Skewness and Staging: Does the Floor Effect Induce Bias in Multilevel AR(1) Models?

Multilevel autoregressive models are popular choices for the analysis of intensive longitudinal data in psychology. Empirical studies have found a positive correlation between autoregressive parameters of affective time series and the between-person measures of psychopathology, a phenomenon known as the staging effect. However, it has been argued that such findings may represent a statistical artifact: Although common models assume normal error distributions, empirical data (for instance, measurements of negative affect among healthy individuals) often exhibit the floor effect, that is response distributions with high skewness, low mean, and low variability. In this paper, we investigated whether—and to what extent—the floor effect leads to erroneous conclusions by means of a simulation study. We describe three dynamic models which have meaningful substantive interpretations and can produce floor-effect data. We simulate multilevel data from these models, varying skewness independent of individuals’ autoregressive parameters, while also varying the number of time points and cases. Analyzing these data with the standard multilevel AR(1) model we found that positive bias only occurs when modeling with random residual variance, whereas modeling with fixed residual variance leads to negative bias. We discuss the implications of our study for data collection and modeling choices

Kafirin structure and functionality

The structural and functional properties of kafirins are reviewed. Three classes of kafirin: the a, ß and ? forms have been identified at the protein level and one, the d, has been identified only at the gene and transcript levels. All forms show high homology with the equivalent zein proteins. By analogy with the zeins it is believed that the a-kafirins probably have an extended hairpin structure in solution, comprising elements of a-helix, ß-sheet and turns folded back on itself. Kafirins are the most hydrophobic of the prolamins as shown by their solubility, and calculated hydration free energies. The proteins exhibit extensive cross-linking by disulphide bonds and on cooking form indigestible aggregates which are not solubilised by reduction of disulphide bonds. In spite of continuing studies, the reasons for the low digestibility of the protein remain uncertain and there may be several factors involved. Other research has shown that kafirins may have non-food uses and may be used to form films

Molecular mechanism by which prominent human gut Bacteroidetes utilize mixed-linkage beta-glucans, major health-promoting cereal polysaccharides

Microbial utilization of complex polysaccharides is a major driving force in shaping the composition of the human gut microbiota. There is a growing appreciation that finely tuned polysaccharide utilization loci enable ubiquitous gut Bacteroidetes to thrive on the plethora of complex polysaccharides that constitute “dietary fiber.” Mixed-linkage β(1,3)/β(1,4)-glucans (MLGs) are a key family of plant cell wall polysaccharides with recognized health benefits but whose mechanism of utilization has remained unclear. Here, we provide molecular insight into the function of an archetypal MLG utilization locus (MLGUL) through a combination of biochemistry, enzymology, structural biology, and microbiology. Comparative genomics coupled with growth studies demonstrated further that syntenic MLGULs serve as genetic markers for MLG catabolism across commensal gut bacteria. In turn, we surveyed human gut metagenomes to reveal that MLGULs are ubiquitous in human populations globally, which underscores the importance of gut microbial metabolism of MLG as a common cereal polysaccharide

Reciprocal relationships between trajectories of depressive symptoms and screen media use during adolescence

Adolescents are constantly connected with each other and the digital landscape through a myriad of screen media devices. Unprecedented access to the wider world and hence a variety of activities, particularly since the introduction of mobile technology, has given rise to questions regarding the impact of this changing media environment on the mental health of young people. Depressive symptoms are one of the most common disabling health issues in adolescence and although research has examined associations between screen use and symptoms of depression, longitudinal investigations are rare and fewer still consider trajectories of change in symptoms. Given the plethora of devices and normalisation of their use, understanding potential longitudinal associations with mental health is crucial. A sample of 1,749 (47% female) adolescents (10-17 years) participated in six waves of data collection over two years. Symptoms of depression, time spent on screens, and on separate screen activities (social networking, gaming, web browsing, TV/passive) were self-reported. Latent growth curve modelling revealed three trajectories of depressive symptoms (Low-Stable, High-Decreasing, and Low-Increasing) and there were important differences across these groups on screen use. Some small, positive associations were evident between depressive symptoms and later screen use, and between screen use and later depressive symptoms. However, a Random Intercept Cross Lagged Panel Model revealed no consistent support for a longitudinal association. The study highlights the importance of considering differential trajectories of depressive symptoms and specific forms of screen activity to understand these relationships

Self organization in oleic acid-coated CoFe2O4 colloids: a SAXS study

We report a structural study of magnetic colloids composed of CoFe₂O₄ nanoparticles (mean radii in the range 2–7 nm) synthesized by thermal decomposition of different high boiling temperature organic solvents in the presence of oleic acid and oleylamine, and subsequently re-suspended in hexane. Although the surfactant layer prevents permanent aggregation and precipitation of the disperse phase, competition between attractive interactions (i.e., dipolar and van der Waals) and repulsive steric interaction leads to self organization of the magnetic nanoparticles. Our small angle X-ray scattering results evidence the presence of distinctive self organized structures in the liquid colloid depending on the type of solvent used in the synthesis. A completely homogeneous dispersion is obtained for those colloids synthesized with benzyl-ether and octadecene. Bi-disperse systems, in which nanoclusters coexist with free nanoparticles, appear when phenyl-ether and trioctylamine are used. Chain-like structures are observed in a colloid containing the particles synthesized using phenyl-ether, while more compact 3D structures form in colloids prepared with particles synthesized with trioctylamine. The presented results have important implications in the design and selection of magnetic nanoparticles for those applications where the size dispersion determines the final efficiency of the material, such as magnetic fluid hyperthermia clinical therapy.Facultad de Ciencias ExactasInstituto de Física La Plat

Modeling affect dynamics: State-of-the-art and future challenges

© 2015 ISRE and SAGE. The current article aims to provide an up-to-date synopsis of available techniques to study affect dynamics using intensive longitudinal data (ILD). We do so by introducing the following eight dichotomies that help elucidate what kind of data one has, what process aspects are of interest, and what research questions are being considered: (1) single- versus multiple-person data; (2) univariate versus multivariate models; (3) stationary versus nonstationary models; (4) linear versus nonlinear models; (5) discrete time versus continuous time models; (6) discrete versus continuous variables; (7) time versus frequency domain; and (8) modeling the process versus computing descriptives. In addition, we discuss what we believe to be the most urging future challenges regarding the modeling of affect dynamics.status: publishe

Personality dynamics

This Encyclopedia provides a comprehensive overview of individual differences within the domain of personality, with major sub-topics including assessment and research design, taxonomy, biological factors, evolutionary evidence, motivation, ...status: accepte

Changing dynamics: Time-varying autoregressive models using generalized additive modeling

In psychology, the use of intensive longitudinal data has steeply increased during the past decade. As a result, studying temporal dependencies in such data with autoregressive modeling is becoming common practice. However, standard autoregressive models are often suboptimal as they assume that parameters are time-invariant. This is problematic if changing dynamics (e.g., changes in the temporal dependency of a process) govern the time series. Often a change in the process, such as emotional well-being during therapy, is the very reason why it is interesting and important to study psychological dynamics. As a result, there is a need for an easily applicable method for studying such nonstationary processes that result from changing dynamics. In this article we present such a tool: the semiparametric TV-AR model. We show with a simulation study and an empirical application that the TV-AR model can approximate nonstationary processes well if there are at least 100 time points available and no unknown abrupt changes in the data. Notably, no prior knowledge of the processes that drive change in the dynamic structure is necessary. We conclude that the TV-AR model has significant potential for studying changing dynamics in psychology. (PsycINFO Database Recordstatus: publishe