scholarly journals Bayesian Multivariate Mixed-Effects Location Scale Modeling of Longitudinal Relations among Affective Traits, States, and Physical Activity

2019 ◽  
Author(s):  
Donald Ray Williams ◽  
Siwei Liu ◽  
Stephen Ross Martin ◽  
Philippe Rast
Keyword(s):  
Hypothesis Testing ◽  
Personality Traits ◽  
Random Effects ◽  
Mixed Effects ◽  
Scale Model ◽  
Affective Traits ◽  
Personality Research ◽  
Scale Modeling ◽  
Fully Bayesian ◽  
Experience Sampling Methods

Intensive longitudinal studies and experience sampling methods are becoming more common in psychology. While they provide a unique opportunity to ask novel questions about within-person processes relating to personality, there is a lack of methods specifically built to characterize the interplay between traits and states. We thus introduce a Bayesian multivariate mixed-effects location scale model (M-MELSM). The formulation can simultaneously model both personality traits (the location) and states (the scale) for multivariate data common to personality research. Variables can be included to predict either (or both) the traits and states, in addition to estimating random effects therein. This provides correlations between location and scale random effects, both across and within each outcome, which allows for characterizing relations between any number personality traits and the corresponding states. We take a \textit{fully} Bayesian approach, not only to make estimation possible, but also because it provides the necessary information for use in psychological applications such as hypothesis testing. To illustrate the model we use data from 194 individuals that provided daily ratings of negative and positive affect, as well as their psychical activity in the form of step counts over 100 consecutive days. We describe the fitted model, where we emphasize, with visualization, the richness of information provided by the M-MELSM. We demonstrate Bayesian hypothesis testing for the correlations between the random effects. We conclude by discussing limitations of the MELSM in general and extensions to the M-MELSM specifically for personality research.

scholarly journals Bayesian Multivariate Mixed-Effects Location Scale Modeling of Longitudinal Relations Among Affective Traits, States, and Physical Activity

2020 ◽  
Vol 36 (6) ◽  
pp. 981-997
Author(s):  
Donald R. Williams ◽  
Stephen R. Martin ◽  
Siwei Liu ◽  
Philippe Rast
Keyword(s):  
Physical Activity ◽  
Hypothesis Testing ◽  
Personality Traits ◽  
Random Effects ◽  
Mixed Effects ◽  
Scale Model ◽  
Affective Traits ◽  
Personality Research ◽  
Scale Modeling ◽  
Fully Bayesian

Abstract. Intensive longitudinal studies and experience sampling methods are becoming more common in psychology. While they provide a unique opportunity to ask novel questions about within-person processes relating to personality, there is a lack of methods specifically built to characterize the interplay between traits and states. We thus introduce a Bayesian multivariate mixed-effects location scale model (M-MELSM). The formulation can simultaneously model both personality traits (the location) and states (the scale) for multivariate data common to personality research. Variables can be included to predict either (or both) the traits and states, in addition to estimating random effects therein. This provides correlations between location and scale random effects, both across and within each outcome, which allows for characterizing relations between any number of personality traits and the corresponding states. We take a fully Bayesian approach, not only to make estimation possible, but also because it provides the necessary information for use in psychological applications such as hypothesis testing. To illustrate the model we use data from 194 individuals that provided daily ratings of negative and positive affect, as well as their physical activity in the form of step counts over 100 consecutive days. We describe the fitted model, where we emphasize, with visualization, the richness of information provided by the M-MELSM. We demonstrate Bayesian hypothesis testing for the correlations between the random effects. We conclude by discussing limitations of the MELSM in general and extensions to the M-MELSM specifically for personality research.

scholarly journals Random effects structure for confirmatory hypothesis testing: Keep it maximal

