A future agenda for research on climate change and human mobility

In the past 15 years, research activities focusing on the interlinkages between climate change and human mobility have intensified. At the same time, an increasing number of actors and processes have sought to address human mobility in the context of climate change from a policy perspective. Hitherto, research has been limited in terms of geographical preferences as well as conceptual and methodological focus areas. This paper argues that to address the evolving policy space, future research on climate change in the context of human mobility needs to become more differentiated, integrated and generalized. This includes concerted efforts to better integrate researchers from the global South, improved cross-linkages between different datasets, approaches and disciplines, more longitudinal and comparative studies and development of innovative qualitative and quantitative methods.     

Toward an Understanding of Founder's Syndrome: An Assessment of Power and Privilege Among Founders of Nonprofit Organizations

On the subject of stewardship, Block (1996, p. 47) writes, “Stewardship not only affects the use of power, but it also confronts the way we hold privilege.” He also states, “Holding on to privilege is an act of self‐interest, the antithesis of service and stewardship.” As Block uses it, the term privilege denotes a class structure within governance settings, and self‐interest refers to using one's power (through the exercise of privilege) to ascertain an objective that may not be in accord with others'.     

Slice sampling mixture models

We propose a more efficient version of the slice sampler for Dirichlet process mixture models described by Walker (Commun. Stat., Simul. Comput. 36:45–54, 2007). This new sampler allows for the fitting of infinite mixture models with a wide-range of prior specifications. To illustrate this flexibility we consider priors defined through infinite sequences of independent positive random variables. Two applications are considered: density estimation using mixture models and hazard function estimation. In each case we show how the slice efficient sampler can be applied to make inference in the models. In the mixture case, two submodels are studied in detail. The first one assumes that the positive random variables are Gamma distributed and the second assumes that they are inverse-Gaussian distributed. Both priors have two hyperparameters and we consider their effect on the prior distribution of the number of occupied clusters in a sample. Extensive computational comparisons with alternative “conditional” simulation techniques for mixture models using the standard Dirichlet process prior and our new priors are made. The properties of the new priors are illustrated on a density estimation problem.     

The potential for bias in reporting of industry-sponsored clinical trials

Concerns about potentially misleading reporting of pharmaceutical industry research have surfaced many times. The potential for duality (and thereby conflict) of interest is only too clear when you consider the sums of money required for the discovery, development and commercialization of new medicines. As the ability of major, mid-size and small pharmaceutical companies to innovate has waned, as evidenced by the seemingly relentless decline in the numbers of new medicines approved by Food and Drug Administration and European Medicines Agency year-on-year, not only has the cost per new approved medicine risen: so too has the public and media concern about the extent to which the pharmaceutical industry is open and honest about the efficacy, safety and quality of the drugs we manufacture and sell. In 2005 an Editorial in Journal of the American Medical Association made clear that, so great was their concern about misleading reporting of industry-sponsored studies, henceforth no article would be published that was not also guaranteed by independent statistical analysis. We examine the precursors to this Editorial, as well as its immediate and lasting effects for statisticians, for the manner in which statistical analysis is carried out, and for the industry more generally.     

The exchangeability and leachability of metals from select green roof growth substrates

Batch extraction and leaching studies were conducted with potential green roof substrates (e.g., Axis, Arklayte, coal bottom ash, Haydite, Lassenite, lava rock, and composted pine bark). The results indicated that these materials would not likely be sources of Cr, Cu, Fe, Ni, or Zn and that Lassenite would be considered a source of Mn if the leachate concentrations were compared to USEPA drinking water standards for these elements. Lassenite would not be a source of Mn if the data was compared to a USEPA standard for Mn toxicity to aquatic life. All of the substrates tested leached Cd and/or Pb concentrations that exceeded the USEPA water quality standards at least once during the 6-month leaching study, so these materials may be potential sources of Cd and Pb in green roof storm water runoff. The leaching of Cu, Cd, Fe, Mn, Pb, and Zn was differentially influenced by time and/or the presence of a single Sedum hybridum ‘immergrauch’ plant. The leaching of Cd, Cu, and Pb displayed complex, three-way interactions between main effects (substrate type and the presence or absence of a plant) and between leaching events. For all substrates except Lassenite, the presence of a S. hybridum plant decreased the leaching of Pb over time. The leaching of Cd was generally enhanced by plants for most substrates with time. Collectively the results suggest that changes in the biogeochemical conditions within green roof systems may alter metal solubility, decreasing the leaching of some elements and increasing the leaching of others.     

