100 key research questions for the post‐2015 development agenda

The Sustainable Development Goals (SDGs) herald a new phase for international development. This article presents the results of a consultative exercise to collaboratively identify 100 research questions of critical importance for the post‐2015 international development agenda. The final shortlist is grouped into nine thematic areas and was selected by 21 representatives of international and non‐governmental organisations and consultancies, and 14 academics with diverse disciplinary expertise from an initial pool of 704 questions submitted by 110 organisations based in 34 countries. The shortlist includes questions addressing long‐standing problems, new challenges and broader issues related to development policies, practices and institutions. Collectively, these questions are relevant for future development‐related research priorities of governmental and non‐governmental organisations worldwide and could act as focal points for transdisciplinary research collaborations.     

All Data are Wrong,but Some are Useful? Advocating the Need for Data Auditing

In a recent article from the Annals of Applied Statistics, Cox discussed the main phases of applied statistical research ranging from clarifying study objectives to final data analysis and interpreting results. As an incidental remark to these main phases, we advocate that beyond cleaning and preprocessing the data, it is a good practice to audit the data to determine if they can be trusted at all. A case study based on Ghanaian Official Fishery Statistics is used to illustrate this need, with Benford's law being the tool used to carrying out the data audit. Supplementary materials for this article are available online.     

Minimal covering set solutions

We study necessary and sufficient conditions for a multi-valued solution S to be rationalized in the following sense: there exists a complete asymmetric relation T (a tournament) such that, for each feasible (finite) set, the solution set of S coincides with the minimal covering set of T restricted to that feasible set. Our characterization result relies only on properties relating S across feasible choice sets.     

The Inability of Self-affirmations to Decrease Defensive Bias Toward an Alcohol-Related Risk Message Among High-Risk College Students

Abstract

Objective: To examine the efficacy of a self-affirmation task in deterring college alcohol misuse and the importance of preexisting beliefs in predicting subsequent behavior change. Participants: Heavy-drinking undergraduates (N = 110) participated during the 2011–2012 academic year. Methods: Participants were randomized to complete an affirmation or control task before reading an alcohol risk message. Alcohol-related beliefs and behaviors were assessed. Participants completed a 2-week online follow-up assessing alcohol-related behaviors. Results: Both groups reported increased perceived problem importance, but neither group displayed changes in personal risk. Follow-up assessment revealed similar, significant declines in peak consumption in both groups, with no significant between-group differences. Preexisting beliefs accounted for 5% to 10% of variance in drinking outcomes. Conclusions: An affirmation task does not seem to decrease defensive processing or alter high-risk drinking behaviors among college students and should not be utilized in lieu of more effective strategies.     

Climate change and population migration in Brazil’s Northeast: scenarios for 2025–2050

This research contributes to an understanding of the relationship between climate change, economic impacts and migration. We model the long-term relationship (up to 45 years of projection) between demographic dynamics—particularly migration—driven by changes in the performance of the economy due to climate changes in the Northeast region of Brazil. The region is of particular relevance to the study of climate change impacts given its large human population (28% of Brazil’s population) and high levels of impoverishment, having an extensive semi-dry area which will be severely impacted by growing temperatures. Ultimately, the integrated model generates state- and municipal-level migration scenarios based on climate change impacts on the primary economic sectors and their articulations with other sectors. Results suggest that the predicted climate changes will impact severely the agriculture sector in the region, acting as a potential migration push factor to other regions in the country. Finally, we discuss how the increased vulnerability of some groups, particularly migrants, can be factored into Brazilian public policy and planning.     

Exploring the relation between maternal mind‐mindedness and children’s symbolic play: A longitudinal study from 6 to 18 months

Relations between mothers’ mind‐mindedness (appropriate attunement to their infants’ internal states) at 6 and 12 months and infants’ early symbolic play during infant–mother pretense at 12 and 18 months were investigated in a sample of 43 mothers and infants. Mothers’ appropriate mind‐related comments were associated with average level, length, complexity, and maturity level of symbolic play. Specific sub‐categories of appropriate mind‐related comments were identified as independent predictors of children's symbolic play. Appropriate comments about desires and cognitions at 6 months were associated with average level and length of episodes, as well as with maturity level of symbolic play at 12 months. Longitudinal stability in the appropriateness and content of mothers’ mind‐related comments was also investigated. The results are discussed in terms of the proposal that attunement to specific types of internal state should vary as a function of infant age in order to index mind‐mindedness.     

Concurrent training protocol for men with androgen deficiency in the aging male: a randomized clinical trial

Background: The androgen deficiency in the aging male (ADAM) affects physical, sexual, and psychological aspects with characteristics symptoms of middle-aged men. The practice of regular physical activity and physical exercise can attenuate these symptoms. The aim of this randomized clinical trial is to propose a physical exercise protocol based on concurrent training for middle-aged men with ADAM.

Method: Randomized clinical trial with a 6-month intervention will randomly divided into two groups: experimental group (EG) and control group (CG). Four evaluations will be carried out, (1) pre-intervention; (2) in the first month of intervention; (3) in the third month of intervention; (4) post-intervention, evaluating: physical, psychological, sexual, and hormonal aspects. The intervention protocol with concurrent training will have duration of 6 months; frequency of 3 times weekly, with 60?min per session. The two-way ANOVA test will be used for the inter-group and intra-group comparisons with repeated measurements, and also Sydak’s comparison test.

Conclusion: This protocol was developed with the intent of easing the symptoms of ADAM. In addition, it is believed that the concurrent training protocol could be capable to recover hormonal, physical, psychological, and sexual aspect of middle-aged men with ADAM.     

Geographically Weighted Negative Binomial Regression—incorporating overdispersion

Global regression assumes that a single model adequately describes all parts of a study region. However, the heterogeneity in the data may be sufficiently strong that relationships between variables can not be spatially constant. In addition, the factors involved are often sufficiently complex that it is difficult to identify them in the form of explanatory variables. As a result Geographically Weighted Regression (GWR) was introduced as a tool for the modeling of non-stationary spatial data. Using kernel functions, the GWR methodology allows the model parameters to vary spatially and produces non-parametric surfaces of their estimates. To model count data with overdispersion, it is more appropriate to use a negative binomial distribution instead of a Poisson distribution. Therefore, we propose the Geographically Weighted Negative Binomial Regression (GWNBR) method for the modeling of data with overdispersion. The results obtained using simulated and real data show the superiority of this method for the modeling of non-stationary count data with overdispersion compared with competing models, such as global regressions, e.g., Poisson and negative binomial and Geographically Weighted Poisson Regression (GWPR). Moreover, we illustrate that these competing models are special cases of the more robust model GWNBR.