ArticleBehavior research methods2019
A Bayesian nonlinear mixed-effects location scale model for learning.
Article in Behavior research methods, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Transcranial direct current stimulation enhances delayed retention after 5 days of lower-limb motor skill learning.The Journal of physiology · 2026Trial
- Personality trait and state associations with cognitive functioning and variability in middle-aged and older adults.The journals of gerontology. Series B, Psychological sciences and social sciences · 2026Article
- How am I doing? It varies: mixed-effects location scale modeling to examine intra-individual variability in health-related research.Journal of behavioral medicine · 2026Article
- Ecological momentary assessments of cognitive performance are more variable in breast cancer survivors.Communications psychology · 2025Article
- Negative binomial mixed effects location-scale models for intensive longitudinal count-type physical activity data provided by wearable devices.Biometrics · 2025Article
- Demographic factors associated with within-individual variability of lung function for adults with cystic fibrosis: A UK registry study.Journal of cystic fibrosis : official journal of the European Cystic Fibrosis Society · 2024Article
- Individual random effects model for differences in trait distribution among respondents.Scientific reports · 2024Article
- Alternative covariance structures in mixed-effects models: Addressing intra- and inter-individual heterogeneity.Behavior research methods · 2024Article
- Testing for intersectional measurement invariance with the alignment method: Evaluation of the 8-item patient health questionnaire.Health services research · 2023Article
- Impact of word properties on list learning: An explanatory item analysis.Neuropsychology · 2023Article
- Putting the individual into reliability: Bayesian testing of homogeneous within-person variance in hierarchical models.Behavior research methods · 2022Article
- Beneath the surface: Unearthing within-person variability and mean relations with Bayesian mixed models.Psychological methods · 2021Article
- Studying at university in later life slows cognitive decline: A long-term prospective study.Alzheimer's & dementia (New York, N. Y.) · 2021Article
- Bayesian Multivariate Mixed-Effects Location Scale Modeling of Longitudinal Relations Among Affective Traits, States, and Physical Activity.European journal of psychological assessment : official organ of the European Association of Psychological Assessment · 2020Article
- A Tutorial on Analyzing Ecological Momentary Assessment Data in Psychological Research With Bayesian (Generalized) Mixed-Effects Models.Advances in methods and practices in psychological scienceArticle
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3 authors.
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Abstract
We present a Bayesian nonlinear mixed-effects location scale model (NL-MELSM). The NL-MELSM allows for fitting nonlinear functions to the location, or individual means, and the scale, or within-person variance. Specifically, in the context of learning, this model allows the within-person variance to follow a nonlinear trajectory, where it can be determined whether variability reduces during learning. It incorporates a sub-model that can predict nonlinear parameters for both the location and scale. This specification estimates random effects for all nonlinear location and scale parameters that are drawn from a common multivariate distribution. This allows estimation of covariances among the random effects, within and across the location and the scale. These covariances offer new insights into the interplay between individual mean structures and intra-individual variability in nonlinear parameters. We take a fully Bayesian approach, not only for ease of estimation but also for inference because it provides the necessary and consistent information for use in psychological applications, such as model selection and hypothesis testing. To illustrate the model, we use data from 333 individuals, consisting of three age groups, who participated in five learning trials that assessed verbal memory. In an exploratory context, we demonstrate that fitting a nonlinear function to the within-person variance, and allowing for individual variation therein, improves predictive accuracy compared to customary modeling techniques (e.g., assuming constant variance). We conclude by discussing the usefulness, limitations, and future directions of the NL-MELSM.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.