How Large Are Your G-Values? Try Gosset’s Guinnessometrics When a Little “p” Is Not Enough

Abstract

A crisis of validity has emerged from three related crises of science, that is, the crises of statistical significance and complete randomization, of replication, and of reproducibility. Guinnessometrics takes commonplace assumptions and methods of statistical science and stands them on their head, from little p-values to unstructured Big Data. Guinnessometrics focuses instead on the substantive significance which emerges from a small series of independent and economical yet balanced and repeated experiments. Originally developed and market-tested by William S. Gosset aka “Student” in his job as Head Experimental Brewer at the Guinness Brewery in Dublin, Gosset’s economic and common sense approach to statistical inference and scientific method has been unwisely neglected. In many areas of science and life, the 10 principles of Guinnessometrics or G-values outlined here can help. Other things equal, the larger the G-values, the better the science and judgment. By now a colleague, neighbor, or YouTube junkie has probably shown you one of those wacky psychology experiments in a video involving a gorilla, and testing the limits of human cognition. In one video, a person wearing a gorilla suit suddenly appears on the scene among humans, who are themselves engaged in some ordinary, mundane activity such as passing a basketball. The funny thing is, prankster researchers have discovered, when observers are asked to think about the mundane activity (such as by counting the number of observed passes of a basketball), the unexpected gorilla is frequently unseen (for discussion see Kahneman 2011 Kahneman, D. (2011), Thinking Fast and Slow, New York: Farrar, Straus and Giroux. [Google Scholar]). The gorilla is invisible. People don’t see it.     

Therapist Personal Agency: a model for examining the training context

This study reviews the creation and testing of a model of Therapist Personal Agency during MFT training. A model including self-efficacy, trainee developmental level, supervisor working alliance, family of origin relationships, and psychological states was supported by data collected from a national sample of MFT students. The model supported by the data was consistent with much of the previous research regarding the correlates of therapist self-efficacy. Furthermore, the model accounted for 20% of the variance in therapist performance as measured by therapist reports of the working alliance. The results also highlighted the importance of attending to the relationships, interactions, and experiences that take place within the training environment and secondly, the added value that may accrue from focusing on the trainee's extended network of family relationships. A brief case example serves to illustrate the importance of tending to Therapist Personal Agency. In sum, this study provides preliminary support for holistic training methods that focus upon the whole person of the therapist.     

Equalities between OLSE,BLUE and BLUP in the linear model

We consider equalities between the ordinary least squares estimator ( $\mathrm {OLSE} $ ), the best linear unbiased estimator ( $\mathrm {BLUE} $ ) and the best linear unbiased predictor ( $\mathrm {BLUP} $ ) in the general linear model $\{ \mathbf y , \mathbf X \varvec{\beta }, \mathbf V \}$ extended with the new unobservable future value $ \mathbf y _{*}$ of the response whose expectation is $ \mathbf X _{*}\varvec{\beta }$ . Our aim is to provide some new insight and new proofs for the equalities under consideration. We also collect together various expressions, without rank assumptions, for the $\mathrm {BLUP} $ and provide new results giving upper bounds for the Euclidean norm of the difference between the $\mathrm {BLUP} ( \mathbf y _{*})$ and $\mathrm {BLUE} ( \mathbf X _{*}\varvec{\beta })$ and between the $\mathrm {BLUP} ( \mathbf y _{*})$ and $\mathrm {OLSE} ( \mathbf X _{*}\varvec{\beta })$ . A remark is made on the application to small area estimation.