2018 ◽  
Author(s):  
Dale Barr ◽  
Roger Philip Levy ◽  
Christoph Scheepers ◽  
Harry Tily
Keyword(s):  
Hypothesis Testing ◽  
Random Effects ◽  
Gold Standard ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Data Driven ◽  
Linear Mixed Effects Models ◽  
Linear Mixed Effects ◽  
Within Subjects ◽  
Maximal Models

Linear mixed-effects models (LMEMs) have become increasingly prominent in psycholinguistics and related areas. However, many researchers do not seem to appreciate how random effects structures affect the generalizability of an analysis. Here, we argue that researchers using LMEMs for confirmatory hypothesis testing should minimally adhere to the standards that have been in place for many decades. Through theoretical arguments and Monte Carlo simulation, we show that LMEMs generalize best when they include the maximal random effects structure justified by the design. The generalization performance of LMEMs including data-driven random effects structures strongly depends upon modeling criteria and sample size, yielding reasonable results on moderately-sized samples when conservative criteria are used, but with little or no power advantage over maximal models. Finally, random-intercepts-only LMEMs used on within-subjects and/or within-items data from populations where subjects and/or items vary in their sensitivity to experimental manipulations always generalize worse than separate F1 and F2 tests, and in many cases, even worse than F1 alone. Maximal LMEMs should be the ‘gold standard’ for confirmatory hypothesis testing in psycholinguistics and beyond.

scholarly journals A Bayesian Nonlinear Mixed-Effects Location Scale Model for Learning

2018 ◽  
Author(s):  
Donald Ray Williams ◽  
Philippe Rast
Keyword(s):  
Random Effects ◽  
Verbal Memory ◽  
Predictive Accuracy ◽  
Age Groups ◽  
Nonlinear Function ◽  
Mixed Effects ◽  
Individual Variability ◽  
Scale Model ◽  
Nonlinear Mixed Effects ◽  
Context Of Learning

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 while in the process learning. It incorporates a sub-model that can predictnonlinear parameters for the location and/or 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 because it provides the necessaryand 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 discussingthe usefulness, limitations, and future directions of the NL-MELSM.

scholarly journals A Mixed-Effects Location Scale Model for Dyadic Interactions

2018 ◽  
Author(s):  
Philippe Rast ◽  
Emilio Ferrer
Keyword(s):  
Monte Carlo ◽  
Random Effects ◽  
Monte Carlo Methods ◽  
Negative Emotions ◽  
Mixed Effects ◽  
Individual Variability ◽  
Scale Model ◽  
Dyadic Interactions ◽  
Scale Parameters ◽  
Mean Structures

We present a mixed-effects location scale model (MELSM) for examining thedaily dynamics of affect in dyads. The MELSM includes person and timevarying variables to predict the location, or individual means, and the scale,or within-person variances. It also incorporates a sub-model to account forbetween-person variances. The dyadic specification can accommodate individual and partner effects in both the location and the scale components,and allows random effects for all location and scale parameters. All covariances among the random effects, within and across the location and the scaleare also estimated. These covariances offer new insights into the interplayof individual mean structures, intra-individual variability, and the influenceof partner effects on such factors. To illustrate the model, we use data from274 couples who provided daily ratings on their positive and negative emotions toward their relationship – up to 90 consecutive days. The model is fitusing Hamiltonian Monte Carlo methods, and includes subsets of predictorsin order to demonstrate the flexibility of this approach. We conclude witha discussion on the usefulness and the limitations of the MELSM for dyadicresearch.

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.

Personality Processes Related to the Development and Resolution of Alcohol Use Disorders

2018 ◽  
Author(s):  
Kenneth J. Sher ◽  
Andrew Littlefield ◽  
Matthew Lee
Keyword(s):  
Alcohol Use ◽  
Personality Traits ◽  
Alcohol Use Disorder ◽  
Alcohol Use Disorders ◽  
Past Research ◽  
Life Transitions ◽  
Is Research ◽  
Personality Research ◽  
Adult Role ◽  
Static View

