A population sample registry for public health survey investigations: methods,lessons, and successes

Public health relies on sample surveys to monitor the health of populations and investigate factors that contribute to population health and illness. Such investigations require surveying individuals who are selected in ways that support statistical inference to the population. Faced with rising costs and declining participation, survey researchers are exploring economical ways to recruit samples that validly represent larger populations. The current report describes an innovative resource, the Colorado Adult Population Sample–Survey Research Registry, which maintains survey access to respondents from a probability-based state health survey. We describe recruitment and retention strategies, explore representativeness, report several studies that used the registry, and discuss future directions.     

Incorporating eye tracking into cognitive interviewing to pretest survey questions

In this study, we investigated whether incorporating eye tracking into cognitive interviewing is effective when pretesting survey questions. In the control condition, a cognitive interview was conducted using a standardized interview protocol that included pre-defined probing questions for about one-quarter of the questions in a 52-item questionnaire. In the experimental condition, participants’ eye movements were tracked while they completed an online version of the questionnaire. Simultaneously, their reading patterns were monitored for evidence of response problems. Afterward, a cognitive interview was conducted using an interview protocol identical to that in the control condition. We compared both approaches with regard to the number and types of problems they detected. We found support for our hypothesis that cognitive interviewing and eye tracking complement each other effectively. As expected, the hybrid method was more productive in identifying both questionnaire problems and problematic questions than applying cognitive interviewing alone.     

The role of social capital in the job-related regional mobility decisions of unemployed individuals

Social capital is an important factor in interregional mobility. Although most prior research has focused on its role in the job-finding process, this study investigates the function of social networks and the social capital embedded therein after an interregional job offer has been received. This subject is particularly important for the unemployed, who should be able to exploit a mobility strategy to re-enter the labour market. Unemployed persons rely on their social networks to cope with joblessness, but there is evidence that social contexts can also act as mobility traps for this group (Windzio, 2004). We examine whether the unemployed weight social capital in a unique manner when making decisions regarding mobility.To investigate these issues, we combine a factorial survey module (FSM) with data from the German “Labour market and social security” (PASS) panel study to generate representative samples of both unemployed and employed persons with a randomised mobility stimulus in the form of hypothetical interregional job offers. Our results reveal the mobilising effects of exposure to conflict-laden relationships with the social network and the household. These are particularly pronounced for unemployed persons, highlighting the importance of factors that influence decision making about mobility beyond simple economic considerations.     

Quasi-minimax estimation in the partial linear model

This article discusses the minimax estimator in partial linear model y = Zβ + f + ε under ellipsoidal restrictions on the parameter space and quadratic loss function. The superiority of the minimax estimator over the two-step estimator is studied in the mean squared error matrix criterion.     

Why do we observe misclassification errors smaller than the Bayes error?

In simulation studies for discriminant analysis, misclassification errors are often computed using the Monte Carlo method, by testing a classifier on large samples generated from known populations. Although large samples are expected to behave closely to the underlying distributions, they may not do so in a small interval or region, and thus may lead to unexpected results. We demonstrate with an example that the LDA misclassification error computed via the Monte Carlo method may often be smaller than the Bayes error. We give a rigorous explanation and recommend a method to properly compute misclassification errors.     

Aggregating classifiers via Rademacher–Walsh polynomials

Methods are proposed to combine several individual classifiers in order to develop more accurate classification rules. The proposed algorithm uses Rademacher–Walsh polynomials to combine M (≥2) individual classifiers in a nonlinear way. The resulting classifier is optimal in the sense that its misclassification error rate is always less than, or equal to, that of each constituent classifier. A number of numerical examples (based on both real and simulated data) are also given. These examples demonstrate some new, and far-reaching, benefits of working with combined classifiers.     

Mean shift and influence measures in linear measurement error models with stochastic linear restrictions

We present influence diagnostics for linear measurement error models with stochastic linear restrictions using the corrected likelihood of Nakamura in 1990. The case deletion and mean shift outlier models are developed to identify outlying and influential observations. We derive a corrected score test statistic for outlier detection based on mean shift outlier models. The analogs of Cook's distance and likelihood distance are proposed to determine influential observations based on case deletion models. A parametric bootstrap procedure is used to obtain empirical distributions of the test statistics and a simulation study has been used to evaluate the performance of the proposed estimators based on the mean squares error criterion and the score test statistic. Finally, a numerical example is given to illustrate the theoretical results.     

Generating synthetic data to produce public-use microdata for small geographic areas based on complex sample survey data with application to the National Health Interview Survey

Small area statistics obtained from sample survey data provide a critical source of information used to study health, economic, and sociological trends. However, most large-scale sample surveys are not designed for the purpose of producing small area statistics. Moreover, data disseminators are prevented from releasing public-use microdata for small geographic areas for disclosure reasons; thus, limiting the utility of the data they collect. This research evaluates a synthetic data method, intended for data disseminators, for releasing public-use microdata for small geographic areas based on complex sample survey data. The method replaces all observed survey values with synthetic (or imputed) values generated from a hierarchical Bayesian model that explicitly accounts for complex sample design features, including stratification, clustering, and sampling weights. The method is applied to restricted microdata from the National Health Interview Survey and synthetic data are generated for both sampled and non-sampled small areas. The analytic validity of the resulting small area inferences is assessed by direct comparison with the actual data, a simulation study, and a cross-validation study.     

Barriers to Managing Coronary Artery Disease Among Older Women

Numerous barriers to managing coronary artery disease (CAD) among older women are reported in the literature; however, few studies adjust for demographic and health status differences. A survey assessing barriers and other factors was distributed to a stratified random sampling of older women with CAD. Factor analysis and multiple logistic regression procedures were used to estimate the impact of these issues on receiving a CAD-related office visit. The most problematic barriers included denial and low health literacy. Efforts to promote patient awareness of heart health and better communication between patients and clinicians may alleviate these barriers.     

黄山市音乐考级的现状调查与研究

音乐考级是对音乐能力进行的分级考试。黄山市作为皖南山城,具有浓厚的徽州文化底蕴,在当地老百姓的观念中学习艺术是旁门左道,自从十年前第一个音乐考级点建立起,至今已有三个考级单位,参与培训并获得考级证书的学员近万人。极大程度上推动了黄山市素质教育的发展,更新了老百姓的传统观念。本文通过对黄山市音乐考级现状的调查与分析,了解本市音乐考级这一机制的运行情况,总结音乐考级带来的成效以及存在的不良发展趋向,提出解决弊端的方法并对黄山市音乐考级未来发展提出建设性的建议。