experimental stimuli. I...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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PsychOpen GOLD/ Leibniz Institute for Psychology
2020-06-01
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| Series: | Methodology |
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| Online Access: | https://meth.psychopen.eu/index.php/meth/article/view/2809 |
| _version_ | 1828077976432934912 |
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| author | Jungkyu Park Ramsey Cardwell Hsiu-Ting Yu |
| author_facet | Jungkyu Park Ramsey Cardwell Hsiu-Ting Yu |
| author_sort | Jungkyu Park |
| collection | DOAJ |
| description | Linear Mixed Effect Models (LMEM) have become a popular method for analyzing nested experimental data, which are often encountered in psycholinguistics and other fields. This approach allows experimental results to be generalized to the greater population of both subjects and experimental stimuli. In an influential paper Bar and his colleagues (2013; https://doi.org/10.1016/j.jml.2012.11.001) recommend specifying the maximal random effect structure allowed by the experimental design, which includes random intercepts and random slopes for all within-subjects and within-items experimental factors, as well as correlations between the random effects components. The goal of this paper is to formally investigate whether their recommendations can be generalized to wider variety of experimental conditions. The simulation results revealed that complex models (i.e., with more parameters) lead to a dramatic increase in the non-convergence rate. Furthermore, AIC and BIC were found to select the true model in the majority of cases, although selection accuracy varied by LMEM random effect structure. |
| first_indexed | 2024-04-11T02:39:11Z |
| format | Article |
| id | doaj.art-9b4ffdd389344575b811e3d6320c760f |
| institution | Directory Open Access Journal |
| issn | 1614-2241 |
| language | English |
| last_indexed | 2024-04-11T02:39:11Z |
| publishDate | 2020-06-01 |
| publisher | PsychOpen GOLD/ Leibniz Institute for Psychology |
| record_format | Article |
| series | Methodology |
| spelling | doaj.art-9b4ffdd389344575b811e3d6320c760f2023-01-02T19:26:11ZengPsychOpen GOLD/ Leibniz Institute for PsychologyMethodology1614-22412020-06-011629211110.5964/meth.2809meth.2809Specifying the Random Effect Structure in Linear Mixed Effect Models for Analyzing Psycholinguistic DataJungkyu Park0Ramsey Cardwell1Hsiu-Ting Yu2Department of Psycholgy, Kyungpook National University, Daegu, South KoreaDepartment of Educational Research Methodology, University of North Carolina at Greensboro, Greensboro, USADepartment of Psychology, National Chengchi University, Taipei, TaiwanLinear Mixed Effect Models (LMEM) have become a popular method for analyzing nested experimental data, which are often encountered in psycholinguistics and other fields. This approach allows experimental results to be generalized to the greater population of both subjects and experimental stimuli. In an influential paper Bar and his colleagues (2013; https://doi.org/10.1016/j.jml.2012.11.001) recommend specifying the maximal random effect structure allowed by the experimental design, which includes random intercepts and random slopes for all within-subjects and within-items experimental factors, as well as correlations between the random effects components. The goal of this paper is to formally investigate whether their recommendations can be generalized to wider variety of experimental conditions. The simulation results revealed that complex models (i.e., with more parameters) lead to a dramatic increase in the non-convergence rate. Furthermore, AIC and BIC were found to select the true model in the majority of cases, although selection accuracy varied by LMEM random effect structure.https://meth.psychopen.eu/index.php/meth/article/view/2809linear mixed-effect modelspsycholinguistic datarandom effect structuremodel specificationrandom effects |
| spellingShingle | Jungkyu Park Ramsey Cardwell Hsiu-Ting Yu Specifying the Random Effect Structure in Linear Mixed Effect Models for Analyzing Psycholinguistic Data Methodology linear mixed-effect models psycholinguistic data random effect structure model specification random effects |
| title | Specifying the Random Effect Structure in Linear Mixed Effect Models for Analyzing Psycholinguistic Data |
| title_full | Specifying the Random Effect Structure in Linear Mixed Effect Models for Analyzing Psycholinguistic Data |
| title_fullStr | Specifying the Random Effect Structure in Linear Mixed Effect Models for Analyzing Psycholinguistic Data |
| title_full_unstemmed | Specifying the Random Effect Structure in Linear Mixed Effect Models for Analyzing Psycholinguistic Data |
| title_short | Specifying the Random Effect Structure in Linear Mixed Effect Models for Analyzing Psycholinguistic Data |
| title_sort | specifying the random effect structure in linear mixed effect models for analyzing psycholinguistic data |
| topic | linear mixed-effect models psycholinguistic data random effect structure model specification random effects |
| url | https://meth.psychopen.eu/index.php/meth/article/view/2809 |
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