Teachers’ Effortful Control and Student Functioning: Mediating and Moderating Processes

Evidence is emerging that teachers’ dispositional characteristics are related to students’ classroom functioning, but processes are not well understood. We examined associations between second‐grade teachers’ effortful control (EC), student‐teacher closeness or conflict, students’ EC, and changes in students’ externalizing behaviors and reading and math achievement. Teachers’ EC was directly related to their students’ externalizing behaviors, but not achievement. Conflict, but not closeness, mediated associations between teachers’ EC and students’ externalizing behaviors. In moderated mediation tests, conflict was positively associated with externalizing behaviors most strongly for students low or moderate in EC. Closeness was positively associated with reading achievement, and negatively associated with math achievement, only for low‐EC students.     

Player transgressions and the management of the sport sponsor relationship

Player transgressions in sport relate to on- or off-field incidents that are out of the ordinary and may bring negative repercussions for stakeholders as a result of their association with the team, athlete or sport. Based on a series of depth interviews conducted with executives representing major Australian team-based sporting organizations, this paper outlines the effects that these incidents can have on relationships with sponsors from a sport organization perspective. The findings serve to develop a conceptual model that illustrates that the impact of transgressions is determined by a number of factors, which are discussed. The paper further presents a number of implications for the sport organization's public relations strategy, given that the role of public relations has evolved from primarily a communications focus to a more strategic management of relationships with various publics and stakeholders.     

On MCMC sampling in hierarchical longitudinal models

Markov chain Monte Carlo (MCMC) algorithms have revolutionized Bayesian practice. In their simplest form (i.e., when parameters are updated one at a time) they are, however, often slow to converge when applied to high-dimensional statistical models. A remedy for this problem is to block the parameters into groups, which are then updated simultaneously using either a Gibbs or Metropolis-Hastings step. In this paper we construct several (partially and fully blocked) MCMC algorithms for minimizing the autocorrelation in MCMC samples arising from important classes of longitudinal data models. We exploit an identity used by Chib (1995) in the context of Bayes factor computation to show how the parameters in a general linear mixed model may be updated in a single block, improving convergence and producing essentially independent draws from the posterior of the parameters of interest. We also investigate the value of blocking in non-Gaussian mixed models, as well as in a class of binary response data longitudinal models. We illustrate the approaches in detail with three real-data examples.     

The Application of Genetic Information for Regulatory Standard Setting Under the Clean Air Act: A Decision-Analytic Approach

In 2002, the U.S. Environmental Protection Agency (EPA) released an “Interim Policy on Genomics,” stating a commitment to developing guidance on the inclusion of genetic information in regulatory decision making. This statement was followed in 2004 by a document exploring the potential implications. Genetic information can play a key role in understanding and quantifying human susceptibility, an essential step in many of the risk assessments used to shape policy. For example, the federal Clean Air Act (CAA) requires EPA to set National Ambient Air Quality Standards (NAAQS) for criteria pollutants at levels to protect even sensitive populations from adverse health effects with an adequate margin of safety. Asthmatics are generally regarded as a sensitive population, yet substantial research gaps in understanding genetic susceptibility and disease have hindered quantitative risk analysis. This case study assesses the potential role of genomic information regarding susceptible populations in the NAAQS process for fine particulate matter (PM_2.5) under the CAA. In this initial assessment, we model the contribution of a single polymorphism to asthma risk and mortality risk; however, multiple polymorphisms and interactions (gene‐gene and gene‐environment) are known to play key roles in the disease process. We show that the impact of new information about susceptibility on estimates of population risk or average risk derived from large epidemiological studies depends on the circumstances. We also suggest that analysis of a single polymorphism, or other risk factor such as health status, may or may not change estimates of individual risk enough to alter a particular regulatory decision, but this depends on specific characteristics of the decision and risk information. We also show how new information about susceptibility in the context of the NAAQS for PM_2.5 could have a large impact on the estimated distribution of individual risk. This would occur if a group were consequently identified (based on genetic and/or disease status), that accounted for a disproportionate share of observed effects. Our results highlight certain conditions under which genetic information is likely to have an impact on risk estimates and the balance of costs and benefits within groups, and highlight critical research needs. As future studies explore more fully the relationship between exposure, genetic makeup, and disease status, the opportunity for genetic information and disease status to play pivotal roles in regulation can only increase.     

