浅议中高层管理者绩效评估指标的设计

从中高层管理者绩效评估指标设计中存在的问题出发 ,强调了绩效评估理念在绩效评估指标设计中的重要性 ,阐明了绩效评估的理念 ,并以适用于中高层管理者绩效评估的平衡计分卡为例 ,介绍了如何将绩效评估理念全面地贯彻到绩效评估指标的具体设计过程之中 ,最后针对我国的绩效评估现状提出相应建议。     

大学生职业选择匹配度量表的编制与估计

运用深度访谈和文献法初步编制条目,经过419份有效数据的预测和1680份有效数据的正式检测,构建出信度与效度均为合理的大学生职业选择匹配度量表。运用回归分析方法,建构职业选择影响因素权重等值和不等值条件下的匹配度估计模型,作为量化大学生职业选择与社会生活满意匹配度,服务提高大学生就业质量的操作分析工具。     

长沙市制造业与现代服务业融合发展战略研究

从产业集群的理论角度分析了制造业与现代服务业融合的趋势,分析了长沙制造业与现代服务业及其融合发展基础和存在的主要问题,在借鉴我国先进城市的基础,提出了长沙制造业与现代服务业融合的战略和原则,优势产业、新兴产业和传统产业与现代服务业融合路径和措施。     

General purpose multiply robust data integration procedures for handling nonprobability samples

In recent years, there has been an increased interest in combining probability and nonprobability samples. Nonprobability sample are cheaper and quicker to conduct but the resulting estimators are vulnerable to bias as the participation probabilities are unknown. To adjust for the potential bias, estimation procedures based on parametric or nonparametric models have been discussed in the literature. However, the validity of the resulting estimators relies heavily on the validity of the underlying models. Also, nonparametric approaches may suffer from the curse of dimensionality and poor efficiency. We propose a data integration approach by combining multiple outcome regression models and propensity score models. The proposed approach can be used for estimating general parameters including totals, means, distribution functions, and percentiles. The resulting estimators are multiply robust in the sense that they remain consistent if all but one model are misspecified. The asymptotic properties of point and variance estimators are established. The results from a simulation study show the benefits of the proposed method in terms of bias and efficiency. Finally, we apply the proposed method using data from the Korea National Health and Nutrition Examination Survey and data from the National Health Insurance Sharing Services.     

Variance estimation of the risk difference when using propensity-score matching and weighting with time-to-event outcomes

Observational studies are increasingly being used in medicine to estimate the effects of treatments or exposures on outcomes. To minimize the potential for confounding when estimating treatment effects, propensity score methods are frequently implemented. Often outcomes are the time to event. While it is common to report the treatment effect as a relative effect, such as the hazard ratio, reporting the effect using an absolute measure of effect is also important. One commonly used absolute measure of effect is the risk difference or difference in probability of the occurrence of an event within a specified duration of follow-up between a treatment and comparison group. We first describe methods for point and variance estimation of the risk difference when using weighting or matching based on the propensity score when outcomes are time-to-event. Next, we conducted Monte Carlo simulations to compare the relative performance of these methods with respect to bias of the point estimate, accuracy of variance estimates, and coverage of estimated confidence intervals. The results of the simulation generally support the use of weighting methods (untrimmed ATT weights and IPTW) or caliper matching when the prevalence of treatment is low for point estimation. For standard error estimation the simulation results support the use of weighted robust standard errors, bootstrap methods, or matching with a naïve standard error (i.e., Greenwood method). The methods considered in the article are illustrated using a real-world example in which we estimate the effect of discharge prescribing of statins on patients hospitalized for acute myocardial infarction.