scholarly journals Specifying the random effect structure in linear mixed effect models for analyzing psycholinguistic data

Methodology ◽  
2020 ◽  
Vol 16 (2) ◽  
pp. 92-111
Author(s):  
Jungkyu Park ◽  
Ramsey Cardwell ◽  
Hsiu-Ting Yu
Keyword(s):  
Random Effect ◽  
Experimental Conditions ◽  
Mixed Effect ◽  
Linear Mixed Effect ◽  
Mixed Effect Models ◽  
Influential Paper ◽  
Within Subjects ◽  
Complex Models ◽  
Random Slopes ◽  
Linear Mixed Effect Models

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.

scholarly journals The role of incubator classification on performance of incubators in Kenya.

2021 ◽  
Vol 10 (6) ◽  
pp. 256-267
Author(s):  
Miriti Jane Kinya ◽  
Kenneth Lawrance Wanjau ◽  
Nyagweth Ebenezer Odeyo
Keyword(s):  
Fixed Effects ◽  
Selection Criteria ◽  
Primary Data ◽  
Cross Sectional ◽  
Mixed Effect ◽  
Study Objective ◽  
Mixed Effect Models ◽  
Random Slopes ◽  
Linear Mixed Effect Models

The study sought to assess the importance of classifying incubators based on the programs offered for optimum performance. Client selection criteria were assessed through three constructs namely: models that fit program goals, uniqueness of ideas, and standard selection tool. A mixed cross-sectional and causal design was adopted and a census was carried out targeting all the 51 incubators. Primary data was collected with an incubator program as a grouping/ cluster variable yielding a multilevel data structure with incubator centres nested in programs. Linear mixed effect models were fitted using Stata to assess the study objective taking into account the fixed effects for the incubator centre level (level-1) and random effects for the program level (level-2). The uniqueness of ideas was found to have a significant fixed effect on performance at level one while at level two, the study found significant random intercepts of incubator centre performance across the programs. Models that match program goals and standard selection tools were also found to have significant random slopes as level two random covariates in the model. Based on the findings of significant random slopes, the study concluded that incubator classification is key for client selection criteria and enhances incubator performance.

scholarly journals Relationship between β-amyloid and structural network topology in decedents without dementia

Neurology ◽  
2020 ◽  
Vol 95 (5) ◽  
pp. e532-e544
Author(s):  
Laura E. Jonkman ◽  
Martijn D. Steenwijk ◽  
Nicky Boesen ◽  
Annemieke J.M. Rozemuller ◽  
Frederik Barkhof ◽  
...  
Keyword(s):  
Network Topology ◽  
Pathologic Change ◽  
Mixed Effect ◽  
Linear Mixed Effect ◽  
Local Efficiency ◽  
Mixed Effect Models ◽  
Global Efficiency ◽  
Structural Network ◽  
Β Amyloid ◽  
Linear Mixed Effect Models

ObjectiveTo investigate the association between β-amyloid (Aβ) load and postmortem structural network topology in decedents without dementia.MethodsFourteen decedents (mean age at death 72.6 ± 7.2 years) without known clinical diagnosis of neurodegenerative disease and meeting pathology criteria only for no or low Alzheimer disease (AD) pathologic change were selected from the Normal Aging Brain Collection Amsterdam database. In situ brain MRI included 3D T1-weighted images for anatomical registration and diffusion tensor imaging for probabilistic tractography with subsequent structural network construction. Network topologic measures of centrality (degree), integration (global efficiency), and segregation (clustering and local efficiency) were calculated. Tissue sections from 12 cortical regions were sampled and immunostained for Aβ and hyperphosphorylated tau (p-tau), and histopathologic burden was determined. Linear mixed effect models were used to assess the relationship between Aβ and p-tau load and network topologic measures.ResultsAβ was present in 79% of cases and predominantly consisted of diffuse plaques; p-tau was sparsely present. Linear mixed effect models showed independent negative associations between Aβ load and global efficiency (β = −0.83 × 10−3, p = 0.014), degree (β = −0.47, p = 0.034), and clustering (β = −0.55 × 10−2, p = 0.043). A positive association was present between Aβ load and local efficiency (β = 3.16 × 10−3, p = 0.035). Regionally, these results were significant in the posterior cingulate cortex (PCC) for degree (β = −2.22, p < 0.001) and local efficiency (β = 1.01 × 10−2, p = 0.014) and precuneus for clustering (β = −0.91 × 10−2, p = 0.017). There was no relationship between p-tau and network topology.ConclusionThis study in deceased adults with AD-related pathologic change provides evidence for a relationship among early Aβ accumulation, predominantly of the diffuse type, and structural network topology, specifically of the PCC and precuneus.

scholarly journals Modelling subject-specific childhood growth using linear mixed-effect models with cubic regression splines

2016 ◽  
Vol 13 (1) ◽  
Author(s):  
Laura M. Grajeda ◽  
Andrada Ivanescu ◽  
Mayuko Saito ◽  
Ciprian Crainiceanu ◽  
Devan Jaganath ◽  
...  
Keyword(s):  
Regression Splines ◽  
Mixed Effect ◽  
Childhood Growth ◽  
Linear Mixed Effect ◽  
Mixed Effect Models ◽  
Subject Specific ◽  
Linear Mixed Effect Models

Snails on oceanic islands: testing the general dynamic model of oceanic island biogeography using linear mixed effect models

2012 ◽  
Vol 40 (1) ◽  
pp. 117-130 ◽  
Author(s):  
Robert A. D. Cameron ◽  
Kostas A. Triantis ◽  
Christine E. Parent ◽  
François Guilhaumon ◽  
María R. Alonso ◽  
...  
Keyword(s):  
Dynamic Model ◽  
Island Biogeography ◽  
Oceanic Islands ◽  
Oceanic Island ◽  
Mixed Effect ◽  
Linear Mixed Effect ◽  
Mixed Effect Models ◽  
Linear Mixed Effect Models ◽  
General Dynamic
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