Statistical Modelling of Brain Morphological Measures Within Family Pedigrees

Large, family-based imaging studies can provide a better understanding of the interactions of environmental and genetic influences on brain structure and function. The interpretation of imaging data from large family studies, however, has been hindered by the paucity of well-developed statistical tools for that permit the analysis of complex imaging data together with behavioral and clinical data. In this paper, we propose to use two methods for these analyses. First, a variance components model along with score statistics is used to test linear hypotheses of unknown parameters, such as the associations of brain measures (e.g., cortical and subcortical surfaces) with their potential genetic determinants. Second, we develop a test procedure based on a resampling method to assess simultaneously the statistical significance of linear hypotheses across the entire brain. The value of these methods lies in their computational simplicity and in their applicability to a wide range of imaging data. Simulation studies show that our test procedure can accurately control the family-wise error rate. We apply our methods to the detection of statistical significance of gender-by-age interactions and of the effects of genetic variation on the thickness of the cerebral cortex in a family study of major depressive disorder.     

An analysis of covariance markov model for interrupted time series experiments

A simple Markov model is described for the analysis of interrupted time series experiments that employ a control series. The procedure involves regressing the experimental preintervention series on the control preintervention series and then testing for change in the experimental postintervention series which is free of change in the control series.     

Media depth influences Sedum green roof establishment

Species selection and initial establishment of plants is critical for long term survival and health of green roofs. Plants that can withstand harsh environmental conditions and provide rapid coverage on extensive green roofs can reduce erosion, limit weed invasion, and provide a more aesthetically pleasing roof to satisfy customers. This study evaluated the effect of green roof substrate depth on initial establishment of 12 Sedum species in a Midwestern US climate. Plugs of 12 Sedum species were planted on 8 June 2005 and evaluated bi-weekly until first frost for absolute cover (AC) using a stainless steel point-frame transect. Most species exhibited greater growth and coverage at a depth of 7.0 and 10.0 cm relative to 4.0 cm. AC was highest for Sedum sarmentosum at all depths, but this species may be too aggressive. Other suitable species include Sedum floriferum, Sedum stefco, and Sedum spurium ‘John Creech’. In general, species that are less suitable are Sedum ‘Angelina’, Sedum cauticola ‘Lidakense’, Sedum ewersii, Sedum ochroleucum, and Sedum reflexum ‘Blue Spruce’. For the species tested, a minimum of 7.0 cm is highly recommended. With shallower substrates, S. sarmentosum and S. stefco will provide the fastest coverage. This paper is a portion of a M.S. Thesis submitted by K.L. Getter.     

Some adjusted orthogonal,variance balanced,multidimensional block designs

A multidimensional block design (MBD) is an experimental design with d > 1 blocking criteria geometrically represented as a d-dimensional lattice with treatment varieties assigned to some or all nodes of the lattice. Intrablock analysis of variance tables for some special classes of two- and three-dimensional block designs with some empty nodes are given. Design plans and efficiencies for 31 two-dimensional designs, each universally optimal in defined classes of designs, and 7 three-dimensional designs, each nearly optimal in defined classes of designs, are listed in the appendices. A need for such designs is apparent when the blocking criteria are implemented successively and empty nodes do not represent wasted experimental units.     

Examining Mental Health Court Completion: A Focal Concerns Perspective

Sociologists have long‐raised concern about disparate treatment in the justice system. Focal concerns have become the dominant perspective in explaining these disparities in legal processing decisions. Despite the growth of problem‐solving courts, little research has examined how this perspective operates in nontraditional court settings. This article used a mixed‐method approach to examine focal concerns in a mental health court (MHC). Observational findings indicate that gender and length of time in court influence the court's contextualization of noncompliance. While discussions of race were absent in observational data, competing‐risk survival analysis finds that gender and race interact to predict MHC termination.