Regression with Empirical Variable Selection: Description of a New Method and Application to Ecological Datasets

Despite recent papers on problems associated with full-model and stepwise regression, their use is still common throughout ecological and environmental disciplines. Alternative approaches, including generating multiple models and comparing them post-hoc using techniques such as Akaike's Information Criterion (AIC), are becoming more popular. However, these are problematic when there are numerous independent variables and interpretation is often difficult when competing models contain many different variables and combinations of variables. Here, we detail a new approach, REVS (Regression with Empirical Variable Selection), which uses all-subsets regression to quantify empirical support for every independent variable. A series of models is created; the first containing the variable with most empirical support, the second containing the first variable and the next most-supported, and so on. The comparatively small number of resultant models (n = the number of predictor variables) means that post-hoc comparison is comparatively quick and easy. When tested on a real dataset – habitat and offspring quality in the great tit (Parus major) – the optimal REVS model explained more variance (higher R2), was more parsimonious (lower AIC), and had greater significance (lower P values), than full, stepwise or all-subsets models; it also had higher predictive accuracy based on split-sample validation. Testing REVS on ten further datasets suggested that this is typical, with R2 values being higher than full or stepwise models (mean improvement = 31% and 7%, respectively). Results are ecologically intuitive as even when there are several competing models, they share a set of “core” variables and differ only in presence/absence of one or two additional variables. We conclude that REVS is useful for analysing complex datasets, including those in ecology and environmental disciplines

Qualitative study of the use of traditional healing by asthmatic Navajo families. Am Indian Alsk Native Ment Health Res

Abstract: Despite increasing prevalence of asthma among American Indians and/or Alaska Natives, little is known about their use of traditional healing in its management. A convenience sample of 24 Navajo families with asthmatic members (n=35) was interviewed between June 1997 and September 1998. While 46% of families had previously used traditional healing, only 29% sought traditional healing for asthma. Use of traditional healing was unrelated to use of biomedical therapies, hospitalizations, or emergency services. Practical factors and questions about the nature and origins of asthma were the primary considerations determining use of traditional medicine. Little conflict between traditional healing and biomedical treatment was reported. The use of traditional healing for asthma is influenced by beliefs about the disease and factors specific to the individual, including their local social, economic, and cultural context