Reflecting on the Past: A Content Analysis of Family Therapy Research from 2000‐2015

In this content analysis, researchers examine articles published from 2000 to 2015 in three family therapy journals, yielding a total of 948 empirical articles. The purpose is to provide an overview of the research being published, assess who is publishing, and investigate the current state of clinical effectiveness research in marriage and family therapy (MFT). Most first authors were affiliated with MFT programs and primarily included diversity and couples in their research. There was a significant increase of research on clinical process—though the number of clinical outcome studies held steady. There were no significant changes with regard to research funding. Implications support the use of innovative research methods to provide evidence of clinical effectiveness.     

Three Recommendations for Improving the Use of p-Values

ABSTRACT

Researchers commonly use p-values to answer the question: How strongly does the evidence favor the alternative hypothesis relative to the null hypothesis? p-Values themselves do not directly answer this question and are often misinterpreted in ways that lead to overstating the evidence against the null hypothesis. Even in the “post p?<?0.05 era,” however, it is quite possible that p-values will continue to be widely reported and used to assess the strength of evidence (if for no other reason than the widespread availability and use of statistical software that routinely produces p-values and thereby implicitly advocates for their use). If so, the potential for misinterpretation will persist. In this article, we recommend three practices that would help researchers more accurately interpret p-values. Each of the three recommended practices involves interpreting p-values in light of their corresponding “Bayes factor bound,” which is the largest odds in favor of the alternative hypothesis relative to the null hypothesis that is consistent with the observed data. The Bayes factor bound generally indicates that a given p-value provides weaker evidence against the null hypothesis than typically assumed. We therefore believe that our recommendations can guard against some of the most harmful p-value misinterpretations. In research communities that are deeply attached to reliance on “p?<?0.05,” our recommendations will serve as initial steps away from this attachment. We emphasize that our recommendations are intended merely as initial, temporary steps and that many further steps will need to be taken to reach the ultimate destination: a holistic interpretation of statistical evidence that fully conforms to the principles laid out in the ASA statement on statistical significance and p-values.     

Kernel density estimation under negative superadditive dependence and its application for real data

In this paper, the kernel density estimator for negatively superadditive dependent random variables is studied. The exponential inequalities and the exponential rate for the kernel estimator of density function with a uniform version, over compact sets are investigated. Also, the optimal bandwidth rate of the estimator is obtained using mean integrated squared error. The results are generalized and used to improve the ones obtained for the case of associated sequences. As an application, FGM sequences that fulfil our assumptions are investigated. Also, the convergence rate of the kernel density estimator is illustrated via a simulation study. Moreover, a real data analysis is presented.     

Accurate computation of single and product moments of order statistics under progressive censoring

An accurate procedure is proposed to calculate approximate moments of progressive order statistics in the context of statistical inference for lifetime models. The study analyses the performance of power series expansion to approximate the moments for location and scale distributions with high precision and smaller deviations with respect to the exact values. A comparative analysis between exact and approximate methods is shown using some tables and figures. The different approximations are applied in two situations. First, we consider the problem of computing the large sample variance–covariance matrix of maximum likelihood estimators. We also use the approximations to obtain progressively censored sampling plans for log-normal distributed data. These problems illustrate that the presented procedure is highly useful to compute the moments with precision for numerous censoring patterns and, in many cases, is the only valid method because the exact calculation may not be applicable.     

Fostering social capital in an international multi-stakeholder issue network

Scholars have theorized that public relations contributes to societies and communities by bringing attention to pressing public issues and fostering social capital in civil society networks. However, the extant research has studied civil society networks of NGOs, donors, and the media in transitional countries. This study extends the public relations model of civil society in two ways. First, it broadens the scope to an international context. Second, it draws from the multi-stakeholder issue network perspective to conceptualize a civil society network as a space where stakeholders of an issue mix their interests as they collectively address a pressing public issue. The literature on international and multi-stakeholder networks suggest that the international scope and the mixing of interests across sectors may restrict the production of social capital. The results from the social network analysis suggests that the mixing of interests across sectoral and geopolitical boundaries did not restrict the production of social capital. Rather, the patterns of the relationships among those on the core and those on the periphery of the network restricted the production of social capital. Such finding demonstrates how public relations’ functions like relationship building can have profound influences on social capital and civil society networks. The implications for public relations theorizing and research are discussed.     

Patterns of alcohol use and marijuana use among students at 2- and 4-year institutions

Objective: The objective of this study was to understand substance use patterns of alcohol, marijuana, and simultaneous alcohol and marijuana (SAM) use among 2- and 4-year college students. Participants: Participants were 526 young adults aged 18–23 (n?=?355 4-year students; n?=?171 2-year students) recruited from February 2015 to January 2016 who were participating in a larger longitudinal study. Methods: Latent class analysis (LCA) was used to identify past-month classes of alcohol, marijuana, and SAM use. Results: Among both 2- and 4-year students, a four-class solution yielded the best-fitting model, with 2-year classes tending to include greater marijuana use and less alcohol use and 4-year classes tending to include heavy alcohol use. Demographic characteristics were largely similar across classes. Conclusions: Classes of alcohol, marijuana, and SAM use differed by education status. Screening and prevention efforts for 4-year students may need to be tailored for the needs of 2-year students.     

A new aging concept derived from the increasing convex ordering

In this paper, a comparison between the life distribution of a new unit with that of a used unit in the increasing convex order is made leading to a new class of life distributions which we call “new better than used in convex ordering of second order”. This class includes as subclasses the NBU and the NBUC and is a subclass of the NBUCA class. Preservation properties under convolution, random maxima, mixing and formation of coherent structures are established. Stochastic comparisons of the excess lifetime when the inter-arrival times belong to the NBUC(2) class are developed. Some applications of Poisson shock models and a test of exponentiality against NBUC(2) alternative are presented.     

A reflected feature space for CART

We present an algorithm for learning oblique decision trees, called HHCART(G). Our decision tree combines learning concepts from two classification trees, HHCART and Geometric Decision Tree (GDT). HHCART(G) is a simplified HHCART algorithm that uses linear structure in the training examples, captured by a modified GDT angle bisector, to define splitting directions. At each node, we reflect the training examples with respect to the modified angle bisector to align this linear structure with the coordinate axes. Searching axis parallel splits in this reflected feature space provides an efficient and effective way of finding oblique splits in the original feature space. Our method is much simpler than HHCART because it only considers one reflected feature space for node splitting. HHCART considers multiple reflected feature spaces for node splitting making it more computationally intensive to build. Experimental results show that HHCART(G) is an effective classifier, producing compact trees with similar or better results than several other decision trees, including GDT and HHCART trees.     

Special issue dedicated to Odd O. Aalen

Lifetime Data Analysis -     

WILL GIRLS BE GIRLS? RISK TAKING AND COMPETITION IN AN ALL‐GIRLS' SCHOOL

We conduct an experiment that examines the relationship between girls only schooling and risk taking and competitive behavior. In it, we compare decisions made by students in an all‐girls' school to those made by students in a closely matched coeducational school. We further investigate the developmental nature of this behavior by comparing choices made by younger students (Grades 7 and 8) with those of older students (Grades 11 and 12). We focus on the differences between those who select into the all‐girls' school, and find that although girls educated in a single sex environment are the most risk averse, they are also among the most competitive. These results lend texture to the hypothesis that “nurture matters” in the gender differences debate. (JEL C93, J16, I21)