Relative effect sizes for measures of risk

Effect sizes are an important component of experimental design, data analysis, and interpretation of statistical results. In some situations, an effect size of clinical or practical importance may be unknown to the researcher. In other situations, the researcher may be interested in comparing observed effect sizes to known standards to quantify clinical importance. In these cases, the notion of relative effect sizes (small, medium, large) can be useful as benchmarks. Although there is generally an extensive literature on relative effect sizes for continuous data, little of this research has focused on relative effect sizes for measures of risk that are common in epidemiological or biomedical studies. The aim of this paper, therefore, is to extend existing relative effect sizes to the relative risk, odds ratio, hazard ratio, rate ratio, and Mantel–Haenszel odds ratio for related samples. In most scenarios with equal group allocation, effect sizes of 1.22, 1.86, and 3.00 can be taken as small, medium, and large, respectively. The odds ratio for a non rare event is a notable exception and modified relative effect sizes are 1.32, 2.38, and 4.70 in that situation.     

Simplified Likelihood Based Goodness-of-fit Tests for the Weibull Distribution

The aim of this paper is to present new likelihood based goodness-of-fit tests for the two-parameter Weibull distribution. These tests consist in nesting the Weibull distribution in three-parameter generalized Weibull families and testing the value of the third parameter by using the Wald, score, and likelihood ratio procedures. We simplify the usual likelihood based tests by getting rid of the nuisance parameters, using three estimation methods. The proposed tests are not asymptotic. A comprehensive comparison study is presented. Among a large range of possible GOF tests, the best ones are identified. The results depend strongly on the shape of the underlying hazard rate.     

Multiscale Bayesian state-space model for Granger causality analysis of brain signal

Modelling time-varying and frequency-specific relationships between two brain signals is becoming an essential methodological tool to answer theoretical questions in experimental neuroscience. In this article, we propose to estimate a frequency Granger causality statistic that may vary in time in order to evaluate the functional connections between two brain regions during a task. We use for that purpose an adaptive Kalman filter type of estimator of a linear Gaussian vector autoregressive model with coefficients evolving over time. The estimation procedure is achieved through variational Bayesian approximation and is extended for multiple trials. This Bayesian State Space (BSS) model provides a dynamical Granger-causality statistic that is quite natural. We propose to extend the BSS model to include the à trous Haar decomposition. This wavelet-based forecasting method is based on a multiscale resolution decomposition of the signal using the redundant à trous wavelet transform and allows us to capture short- and long-range dependencies between signals. Equally importantly it allows us to derive the desired dynamical and frequency-specific Granger-causality statistic. The application of these models to intracranial local field potential data recorded during a psychological experimental task shows the complex frequency-based cross-talk between amygdala and medial orbito-frontal cortex.     

Occupational therapy students' contribution towards enabling potential in a semi-rural community

Occupational therapy students at the University of Pretoria engage in a 7-week service learning fieldwork in their fourth year. This fieldwork takes place in a semi-rural community. The article reports on a successful project where the students enabled adult learners with disabilities to improve their engagement in meaningful activities and trained the learners in selected prevocational skills. The students included a teacher from the center and she was given the opportunity to expand her skills in teaching as well. The ZOPP model of community project planning was used and proved to be a useful tool for students who have limited time for planning, implementation and evaluation of a project.     

WHY ARE THE 2000s SO DIFFERENT FROM THE 1970s? A STRUCTURAL INTERPRETATION OF CHANGES IN THE MACROECONOMIC EFFECTS OF OIL PRICES

In the 1970s, large increases in the price of oil were associated with sharp decreases in output and large increases in inflation. In the 2000s, even larger increases in the price of oil were associated with much milder movements in output and inflation. Using a structural VAR approach, Blanchard and Gali (in J. Gali and M. Gertler (eds.) 2009, International Dimensions of Monetary Policy, University of Chicago Press, pp. 373–428) argued that this reflected a change in the causal relation from the price of oil to output and inflation. They then argued that this change could be due to a combination of three factors: a smaller share of oil in production and consumption, lower real wage rigidity, and better monetary policy. Their argument, based on simulations of a simple new‐Keynesian model, was informal. Our purpose in this paper is to take the next step, and to estimate the explanatory power and contribution of each of these factors. To do so, we use a minimum distance estimator that minimizes, over the set of structural parameters and for each of two samples (pre‐ and post‐1984), the distance between the empirical SVAR‐based impulse response functions and those implied by a new‐Keynesian model. Our empirical results point to an important role for all three factors.     

Individual vs. couple behavior: an experimental investigation of risk preferences

In this article, we elicit both individuals’ and couples’ preferences assuming prospect theory (PT) as a general theoretical framework for decision under risk. Our experimental method, based on certainty equivalents, allows to infer measurements of utility and probability weighting at the individual level and at the couple level. Our main results are twofold. First, risk attitude for couples is compatible with PT and incorporates deviations from expected utility similar to those found in individual decision making. Second, couples’ attitudes towards risk are found to be consistent with a mix of individual attitudes, women being more influent on couples’ preferences at low probability levels.     

Enhancing Confidentiality within Small Groups: The Experiences of AIDS Service Organizations

Fear that others will not maintain confidentiality can prevent persons living with HIV/AIDS (PHAs) from participating in small groups. This article reports on practices for enhancing confidentiality among group members. The research involved surveying group workers on practices used to promote confidentiality and providing PHAs opportunity to share their perspectives. Group workers most frequently reported discussing confidentiality with group members, modelling respect of confidentiality, and seeking member agreement to maintain confidentiality. They also shared suggestions and described challenges related to maintaining confidentiality. Overall, the PHAs agreed with the reported practices. However, differences did emerge around the use and emphasis of certain means, such as the use of signed agreements.     

The structure and evolution of the strategic management field: A content analysis of 26 years of strategic management research

This paper analyses 26 years of strategic management research published in Academy of Management Journal, Academy of Management Review, Administrative Science Quarterly and Strategic Management Journal. Through a content analysis, it studies the relationships between the subfields of strategic management. A multiple correspondence analysis provides a map of keywords and authors, and a framework to track this literature over the 26‐year period. A discussion of future pathways in the strategic management literature is also provided.     

Goodness-of-Fit Methods for Probabilistic Index Models

A class of semiparametric regression models, called probabilistic index models, has been recently proposed. Because these models are semiparametric, inference is only valid when the proposed model is consistent with the underlying data-generating model. However, no formal goodness-of-fit methods for these probabilistic index models exist yet. We propose a test and a graphical tool for assessing the model adequacy. Simulation results indicate that both methods succeed in detecting lack-of-fit. The methods are also illustrated on a case study.     

Estimation in a Two Variance Components Model When one Component is Known

We study a mixed linear model with two variance components. We suppose that one component is known. The objective of the paper is the estimation of the unknown component. The usual MINQE estimators seem to be unadapted to the problem. So we propose a new family of quadratic estimators, based on a natural class of estimators and the idea upon which the MINQE theory is built. All the estimators are compared on simulated data.