The balance of planting and mortality in a street tree population

Street trees have aesthetic, environmental, human health, and economic benefits in urban ecosystems. Street tree populations are constructed by cycles of planting, growth, death, removal and replacement. The goals of this study were to understand how tree mortality and planting rates affect net population growth, evaluate the shape of the mortality curve, and assess selected risk factors for survival. We monitored a street tree population in West Oakland, CA for 5 years after an initial inventory (2006). We adapted the classic demographic balancing equation to quantify annual inputs and outputs to the system, tracking pools of live and standing dead trees. There was a 17.2 % net increase in live tree counts during the study period (995 in 2006, 1166 in 2011), with population growth observed each year. Of the live trees in 2006, 822 survived to 2011, for an annual mortality rate of 3.7 %. However, population growth was constrained by high mortality of young/small trees. Annual mortality was highest for small trees, and lower for mid-size and large trees; this represents a Type III mortality curve. We used multivariate logistic regression to evaluate the relationship between 2011 survival outcomes and inventory data from 2006. In the final model, significant associations were found for size class, foliage condition, planting location, and a multiplicative interaction term for size and foliage condition. Street tree populations are complex cultivated systems whose dynamics can be understood by a combination of longitudinal data and demographic analysis. Urban forest monitoring is important to understand the impact of tree planting programs.     

The influence of perception of school principal’s leadership on teachers’ job satisfaction in the interior of Sabah,Malaysia: a case study in Beaufort

This study investigates the relationship between teachers’ perceptions of the school principal’s leadership and their job satisfaction in Beaufort, a multiethnic semirural area in the interior of Sabah, Malaysia. Results of the research showed that there is a significant positive relationship between the perceived leadership of the school principal and job satisfaction among the teaching staff, and the relationship is not influenced by the demographic attributes of the study participants. The results accentuate the importance of developing the leadership of principals in public schools to enhance the commitment and job satisfaction of the teaching staff, which indirectly affects the overall performance of schools and students.     

Stuck inside the federal-Indian funding relationship: a tale of two evaluations

Two case studies are presented to compare and contrast the challenges encountered when attempting to conduct participatory evaluations (P-Es) with tribal programs that represented two extremes of collaboration between the programs and evaluators. In one case, the P-E was successful because the principals were invested in the program, whereas in the second case, the absence of a shared program vision hampered the ability to adequately evaluate the program. The authors contend that governmental funding agencies have responsibilities to ensure that their expectations are appropriate, the proposed program is feasible, and that technical assistance should be provided before the grant is written, not after it has been funded.     

Human resource development,organization development,organizational learning,and organizational effectiveness: all needed more than ever

In this study the Job Demand-Control model was used to study the quality of working life of Dutch secondary teachers. The Job Demand-Control model of Karasek is a theoretical model in which stress and learning are both considered as dependent variables which are influenced by three different task characteristics: job demands, job control, and social support. This model was tested for Dutch secondary teachers (n = 542). Results shed light on the relationship between stress and learning, on the one hand, and the effects of task characteristics on work stress and on work-based learning, on the other hand. It is concluded that the relationship between stress and learning is mediated by the amount of job control as the model predicts. However, the results also reveal that the Karasek model is better suited for explaining stress than for explaining learning. To explain work-based learning variables other than task characteristics have to be taken into account.     

Influence of a Statewide Marriage Initiative on Clergy Involvement in Marriage Preparation

Although government-funded community healthy marriage initiatives (CHMIs) often focus on clergy-provided premarital education, little is known about the influence of a statewide initiative on clergy attitudes, perceptions, and behaviors. The purpose of this study was to compare Oklahoma clergy over time and Oklahoma clergy with clergy from other states. The findings suggest that the Oklahoma Marriage Initiative (OMI), one of the earliest and largest CHMIs, has influenced clergy attitudes and perceived effectiveness but has had a limited effect on clergy behaviors. In addition, the findings suggest that Oklahoma clergy did not significantly differ from other clergy in the United States regarding premarital education attitudes and behaviors despite the efforts of the OMI. Based on these findings, policymakers should reevaluate approaches to clergy involvement in CHMIs.     

The neglog transformation and quantile regression for the analysis of a large credit scoring database

Summary.  A statistical analysis of a bank's credit card database is presented. The database is a snapshot of accounts whose holders have missed a payment on a given month but who do not subsequently default. The variables on which there is information are observable measures on the account (such as profit and activity), and whether actions that are available to the bank (such as letters and telephone calls) have been taken. A primary objective for the bank is to gain insight into the effect that collections activity has on on-going account usage. A neglog transformation that highlights features that are hidden on the original scale and improves the joint distribution of the covariates is introduced. Quantile regression, a novel methodology to the credit scoring industry, is used as it is relatively assumption free, and it is suspected that different relationships may be manifest in different parts of the response distribution. The large size is handled by selecting relatively small subsamples for training and then building empirical distributions from repeated samples for validation. In the application to the database of clients who have missed a single payment a substantive finding is that the predictor of the median of the target variable contains different variables from those of the predictor of the 30% quantile. This suggests that different mechanisms may be at play in different parts of the distribution.     

Efficiency of generalized estimating equations for binary responses

Summary.  Using standard correlation bounds, we show that in generalized estimation equations (GEEs) the so-called 'working correlation matrix' R ( α ) for analysing binary data cannot in general be the true correlation matrix of the data. Methods for estimating the correlation param-eter in current GEE software for binary responses disregard these bounds. To show that the GEE applied on binary data has high efficiency, we use a multivariate binary model so that the covariance matrix from estimating equation theory can be compared with the inverse Fisher information matrix. But R ( α ) should be viewed as the weight matrix, and it should not be confused with the correlation matrix of the binary responses. We also do a comparison with more general weighted estimating equations by using a matrix Cauchy–Schwarz inequality. Our analysis leads to simple rules for the choice of α in an exchangeable or autoregressive AR(1) weight matrix R ( α ), based on the strength of dependence between the binary variables. An example is given to illustrate the assessment of dependence and choice of α .     

Composite likelihood estimation in multivariate data analysis

The authors propose two composite likelihood estimation procedures for multivariate models with regression/univariate and dependence parameters. One is a two‐stage method based on both univariate and bivariate margins. The other estimates all the parameters simultaneously based on bivariate margins. For some special cases, the authors compare their asymptotic efficiencies with the maximum likelihood method. The performance of the two methods is reasonable, except that the first procedure is inefficient for the regression parameters under strong dependence. The second approach is generally better for the regression parameters, but less efficient for the dependence parameters under weak dependence.     

Maximizing potential: innovative collaborative strategies between one-stops and mental health systems of care

Barriers to seamless service delivery between workforce development and mental health systems of care have kept both entities from maximizing their potential in regards to employment for job seekers with mental illness who are capable of work and seeking employment. Using a multiple case study design, this study examined the nature of collaboration between workforce development and mental health systems to understand the policies and practices in place to assist individuals with mental illness to find and keep work. The paper presents innovative strategies that involved staff from both workforce development and mental health agencies. Findings from this research identified the following collaborative strategies: (a) the creation of liaison positions and collaborative teams; (b) staff training on mental health and workforce issues; and (c) multi-level involvement of individuals with mental illness. Implications for workforce professionals are offered as a way to stimulate implementation of such strategies.