Bayesian Hierarchical/Multilevel Models for Inference and Prediction Using Cross-System Lake Data

2009 ◽  
pp. 111-136 ◽  
Author(s):  
Craig A. Stow ◽  
E. Conrad Lamon ◽  
Song S. Qian ◽  
Patricia A. Soranno ◽  
Kenneth H. Reckhow
Keyword(s):  
Multilevel Models ◽  
Bayesian Hierarchical ◽  
Cross System

Noise-Induced Balance Loss? Rethinking Clinical Implications of Noise Exposure

2019 ◽  
Vol 4 (6) ◽  
pp. 1418-1422
Author(s):  
Bre Myers ◽  
J. Andrew Dundas
Keyword(s):  
Vestibular System ◽  
Noise Exposure ◽  
Balance Control ◽  
Scope Of Practice ◽  
Hearing Conservation ◽  
Exposed Population ◽  
Auditory Systems ◽  
Current Article ◽  
Balance Loss ◽  
Cross System

Purpose The primary aim of the current article is to provide a brief review of the literature regarding the effects of noise exposure on the vestibular and balance control systems. Although the deleterious effects of noise on the auditory system are widely known and continue to be an active area of research, much less is known regarding the effects of noise on the peripheral vestibular system. Audiologists with working knowledge of how both systems interact and overlap are better prepared to provide comprehensive care to more patients as assessment of both the auditory and vestibular systems has been in the audiologists' scope of practice since 1992. Method A narrative review summarizes salient findings from the archival literature. Results Temporary and permanent effects on vestibular system function have been documented in multiple studies. Hearing conservation, vestibular impairment, and fall risk reduction may be more intimately related than previously considered. Conclusions A full appreciation of both the vestibular and auditory systems is necessary to address the growing and aging noise-exposed population. More cross-system studies are needed to further define the complex relationship between the auditory and vestibular systems to improve comprehensive patient care.

Modeling Intraindividual Variability in Three-Level Multilevel Models

Methodology ◽  
2018 ◽  
Vol 14 (3) ◽  
pp. 95-108 ◽  
Author(s):  
Steffen Nestler ◽  
Katharina Geukes ◽  
Mitja D. Back
Keyword(s):  
Multilevel Model ◽  
Multilevel Models ◽  
Mixed Effects ◽  
Repeated Measurements ◽  
Scale Model ◽  
Suggested Approach ◽  
Psychological Study ◽  
Level Data ◽  
Ecological Momentary ◽  
Momentary Assessment

Abstract. The mixed-effects location scale model is an extension of a multilevel model for longitudinal data. It allows covariates to affect both the within-subject variance and the between-subject variance (i.e., the intercept variance) beyond their influence on the means. Typically, the model is applied to two-level data (e.g., the repeated measurements of persons), although researchers are often faced with three-level data (e.g., the repeated measurements of persons within specific situations). Here, we describe an extension of the two-level mixed-effects location scale model to such three-level data. Furthermore, we show how the suggested model can be estimated with Bayesian software, and we present the results of a small simulation study that was conducted to investigate the statistical properties of the suggested approach. Finally, we illustrate the approach by presenting an example from a psychological study that employed ecological momentary assessment.

The impact of participant pathways on the effectiveness of a community-based physical activity intervention grounded in motivational interviewing

2020 ◽  
Author(s):  
Matthew Wade ◽  
Nicola Brown ◽  
James Steele ◽  
Steven Mann ◽  
Bernadette Dancy ◽  
...  
Keyword(s):  
Physical Activity ◽  
Primary Care ◽  
Motivational Interviewing ◽  
Social Action ◽  
Multilevel Models ◽  
Physical Activity Intervention ◽  
Mental Wellbeing ◽  
Community Based ◽  
The Impact ◽  
Time Point

Background: Brief advice is recommended to increase physical activity (PA) within primary care. This study assessed change in PA levels and mental wellbeing after a motivational interviewing (MI) community-based PA intervention and the impact of signposting [SP] and Social Action [SA] (i.e. weekly group support) pathways. Methods: Participants (n=2084) took part in a community-based, primary care PA programme using MI techniques. Self-reported PA and mental wellbeing data were collected at baseline (following an initial 30-minute MI appointment), 12-weeks, six-months, and 12-months. Participants were assigned based upon the surgery they attended to the SP or SA pathway. Multilevel models were used to derive point estimates and 95%CIs for outcomes at each time point and change scores. Results: Participants increased PA and mental wellbeing at each follow-up time point through both participant pathways and with little difference between pathways. Retention was similar between pathways at 12-weeks, but the SP pathway retained more participants at six-months and 12-months. Conclusions: Both pathways produced similar improvements in PA and mental wellbeing, suggesting the effectiveness of MI based PA interventions. However, due to lower resources required yet similar effects, SP pathways are recommended over SA to support PA in primary care settings.

Justifying the data analytical choice in single case research in relation to the expected data pattern

2019 ◽  
Author(s):  
Rumen Manolov
Keyword(s):  
Data Analysis ◽  
Visual Analysis ◽  
Multilevel Models ◽  
Single Case ◽  
Analytical Techniques ◽  
Real Data ◽  
Small Scale ◽  
Data User ◽  
Single Case Research ◽  
User Friendly

The lack of consensus regarding the most appropriate analytical techniques for single-case experimental designs data requires justifying the choice of any specific analytical option. The current text mentions some of the arguments, provided by methodologists and statisticians, in favor of several analytical techniques. Additionally, a small-scale literature review is performed in order to explore if and how applied researchers justify the analytical choices that they make. The review suggests that certain practices are not sufficiently explained. In order to improve the reporting regarding the data analytical decisions, it is proposed to choose and justify the data analytical approach prior to gathering the data. As a possible justification for data analysis plan, we propose using as a basis the expected the data pattern (specifically, the expectation about an improving baseline trend and about the immediate or progressive nature of the intervention effect). Although there are multiple alternatives for single-case data analysis, the current text focuses on visual analysis and multilevel models and illustrates an application of these analytical options with real data. User-friendly software is also developed.

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