Estimating the Reliability of Single-Item Life Satisfaction Measures: Results from Four National Panel Studies

Life satisfaction is often assessed using single-item measures. However, estimating the reliability of these measures can be difficult because internal consistency coefficients cannot be calculated. Existing approaches use longitudinal data to isolate occasion-specific variance from variance that is either completely stable or variance that changes systematically over time. In these approaches, reliable occasion-specific variance is typically treated as measurement error, which would negatively bias reliability estimates. In the current studies, panel data and multivariate latent state-trait models are used to isolate reliable occasion-specific variance from random error and to estimate reliability for scores from single-item life satisfaction measures. Across four nationally representative panel studies with a combined sample size of over 68,000, reliability estimates increased by an average of 16% when the multivariate model was used instead of the more standard univariate longitudinal model.     

High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics

We describe studies in molecular profiling and biological pathway analysis that use sparse latent factor and regression models for microarray gene expression data. We discuss breast cancer applications and key aspects of the modeling and computational methodology. Our case studies aim to investigate and characterize heterogeneity of structure related to specific oncogenic pathways, as well as links between aggregate patterns in gene expression profiles and clinical biomarkers. Based on the metaphor of statistically derived "factors" as representing biological "subpathway" structure, we explore the decomposition of fitted sparse factor models into pathway subcomponents and investigate how these components overlay multiple aspects of known biological activity. Our methodology is based on sparsity modeling of multivariate regression, ANOVA, and latent factor models, as well as a class of models that combines all components. Hierarchical sparsity priors address questions of dimension reduction and multiple comparisons, as well as scalability of the methodology. The models include practically relevant non-Gaussian/nonparametric components for latent structure, underlying often quite complex non-Gaussianity in multivariate expression patterns. Model search and fitting are addressed through stochastic simulation and evolutionary stochastic search methods that are exemplified in the oncogenic pathway studies. Supplementary supporting material provides more details of the applications, as well as examples of the use of freely available software tools for implementing the methodology.     

Protein needs of older adults engaged in resistance training: a review

Protein recommendations by some professional organizations for young adults engaged in resistance training (RT) are higher than the recommended dietary allowance (RDA), but recommendations for resistance-training older adults (>50 years old) are not well characterized. Some argue that the current RDA is adequate, but others indicate increased protein needs. Although concerns have been raised about the consequences of high protein intake, protein intake above the RDA in older adults is associated with increased bone-mineral density when calcium intake is adequate and does not appear to compromise renal health in older individuals with normal renal function. Individual protein needs for older adults in RT are likely highly variable according to health and training regimen, but an intake of 1.0-1.3 g x kg(-1) x day(-1) should adequately and safely meet the needs of older adults engaged in RT, provided that their energy needs are met.     

The Social Participation of Blacks: A Proposed Synthesis of Two Competing Theories

Discrepant findings have typified the research concerning the social participation of blacks in America. The bulk of the findings would seem to posit three competing theories, one emphasizing low participation levels on the part of blacks, and the later two emphasizing high levels of social participation. These theories are examined and a synthesis of the "isolation" and "compensation" models is proposed. By examining both formal and informal types of social participation on the part of blacks, it is argued that both theories are viable toward explaining variation within black participation levels in particular and toward mediating the discrepant findings as they have been previously offered in general. Some support is found for such a proposed synthesis.     

On the Internal Structure of Cities

We prove the existence of a symmetric equilibrium in a circular city in which businesses and housing can both be located anywhere in the city. In this equilibrium, firms balance the external benefits from locating near other producers against the costs of longer commutes for workers. An equilibrium city need not take the form of a central business district surrounded by a residential area. We propose a general algorithm for constructing equilibria, and use it to study the way land use is affected by changes in the model's underlying parameters.     

Strengthening a linear reformulation of the 0-1 cubic knapsack problem via variable reordering

The 0-1 cubic knapsack problem (CKP), a generalization of the classical 0-1 quadratic knapsack problem, is an extremely challenging NP-hard combinatorial optimization problem. An effective exact solution strategy for the CKP is to reformulate the nonlinear problem into an equivalent linear form that can then be solved using a standard mixed-integer programming solver. We consider a classical linearization method and propose a variant of a more recent technique for linearizing 0-1 cubic programs applied to the CKP. Using a variable reordering strategy, we show how to improve the strength of the linear programming relaxation of our proposed reformulation, which ultimately leads to reduced overall solution times. In addition, we develop a simple heuristic method for obtaining good-quality CKP solutions that can be used to provide a warm start to the solver. Computational tests demonstrate the effectiveness of both our variable reordering strategy and heuristic method.