Subjective Well-being in a Multicultural Urban Population: Structural, and Multivariate Analyses of the Ontario Health Survey Well-being Scale

The validity of a scale, from the OntarioHealth Survey, measuring the subjective senseof well-being, for a large multiculturalpopulation in Metropolitan Toronto, is examinedthrough principal components analysis withoblique rotation. Four factors are extracted. Factor 1, is a stress and strain factor, andconsists of health worries, feeling exhaustedand worn out, and feeling tense. Factor 2,interpreted as a sense of control, consists ofno health worries, and ability to handlefeelings. Factor 3, interpreted as positiveaffect, consists of having an interesting life,feeling cheerful and light-hearted, feelingloved, feeling relaxed and full of vitality. Factor 4 is interpreted as a despondency factorconsists of feeling lonely, having a boringlife, inability to control feelings and feelinglow. The statistic of multiple discriminantanalysis between the variables, sex, age, andethnic groupings, as independent discriminatingvariables and the four factor scores asdependent variables, is calculated. Theresults show that the observed mean variance inthe well-being factor scores cannot beexplained by the respondents being eitherfemale or male, or being a certain age or bybelonging to specific cultural groupings. These results are discussed in relation toother studies on the dimensions of subjectivewell-being.     

Variable selection via a multi-stage strategy

Variable selection for nonlinear regression is a complex problem, made even more difficult when there are a large number of potential covariates and a limited number of datapoints. We propose herein a multi-stage method that combines state-of-the-art techniques at each stage to best discover the relevant variables. At the first stage, an extension of the Bayesian Additive Regression tree is adopted to reduce the total number of variables to around 30. At the second stage, sensitivity analysis in the treed Gaussian process is adopted to further reduce the total number of variables. Two stopping rules are designed and sequential design is adopted to make best use of previous information. We demonstrate our approach on two simulated examples and one real data set.     

The Asymptotic Power Of Jonckheere-Type Tests For Ordered Alternatives

For the c -sample location problem with ordered alternatives, the test proposed by Barlow et al . (1972 p. 184) is an appropriate one under the model of normality. For non-normal data, however, there are rank tests which have higher power than the test of Barlow et al ., e.g. the Jonckheere test or so-called Jonckheere-type tests recently introduced and studied by Büning & Kössler (1996). In this paper the asymptotic power of the Jonckheere-type tests is computed by using results of Hájek (1968) which may be considered as extensions of the theorem of Chernoff & Savage (1958). Power studies via Monte Carlo simulation show that the asymptotic power values provide a good approximation to the finite ones even for moderate sample sizes.     

An Information-Maximizing Interactive Procedure for Scenario Probability Elicitation

Various approaches have been proposed for determining scenario probabilities to facilitate long-range planning and decision making. These include microlevel approaches based on the analysis of relevant underlying events and their interrelations and direct macrolevel examination of the scenarios. The determination of a unique solution demands excessive consistency and time requirements on the part of the expert and often is not guaranteed by these procedures. We propose an interactive information maximizing scenario probability query procedure (IMQP) that exploits the desirable features of existing methods while circumventing their drawbacks. The approach requires elicitation of cardinal probability assessments and bounds for only marginal and first-order conditional events, as well as ordinal probability comparisons (probability orderings or rankings) of carefully selected scenario subsets determined using concepts of information theory. Guidelines for implementation based on simulation results are also developed. A goal program for handling inconsistent ordinal probability responses is also integrated into the procedure. The results of behavioral experimentation (which compared our approach to Expert Choice and showed that the IMQP was viable) compared favorably in terms of ease of use and time requirements, and works best for problems with a large number of scenarios. Design modifications to IMQP learned from the experiments, such as incorporating interactive graphics, are also in progress.