Long- and short-term affective states in happiness: Age and sex comparisons

Data from 690 persons in three adult age groups were used to evaluate the generality of a componential model of happiness (Kozma et al., 1990). The model postulates that long- and short-term affective states combine in an additive manner to produce current happiness. The short-term components should be more susceptible to environmental manipulation than the long-term ones and should change more readily with an appropriate experimental manipulation. Subscales of the Memorial University Mood Scale (MUMS) were used to assess short-term affect while the experience subscales of the Memorial University of Newfoundland Scale of Happiness (MUNSH) and Diener's Long-term Satisfaction Scale were used to measure long-term affect. Overall current happiness was assessed by a seven-point avowed happiness rating scale. The Velten mood induction procedure was used to manipulate current affective state. In five of six comparisons, changes on short-term components were significantly greater than on long-term components. Age differences in reactivity to mood induction emerged only when a negative induction procedure was followed by a positive one. Under these conditions, the youngest cohort responded more consistently than the oldest cohort. An additive model, based on long- and short-term affect, age, and sex produced the best explanation for current happiness.     

Process evaluation of the midwifery initiated oral health-dental service program: Perceptions of midwives in Greater Western Sydney,Australia

Background

Despite links between poor maternal oral health, adverse pregnancy outcomes and early childhood decay there is limited emphasis on maternal oral health in Australia. To address this, the Midwifery Initiated Oral Health Dental Service (MIOH-DS) program was developed in collaboration with the Australian College of Midwives.

On Multidimensional Approaches to Financial Literacy Measurement

Social Indicators Research - Financial literacy has become an important research topic in recent years. This paper uses data on financial literacy collected in 2012 by Israel’s Central Bureau...     

Metagovernance,political demand and policy advice: a case from Brazil

ABSTRACT

There has been a call for a “second wave” of scholarship on policy advice to expand our understanding of the relational dynamics within a policy advisory system (PAS). In this article, we use a case from Brazil to address two key gaps in the PAS literature – the lack of attention to systems of network governance and the current predominance of “Westminster” empirical cases. To better understand the impact of policy advice within a system of networked governance, we apply the frame of “metagovernance” – the steering of governance networks. We then introduce and employ the concepts of funnelling, political brokering and gate-keeping to better understand how policy advice is shaped, modified and then either rejected or accepted. The contribution of the article is that, while much of the existing PAS literature describes the contours and key actors within an advisory system, we develop new conceptual scaffolding to better understand the trajectories and impact of policy advice, and the interplay between actors and agents, within a broader system of metagovernance.     

AN APPROACH FOR JOINTLY MODELING MULTIVARIATE LONGITUDINAL MEASUREMENTS AND DISCRETE TIME-TO-EVENT DATA

In many medical studies, patients are followed longitudinally and interest is on assessing the relationship between longitudinal measurements and time to an event. Recently, various authors have proposed joint modeling approaches for longitudinal and time-to-event data for a single longitudinal variable. These joint modeling approaches become intractable with even a few longitudinal variables. In this paper we propose a regression calibration approach for jointly modeling multiple longitudinal measurements and discrete time-to-event data. Ideally, a two-stage modeling approach could be applied in which the multiple longitudinal measurements are modeled in the first stage and the longitudinal model is related to the time-to-event data in the second stage. Biased parameter estimation due to informative dropout makes this direct two-stage modeling approach problematic. We propose a regression calibration approach which appropriately accounts for informative dropout. We approximate the conditional distribution of the multiple longitudinal measurements given the event time by modeling all pairwise combinations of the longitudinal measurements using a bivariate linear mixed model which conditions on the event time. Complete data are then simulated based on estimates from these pairwise conditional models, and regression calibration is used to estimate the relationship between longitudinal data and time-to-event data using the complete data. We show that this approach performs well in estimating the relationship between multivariate longitudinal measurements and the time-to-event data and in estimating the parameters of the multiple longitudinal process subject to informative dropout. We illustrate this methodology with simulations and with an analysis of primary biliary cirrhosis (PBC) data.     

Nuisance Parameters and the Use of Exploratory Graphical Methods in a Bayesian Analysis

Consider the problem of inference about a parameter θ in the presence of a nuisance parameter v. In a Bayesian framework, a number of posterior distributions may be of interest, including the joint posterior of (θ, ν), the marginal posterior of θ, and the posterior of θ conditional on different values of ν. The interpretation of these various posteriors is greatly simplified if a transformation (θ, h(θ, ν)) can be found so that θ and h(θ, v) are approximately independent. In this article, we consider a graphical method for finding this independence transformation, motivated by techniques from exploratory data analysis. Some simple examples of the use of this method are given and some of the implications of this approximate independence in a Bayesian analysis are discussed.     

Robust Mean Estimation Under a Possibly Incorrect Log-Normality Assumption

Nonparametric and parametric estimators are combined to minimize the mean squared error among their linear combinations. The combined estimator is consistent and for large sample sizes has a smaller mean squared error than the nonparametric estimator when the parametric assumption is violated. If the parametric assumption holds, the combined estimator has a smaller MSE than the parametric estimator. Our simulation examples focus on mean estimation when data may follow a lognormal distribution, or can be a mixture with an exponential or a uniform distribution. Motivating examples illustrate possible application areas.     

A comparison of estimation techniques for the three parameter pareto distribution

This paper compares minimum distance estimation with best linear unbiased estimation to determine which technique provides the most accurate estimates for location and scale parameters as applied to the three parameter Pareto distribution. Two minimum distance estimators are developed for each of the three distance measures used (Kolmogorov, Cramer‐von Mises, and Anderson‐Darling) resulting in six new estimators. For a given sample size 6 or 18 and shape parameter 1(1)4, the location and scale parameters are estimated. A Monte Carlo technique is used to generate the sample sets. The best linear unbiased estimator and the six minimum distance estimators provide parameter estimates based on each sample set. These estimates are compared using mean square error as the evaluation tool. Results show that the best linear unbaised estimator provided more accurate estimates of location and scale than did the minimum estimators tested.