Relations Between Parenting Behavior and SES in a Clinical Sample: Validity of SES Measures

Two common methods of measuring socioeconomic status (SES) were examined in relation to observed parenting behaviors in a clinical sample of 89 mothers of 3- to 6-year-olds referred for treatment of oppositional defiant disorder. Families were 74% Caucasian, 9% African American, 5% Hispanic, 1% Asian, and 11% Biracial. Most children were male (75%). Parenting behaviors were measured with the Dyadic Parent–Child Interaction Coding System (DPICS) categories of prosocial talk and negative talk. Analyses were conducted with SES measured in two ways: (a) the Hollingshead Four-Factor Index of Social Status (HI) was correlated with parenting behaviors; and (b) family income, parent education (5 categories), and parent occupation (5 categories) were used to predict parent behavior. SES was significantly positively related to maternal prosocial talk. When SES was operationalized as income, occupation, and education, the model predicted three times more variance in maternal prosocial talk than the HI alone. SES was not related to maternal negative talk by either measure of SES.     

Combining Instructional Activities for Sense-Making Processes and Perceptual-Induction Processes Involved in Connection-Making Among Multiple Visual Representations

Abstract

Connection-making among multiple representations is a crucial but difficult competence in STEM learning. Prior research has focused on one type of learning process involved in connection-making: sense-making processes leading to conceptual understanding of connections. Yet, other research suggests that a second type of learning process is important: inductive learning processes leading to perceptual intuitions about connections. We investigate whether combining instructional activities designed to support sense-making processes for understanding of connections (understanding activities) and instructional activities that support inductive processes for perceptual intuitions about connections (perception activities) enhances students’ learning of chemistry knowledge. A laboratory-based experiment with 117 undergraduate students compared students in (a) a control condition that received only conventional activities that did not require connection-making; (b) a condition that received conventional and understanding-activities; (c) a condition that received conventional and perception-activities; and (d) a combined condition that received conventional, understanding-activities, and perception-activities. Results show that only the combined condition outperformed the control condition on a test of chemistry knowledge. Eye-gaze data and verbal reports show that understanding-activities and perception-activities have complementary effects on how students integrate information from multiple representations during the learning phase. Finally, we found that students’ spatial skills moderate their benefit from understanding-activities and perception-activities.     

A Computationally Efficient State Space Approach to Estimating Multilevel Regression Models and Multilevel Confirmatory Factor Models

Although the state space approach for estimating multilevel regression models has been well established for decades in the time series literature, it does not receive much attention from educational and psychological researchers. In this article, we (a) introduce the state space approach for estimating multilevel regression models and (b) extend the state space approach for estimating multilevel factor models. A brief outline of the state space formulation is provided and then state space forms for univariate and multivariate multilevel regression models, and a multilevel confirmatory factor model, are illustrated. The utility of the state space approach is demonstrated with either a simulated or real example for each multilevel model. It is concluded that the results from the state space approach are essentially identical to those from specialized multilevel regression modeling and structural equation modeling software. More importantly, the state space approach offers researchers a computationally more efficient alternative to fit multilevel regression models with a large number of Level 1 units within each Level 2 unit or a large number of observations on each subject in a longitudinal study.     

Sensitivity of Fit Indices to Misspecification in Growth Curve Models

This study investigated the sensitivity of fit indices to model misspecification in within-individual covariance structure, between-individual covariance structure, and marginal mean structure in growth curve models. Five commonly used fit indices were examined, including the likelihood ratio test statistic, root mean square error of approximation, standardized root mean square residual, comparative fit index, and Tucker-Lewis Index. The fit indices were found to have differential sensitivity to different types of misspecification in either the mean or covariance structures with severity of misspecification controlled. No fit index was always more (or less) sensitive to misspecification in the marginal mean structure relative to those in the covariance structure. Specifying the covariance structure to be saturated can substantially improve the sensitivity of fit indices to misspecification in the marginal mean structure; this result might help identify the sources of specification error in a growth curve model. An empirical example of children's growth in math achievement (Wu, West, &; Hughes, 2008 Wu, W., West, S. G. and Hughes, J. N. 2008. Effect of retention in first grade on children's achievement trajectories over four years: A piecewise growth analysis using propensity score matching.. Journal of Educational Psychology, 100: 727–740. [Crossref], [PubMed], [Web of Science ®] , [Google Scholar]) was used to illustrate the results.     