This chapter discusses relations between personality and alcohol use disorder (AUD). After reviewing basic terms and concepts in personality research, two major areas of contemporary research are discussed. The first area concerns how personality traits are implicated in etiologic pathways to AUD. This highlights the centrality of personality to conceptualizing AUD and related psychopathology. The second area is research emphasizing movement beyond a static view of personality, recognizing that personality traits are dynamic and change as a function of human development and life transitions. In particular, whereas past research on “maturing out” of AUD emphasized salutary effects of young adult role transitions, recent evidence reveals normative patterns of developmental personality maturation and supports these as additional influences on maturing out. The chapter discusses ways that contextual role effects and personality maturation can perhaps be integrated into a broader model of maturing out of AUD. Implications for future investigation are presented.

A novel synergistic multi-scale modeling framework to predict micro- and meso-scale damage behaviors of 2D triaxially braided composite

2021 ◽  
pp. 105678952110339
Author(s):  
Hongyong Jiang ◽  
Yiru Ren ◽  
Qiduo Jin
Keyword(s):  
Compressive Load ◽  
Scale Model ◽  
Modeling Framework ◽  
Braided Composite ◽  
Multi Scale ◽  
Scale Modeling ◽  
Bridge Model ◽  
Multi Scale Modeling ◽  
Transverse Damage ◽  
Meso Scale

A novel synergistic multi-scale modeling framework with a coupling of micro- and meso-scale is proposed to predict damage behaviors of 2D-triaxially braided composite (2DTBC). Based on the Bridge model, the internal stress and micro damage of constituent materials are respectively coupled with the stress and damage of tow. The initial effective elastic properties of tow (IEEP) used as the predefined data are estimated by micro-mechanics models. Due to in-situ effects, stress concentration factor (SCF) is considered in the micro matrix, exhibiting progressive damage accumulation. Comparisons of IEEP and strengths between the Bridge and Chamis’ theory are conducted to validate the values of IEEP and SCF. Based on the representative volume element (RVE), the macro properties and damage modes of 2DTBC are predicted to be consistent with available experiments and meso-scale simulation. Both axial and transverse damage mechanisms of 2DTBC under tensile or compressive load are revealed. Micro fiber and matrix damage accumulations have significant effects on the meso-scale axial and transverse damage of tows due to multi-scale coupling effects. Different from existing meso-/multi-scale models, the proposed multi-scale model can capture a crucial phenomenon that the transverse damage of tow is vulnerable to micro fiber fracture. The proposed multi-scale framework provides a robust tool for future systematic studies on constituent materials level to larger-scale aeronautical materials.

Promoting Multicultural Personality Development: A Strengths-Based, Positive Psychology Worldview for Schools

2008 ◽  
Vol 12 (2) ◽  
pp. 2156759X0801200
Author(s):  
Joseph G. Ponterotto ◽  
David E. Mendelowitz ◽  
Ernest A. Collabolletta
Keyword(s):  
United States ◽  
Positive Psychology ◽  
Personality Traits ◽  
School Counseling ◽  
School Counselors ◽  
Culturally Diverse ◽  
Personality Development ◽  
The United States ◽  
Multicultural Personality ◽  
Personality Research

This article extends the relevance of multicultural development to the Strengths-Based School Counseling (SBSC; Galassi & Akos, 2007) perspective. A relatively new construct for school counselors, the “multicultural personality” (MP), is introduced and defined. The MP is conceptualized as a cluster of narrow personality traits that can be subsumed under broader models of personality. Research has found that MP development is correlated with coping, adapting, and thriving in increasingly culturally diverse environments such as the United States. Suggestions for integrating MP development across the guiding principles of SBSC are presented.

scholarly journals A Mixed-Effects Location Scale Model for Dyadic Interactions

2018 ◽  
Vol 53 (5) ◽  
pp. 756-775 ◽  
Author(s):  
Philippe Rast ◽  
Emilio Ferrer
Keyword(s):  
Mixed Effects ◽  
Scale Model ◽  
Dyadic Interactions
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