The Positive Effects of Easy Goals on Decision Quality and Risk Propensity in an MCPLP Task

In this study the potential performance benefits of easy goals were examined within the multiple cue probability learning paradigm (MCPLP). Specifically, the effects of varying levels of goal difficulty on performance and risk propensity (used to define a form of commitment) were investigated. With few exceptions, previous studies demonstrated support for difficult goals. In this study, contrary to the majority of past evidence, as goals became easier decision quality significantly improved. Moreover, risk propensity increased with easier goals and, as suggested by a post-hoc analysis, had more direct impact on decision makers’ behaviors than goal levels. Goal-related behavior in complex MCPLP tasks appears to significantly differ from the majority of goal evidence in other types of research due to the tendency for subjects to view complex task properties in the same context as goal levels.     

Pediatric Chronic Illness Management: A Qualitative Dyadic Analysis of Adolescent Patient and Parent Illness Narratives

In pediatric chronic illness, little is known about the relational interactions between adolescent patients, parents, and illnesses and how they influence self-management of illness. We conducted interviews with 32 individuals (16 dyads) representing adolescents diagnosed with a chronic illness and their primary parent who had been referred to a psychosocial treatment program for challenges with illness management. Interviews were conducted individually and analyzed dyadically using grounded theory to better understand the relational processes that may be contributing to illness management difficulties. Results include a theory of patient–parent illness responses and how parental illness meanings play a role in adolescent self-management. Results can be used to better understand and treat family relational patterns that may be influencing pediatric illness management challenges.     

Urban vegetation cover correlates with environmental variables in a desert city: insights of mitigation measures to climate change

Urban Ecosystems - Green cover and air quality are important components of life quality and human ecology in arid lands. In the Sonoran Desert of Mexico, Hermosillo is the largest city with a...     

The Persistence of High Levels of Living Alone Among Adults with Disabilities in Sweden, 1993–2011

Population Research and Policy Review - This study investigates how the probability to live alone has developed among working age individuals with and without disabilities in Sweden during the...     

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.     

On Hotelling’s T2 test in a special paired sample case

In a special paired sample case, Hotelling’s T² test based on the differences of the paired random vectors is the likelihood ratio test for testing the hypothesis that the paired random vectors have the same mean; with respect to a special group of affine linear transformations it is the uniformly most powerful invariant test for the general alternative of a difference in mean. We present an elementary straightforward proof of this result. The likelihood ratio test for testing the hypothesis that the covariance structure is of the assumed special form is derived and discussed. Applications to real data are given.