Applying Linear Mixed Effects Models with Crossed Random Effects to Psycholinguistic Data: Multilevel Specification and Model Selection.
Bibliographic record
Abstract
Applying linear mixed effects regression (LMER) models to psycholinguistic data was made popular by 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.076 | 0.210 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".