主观变量解释主观变量:方法论辨析

社会学经验研究经常会需要处理诸如态度、感受、偏好这样的主观变量，无论从理论上还是方法上，对特定主观变量的理解往往会涉及另外一个主观变量。尽管如此，在具体的研究实践中，社会学者往往对“主观解释主观”的分析进路持有相当程度的保留态度，从而尽可能地采用客观变量来解释主观变量，或者探究主观变量对于客观变量的影响。“主观解释主观”的分析进路究竟有什么方法论上的“缺陷”?更为重要的是，采用什么手段可以尽可能避免这些“缺陷”以拓宽研究者的经验解释能力?这些问题到目前为止都还缺乏系统研究。在此背景下，本文考察了“主观解释主观”的分析进路所存在的混淆偏误问题，并从理论论辩、变量测量和数据分析三个方面讨论了一系列的应对策略。针对每一个应对策略，本文展示了经验实例，并对该策略的优缺点和适用情况进行了剖析和对比。此外，本文也具体分析了诸如因果关系方向、判断混淆偏误的敏感性检验、测量误差等未尽议题。本文希望通过这些讨论能增强社会学解释路径的多样性，促进社会学与其他学科（例如心理学、政治学等）的交流与互鉴。     

国际贸易中的环境问题及对策研究

与国际贸易有关的环境问题越来越受到重视,理论界对自由贸易与环境保护之间的争议已久,产生了各种说法与解释;严格的环境保护标准必将影响一国产业的国际竞争力,进而影响对外贸易。合适的政策措施有利于解决全球范围内的贸易与环境之间的问题。     

How does the DerSimonian and Laird procedure for random effects meta-analysis compare with its more efficient but harder to compute counterparts?

The procedure suggested by DerSimonian and Laird is the simplest and most commonly used method for fitting the random effects model for meta-analysis. Here it is shown that, unless all studies are of similar size, this is inefficient when estimating the between-study variance, but is remarkably efficient when estimating the treatment effect. If formal inference is restricted to statements about the treatment effect, and the sample size is large, there is little point in implementing more sophisticated methodology. However, it is further demonstrated, for a simple special case, that use of the profile likelihood results in actual coverage probabilities for 95% confidence intervals that are closer to nominal levels for smaller sample sizes. Alternative methods for making inferences for the treatment effect may therefore be preferable if the sample size is small, but the DerSimonian and Laird procedure retains its usefulness for larger samples.     

Estimation of average treatment effects based on parametric propensity score model

In this paper, the estimation of average treatment effects is examined given that the propensity score is of a parametric form with some unknown parameters. Under the assumption that the treatment is ignorable given some observed characteristics, the MLEs for those unknown parameters in the probability assignment model have been achieved firstly and then three estimators have been defined by the inverse probability weighted, regression and imputation methods, respectively. All the estimators are shown asymptotically normal and more importantly, the substantial efficiency gains of the first two estimates have been obtained theoretically compared with the existing estimators in Hahn (1998) and Hirano et al. (2003), i.e., the inverse weighted probability estimator and the regression estimator have smaller asymptotic variances. Our simulation analysis verifies the theoretical results in terms of biases, SEs and MSEs.     

首都互联网产业经济效应分析

本文揭示了互联网产业产生经济效应的内在机理及其对于经济增长的影响,结合相关的统计数据,分析论证了北京市互联网产业对于整个首都经济的带动作用。     

试论红色经典的传播效果

从传播学角度对红色经典及其文化现象进行解读,是深入研究红色经典的重要方面.红色经典自诞生以来大致经历了三个主要传播阶段:建国后至"文革"结束之前(1949-1976年)为第一阶段,"文革"结束至80年代末(1977-1989年)为第二阶段,1990年以来为第三阶段.这三个阶段的传播效果分别可以用子弹理论/强大效果模式、有限效果模式和适度效果模式加以描述和分析.衍生文本的盛行,是红色经典传播中的突出现象.     

A comparison of bootstrap approaches for estimating uncertainty of parameters in linear mixed‐effects models

A version of the nonparametric bootstrap, which resamples the entire subjects from original data, called the case bootstrap, has been increasingly used for estimating uncertainty of parameters in mixed‐effects models. It is usually applied to obtain more robust estimates of the parameters and more realistic confidence intervals (CIs). Alternative bootstrap methods, such as residual bootstrap and parametric bootstrap that resample both random effects and residuals, have been proposed to better take into account the hierarchical structure of multi‐level and longitudinal data. However, few studies have been performed to compare these different approaches. In this study, we used simulation to evaluate bootstrap methods proposed for linear mixed‐effect models. We also compared the results obtained by maximum likelihood (ML) and restricted maximum likelihood (REML). Our simulation studies evidenced the good performance of the case bootstrap as well as the bootstraps of both random effects and residuals. On the other hand, the bootstrap methods that resample only the residuals and the bootstraps combining case and residuals performed poorly. REML and ML provided similar bootstrap estimates of uncertainty, but there was slightly more bias and poorer coverage rate for variance parameters with ML in the sparse design. We applied the proposed methods to a real dataset from a study investigating the natural evolution of Parkinson's disease and were able to confirm that the methods provide plausible estimates of uncertainty. Given that most real‐life datasets tend to exhibit heterogeneity in sampling schedules, the residual bootstraps would be expected to perform better than the case bootstrap. Copyright © 2013 John Wiley & Sons, Ltd.     

Accounting for Response Misclassification and Covariate Measurement Error Using a Random Effects Logit Model

Often in longitudinal data arising out of epidemiologic studies, measurement error in covariates and/or classification errors in binary responses may be present. The goal of the present work is to develop a random effects logistic regression model that corrects for the classification errors in binary responses and/or measurement error in covariates. The analysis is carried out under a Bayesian set up. Simulation study reveals the effect of ignoring measurement error and/or classification errors on the estimates of the regression coefficients.     

A generalized,multi-stage adjusted,latent class linear mixed model for testing genetic association

ABSTRACT

In this study, a Generalized, Multi-Stage Adjusted, Latent Class Linear Mixed Model is proposed for modeling the heterogeneous distributed phenotype and genetic information across the whole genome in the presence of both serial and familial correlations. Genome data were analyzed by applying the proposed model to Genetic Analysis Workshop (GAW) data, and the model results were compared to the results of standard models. Moreover, the potential of the model is discussed compared to simulated data. As a result of model comparisons, the information criteria and the genomic control parameter were found to be smaller. The results of a power analysis show that the proposed model is more powerful.     

An estimator of relative potency

The Kolmogorov-Sxnirnov statistic is used as a basis for defining nonparaim trie point and interval estimators for the relative potency of a test preparation with respect to a standard preparation These estimators compare favorably with those proposed by Sen (1963) if the dosage distribuiton has heavy mass in either of its tails.