A Comparison of Some Methods for the Selection of a Common Eigenvector Model for the Covariance Matrices of Two Groups

Two new nonparametric common principal component model selection procedures based on bootstrap distributions of the vector correlations of all combinations of the eigenvectors from two groups are proposed. The performance of these methods is compared in a simulation study to the two parametric methods previously suggested by Flury in 1988, as well as modified versions of two nonparametric methods proposed by Klingenberg in 1996 and then by Klingenberg and McIntyre in 1998. The proposed bootstrap vector correlation distribution (BVD) method is shown to outperform all of the existing methods in most of the simulated situations considered.     

Some maximum-indifference estimators for the slope of a univariate linear model

As known, the least-squares estimator of the slope of a univariate linear model sets to zero the covariance between the regression residuals and the values of the explanatory variable. To prevent the estimation process from being influenced by outliers, which can be theoretically modelled by a heavy-tailed distribution for the error term, one can substitute covariance with some robust measures of association, for example Kendall's tau in the popular Theil–Sen estimator. In a scarcely known Italian paper, Cifarelli [(1978), ‘La Stima del Coefficiente di Regressione Mediante l'Indice di Cograduazione di Gini’, Rivista di matematica per le scienze economiche e sociali, 1, 7–38. A translation into English is available at http://arxiv.org/abs/1411.4809 and will appear in Decisions in Economics and Finance] shows that a gain of efficiency can be obtained by using Gini's cograduation index instead of Kendall's tau. This paper introduces a new estimator, derived from another association measure recently proposed. Such a measure is strongly related to Gini's cograduation index, as they are both built to vanish in the general framework of indifference. The newly proposed estimator is shown to be unbiased and asymptotically normally distributed. Moreover, all considered estimators are compared via their asymptotic relative efficiency and a small simulation study. Finally, some indications about the performance of the considered estimators in the presence of contaminated normal data are provided.     

Predictive Inference for Big,Spatial, Non‐Gaussian Data: MODIS Cloud Data and its Change‐of‐Support

Remote sensing of the earth with satellites yields datasets that can be massive in size, nonstationary in space, and non‐Gaussian in distribution. To overcome computational challenges, we use the reduced‐rank spatial random effects (SRE) model in a statistical analysis of cloud‐mask data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on board NASA's Terra satellite. Parameterisations of cloud processes are the biggest source of uncertainty and sensitivity in different climate models’ future projections of Earth's climate. An accurate quantification of the spatial distribution of clouds, as well as a rigorously estimated pixel‐scale clear‐sky‐probability process, is needed to establish reliable estimates of cloud‐distributional changes and trends caused by climate change. Here we give a hierarchical spatial‐statistical modelling approach for a very large spatial dataset of 2.75 million pixels, corresponding to a granule of MODIS cloud‐mask data, and we use spatial change‐of‐Support relationships to estimate cloud fraction at coarser resolutions. Our model is non‐Gaussian; it postulates a hidden process for the clear‐sky probability that makes use of the SRE model, EM‐estimation, and optimal (empirical Bayes) spatial prediction of the clear‐sky‐probability process. Measures of prediction uncertainty are also given.     

Cell Phone Survey Feasibility in The U.S.: Sampling and Calling Cell Numbers Versus Landline Numbers

In 2004, we conducted a nationwide dual frame survey of landlineand cell phone numbers to evaluate the feasibility of includingcell phone numbers in a random digit dial telephone survey.Households with both landline and cell phones were eligiblefor selection in both samples. This article describes our designand data collection methods; compares the results from the twosamples (with an emphasis on operational characteristics); andpresents the outcomes of two experimental manipulations designedto improve the cell phone response rate.     

Protective and risk influences of drug use among a multiethnic sample of adolescent boys

A measurement model was analyzed to demonstrate a differential distribution and cumulative exposure to psychosocial risk and protective influences of adolescent drug use among ethnically-diverse adolescent samples. The sample included U.S.-born (US) Latino (N = 837), foreign-born (FB) Latino (N = 447), White (N = 632), and African American (N = 618) adolescent boys. Psychological Distress, Peer Drug Use, and Adolescent Drug Use were significantly positively correlated with Peer Drug Use and Adolescent Drug Use. In contrast, Family, School, law abidance, and guilt were significantly negatively correlated with Peer Drug Use and Adolescent Drug Use. Multiple group analyses indicated significant similarities and differences among US and FB Latinos, and Whites. Implications for early prevention, multi-level interventions, and suggestions for future research are discussed.     

Where local and national evaluators meet: unintended threats to ethical evaluation practice

The ethical work of program evaluators is based on a covenant of honesty and transparency among stakeholders. Yet even under the most favorable evaluation conditions, threats to ethical standards exist and muddle that covenant. Unfortunately, ethical issues associated with different evaluation structures and contracting arrangements have received little attention in the evaluation research literature. This article focuses on the unintended ethical threats associated with multitiered evaluation contexts. After briefly reviewing the various frames through which evaluation theory and ethics are commonly viewed, we discuss ethical challenges associated with multitiered evaluation designs including examples drawn from our evaluation projects. The article concludes with specific findings and recommendations for evaluators, grantors, grantees, and researchers.     

Injunctive Norms and Problem Gambling among College Students

Two studies examined the relationships among injunctive norms and college student gambling. In study 1 we evaluated the accuracy of perceptions of other students’ approval of gambling and the relationship between perceived approval and gambling behavior. In study 2 we evaluated gambling behavior as a function of perceptions of approval of other students, friends, and family. In study 1, which included 2524 college students, perceptions of other students’ approval of gambling were found to be overestimated and were negatively associated with gambling behavior. The results of study 2, which included 565 college students, replicated the findings of study 1 and revealed positive associations between gambling behavior and perceived approval of friends and family. Results highlight the complexity of injunctive norms and the importance of considering the reference group (e.g., peers, friends, family members) in their evaluation. Results also encourage caution in considering the incorporation of injunctive norms in prevention and intervention approaches.     

College and university compliance with a required meningococcal vaccination law

OBJECTIVE: Maryland became the first state to pass a vaccination law requiring college and university students living on campus to obtain a meningococcal vaccination or to sign a waiver refusing vaccination because college students are at increased risk for disease. The authors sought to identify how Maryland colleges addressed the law and determine whether schools were in full compliance. PARTICIPANTS: The authors surveyed 32 college/university administrators via a self-administered questionnaire. METHODS: The authors calculated vaccination and waiver rates and assessed compliance with the law overall and with specific law components. RESULTS: Among 28 participating schools, annual vaccination rates and waiver rates among students during 2000-2004 ranged from 66%-76% and 12%-17%, respectively. Two (7%) schools were compliant with all components of the law. CONCLUSIONS: Mandatory vaccination laws do not ensure compliance at the college and university level. Mandatory reporting, increased education, and collaboration between colleges and universities and public health agencies are needed.