Applying Linear Mixed Effects Models with Crossed Random Effects to Psycholinguistic Data: Multilevel Specification and Model Selection.
Applying linear mixed effects regression (LMER) models to psycholinguistic data was made popular by Baayen, Davidson, and Bates (2008). However, applied researchers sometimes encounter model specification difficulties when using such models. This article presents a multilevel specifica...
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| Format: | Article |
| Language: | English |
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Université d'Ottawa
2015-06-01
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| Series: | Tutorials in Quantitative Methods for Psychology |
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| Online Access: | http://www.tqmp.org/RegularArticles/Vol11-2/p078/p078.pdf |
| _version_ | 1811308347160264704 |
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| author | Hsiu-Ting Yu |
| author_facet | Hsiu-Ting Yu |
| author_sort | Hsiu-Ting Yu |
| collection | DOAJ |
| description | Applying linear mixed effects regression (LMER) models to psycholinguistic data was made popular by Baayen, Davidson, and Bates (2008). However, applied researchers sometimes encounter model specification difficulties when using such models. This article presents a multilevel specification of LMERs customized for typical psycholinguistic studies. The proposed LMER specifications with crossed random effects allow different combinations of random intercept effects or random slope effects to be specified directly for subject and item covariates. As a result, this approach allows researchers to describe, specify, and interpret a wide range of effects in an LMER more easily. Next, the syntax and steps involved in using the PROC MIXED procedure in SAS to fit the discussed models are illustrated. Thirdly, various issues relating to model selection, specifically for the random component of LMER models with crossed random effects, are discussed. Finally, this article concludes with remarks about model specification and selection of the random structure in the context of analyzing psycholinguistic data using LMERs specifically. This paper provides readers conducting psycholinguistic research with a complete tutorial on how to select, apply, and interpret the multilevel specification of LMERs. |
| first_indexed | 2024-04-13T09:21:35Z |
| format | Article |
| id | doaj.art-daa3d41ef44c4ca9bae13d6c05fe31c7 |
| institution | Directory Open Access Journal |
| issn | 1913-4126 |
| language | English |
| last_indexed | 2024-04-13T09:21:35Z |
| publishDate | 2015-06-01 |
| publisher | Université d'Ottawa |
| record_format | Article |
| series | Tutorials in Quantitative Methods for Psychology |
| spelling | doaj.art-daa3d41ef44c4ca9bae13d6c05fe31c72022-12-22T02:52:36ZengUniversité d'OttawaTutorials in Quantitative Methods for Psychology1913-41262015-06-011127888Applying Linear Mixed Effects Models with Crossed Random Effects to Psycholinguistic Data: Multilevel Specification and Model Selection.Hsiu-Ting Yu0McGill UniversityApplying linear mixed effects regression (LMER) models to psycholinguistic data was made popular by Baayen, Davidson, and Bates (2008). However, applied researchers sometimes encounter model specification difficulties when using such models. This article presents a multilevel specification of LMERs customized for typical psycholinguistic studies. The proposed LMER specifications with crossed random effects allow different combinations of random intercept effects or random slope effects to be specified directly for subject and item covariates. As a result, this approach allows researchers to describe, specify, and interpret a wide range of effects in an LMER more easily. Next, the syntax and steps involved in using the PROC MIXED procedure in SAS to fit the discussed models are illustrated. Thirdly, various issues relating to model selection, specifically for the random component of LMER models with crossed random effects, are discussed. Finally, this article concludes with remarks about model specification and selection of the random structure in the context of analyzing psycholinguistic data using LMERs specifically. This paper provides readers conducting psycholinguistic research with a complete tutorial on how to select, apply, and interpret the multilevel specification of LMERs.http://www.tqmp.org/RegularArticles/Vol11-2/p078/p078.pdfModel selectionlinear mixed effects modelrandom effectspsycholinguistics |
| spellingShingle | Hsiu-Ting Yu Applying Linear Mixed Effects Models with Crossed Random Effects to Psycholinguistic Data: Multilevel Specification and Model Selection. Tutorials in Quantitative Methods for Psychology Model selection linear mixed effects model random effects psycholinguistics |
| title | Applying Linear Mixed Effects Models with Crossed Random Effects to Psycholinguistic Data: Multilevel Specification and Model Selection. |
| title_full | Applying Linear Mixed Effects Models with Crossed Random Effects to Psycholinguistic Data: Multilevel Specification and Model Selection. |
| title_fullStr | Applying Linear Mixed Effects Models with Crossed Random Effects to Psycholinguistic Data: Multilevel Specification and Model Selection. |
| title_full_unstemmed | Applying Linear Mixed Effects Models with Crossed Random Effects to Psycholinguistic Data: Multilevel Specification and Model Selection. |
| title_short | Applying Linear Mixed Effects Models with Crossed Random Effects to Psycholinguistic Data: Multilevel Specification and Model Selection. |
| title_sort | applying linear mixed effects models with crossed random effects to psycholinguistic data multilevel specification and model selection |
| topic | Model selection linear mixed effects model random effects psycholinguistics |
| url | http://www.tqmp.org/RegularArticles/Vol11-2/p078/p078.pdf |
| work_keys_str_mv | AT hsiutingyu applyinglinearmixedeffectsmodelswithcrossedrandomeffectstopsycholinguisticdatamultilevelspecificationandmodelselection |