High-pressure phase transition and lattice dynamics of rock-salt and FeSi-type phases of MgS

A phase transition of MgS under high pressure is investigated using a first-principles method. It is found from energy-volume calculations that the rock-salt (B1) phase of MgS transforms into a FeSi-type (B28) phase at 143?GPa. The calculated ground-state parameters in the B1 phase are in excellent agreement with available experimental and theoretical data. Ab initio phonon calculations are also performed to investigate the structural behaviour of MgS under high pressure. An unstable transverse acoustic mode and a phase transition from B1 to B28 phase at ~143.7?GPa driven by this soft mode are predicted. The B28 structure of MgS is stable up to 350?GPa according to lattice dynamics calculations.     

Determination of the depth distribution of subsurface damage during polishing of alumina

High-resolution grazing-incidence parallel-beam powder diffraction has been used to detect the subsurface damage below alumina surfaces subjected to polishing with cerium oxide or diamond. Despite very significant evolution of the surface morphology, no changes in the subsurface strains were observed over a 20min period of polishing with ceria. For both polishing materials, the variation with angle in the full-width at half-height maximum was successfully modelled by a strain distribution that fell exponentially with increasing depth. Although the surface amplitude and depth dependence parameter are coupled, we have been able to place upper limits on the depth to which the damage extends. Under realistic assumptions, the depth of damage induced by 1µm diamond paste is comparable with that from 3µm ceria polish.     

The orientation and temperature dependences of dislocation velocity in Ni 3 Al single crystals

The average velocities of screw dislocations in Ni 3 Al single crystals have been directly measured as a function of resolved shear stress (RSS) and orientation in the temperature domain of the flow stress anomaly using the etch-pit technique. The velocity was found to be extremely sensitive to the RSS in all cases. In contrast with ordinary metals, the screw dislocation velocities in Ni 3 Al show anomalous behaviour; under a constant RSS, the velocities decrease dramatically with increasing temperature. Furthermore, the velocities and the tension-compression asymmetry of the velocities depend on the orientation of applied stress.     

Prevalence and Predictors of Posttraumatic Growth in Accidentally Injured Patients

This study examined prevalence and predictors of posttraumatic growth in 180 accidentally injured patients of mainland China in their convalescence stage, investigating its relationships with demographic and accidental injury variables, personality, posttraumatic stress disorder (PTSD) symptoms, and coping styles. Our results showed that posttraumatic growth (PTG) presented mostly in the domain of Relating to Others and indicated that PTG was significantly related to marital status, educational level, personality, coping styles, and PTSD symptoms. Avoidance of PTSD symptoms, Openness to experience, and positive coping were significant predictors of PTG. The findings emphasize that when promoting PTG of accidentally injured patients, healthcare providers should facilitate patients utilizing personal resources, understand PTG coexists with PTSD symptoms, and adjust interventions based on the coping styles the patients have adopted.     

Prologue: Introduction and Overview to Special Volume

Abstract

The editors provide an overview of the obstacles that prevent us from ending child abuse in the United States and briefly summarize the various articles in this volume that address these obstacles from multiple points of view.     

Mind-reading without the scanner: Behavioural decoding of working memory content

ABSTRACT

Sophisticated machine learning algorithms have been successfully applied to functional neuroimaging data in order to characterize internal cognitive states. But is it possible to “mind-read” without the scanner? Capitalizing on the robust finding that the contents of working memory guide visual attention toward memory-matching objects, we trained a multivariate pattern classifier on behavioural indices of attentional guidance. Working memory representations were successfully decoded from behaviour alone, both within and between individuals. The current study provides a proof-of-concept for applying machine learning techniques to simple behavioural outputs (e.g., response times) in order to decode information about specific internal cognitive states.