中国近现代史纲要课程素质教育功能探索

全面提升大学生的综合素质是高等教育的根本任务。中国近现代史纲要作为高等学校大学生必修的一门思想政治理论课程，在担负其教学基本目标和要求之外，同时，具备不断提升大学生的综合素质的功能与作用。如何通过中国近现代史纲要教学实践，提升大学生的思想政治素质、人文素质、创新素质，并提出了行之有效的教学模式，对于加强和改进思想政治理论课教学，具有重要的实践意义。     

Backfitting Random Varying-Coefficient Models with Time-Dependent Smoothing Covariates

Abstract.  In this paper, we propose a random varying-coefficient model for longitudinal data. This model is different from the standard varying-coefficient model in the sense that the time-varying coefficients are assumed to be subject-specific, and can be considered as realizations of stochastic processes. This modelling strategy allows us to employ powerful mixed-effects modelling techniques to efficiently incorporate the within-subject and between-subject variations in the estimators of time-varying coefficients. Thus, the subject-specific feature of longitudinal data is effectively considered in the proposed model. A backfitting algorithm is proposed to estimate the coefficient functions. Simulation studies show that the proposed estimation methods are more efficient in finite-sample performance compared with the standard local least squares method. An application to an AIDS clinical study is presented to illustrate the proposed methodologies.     

Estimating household structure in ancient China by using historical data: a latent class analysis of partially missing patterns

Summary.  Social data often contain missing information. The problem is inevitably severe when analysing historical data. Conventionally, researchers analyse complete records only. Listwise deletion not only reduces the effective sample size but also may result in biased estimation, depending on the missingness mechanism. We analyse household types by using population registers from ancient China (618–907 AD) by comparing a simple classification, a latent class model of the complete data and a latent class model of the complete and partially missing data assuming four types of ignorable and non-ignorable missingness mechanisms. The findings show that either a frequency classification or a latent class analysis using the complete records only yielded biased estimates and incorrect conclusions in the presence of partially missing data of a non-ignorable mechanism. Although simply assuming ignorable or non-ignorable missing data produced consistently similarly higher estimates of the proportion of complex households, a specification of the relationship between the latent variable and the degree of missingness by a row effect uniform association model helped to capture the missingness mechanism better and improved the model fit.     

中国心理学史与中国的本土心理学研究之比较

中国的本土心理学和中国心理学史研究既有区别,又有联系。中国的本土心理学以内发性本土化原则为指导,以中国人的心理行为和本土的传统心理学为研究对象,在研究方法上采取多元化的态度。中国心理学史以外在逻辑原则为主导原则,以中国心理学思想为研究对象,形成了系统的研究方法。中国的本土心理学和中国心理学史研究有相同的文化背景和研究价值,这构成了二者契合的前提。中国的本土心理学和中国心理学史可以相互借鉴,共同建构科学的中国心理学。     

Choice of the primary analysis in longitudinal clinical trials

Missing data, and the bias they can cause, are an almost ever‐present concern in clinical trials. The last observation carried forward (LOCF) approach has been frequently utilized to handle missing data in clinical trials, and is often specified in conjunction with analysis of variance (LOCF ANOVA) for the primary analysis. Considerable advances in statistical methodology, and in our ability to implement these methods, have been made in recent years. Likelihood‐based, mixed‐effects model approaches implemented under the missing at random (MAR) framework are now easy to implement, and are commonly used to analyse clinical trial data. Furthermore, such approaches are more robust to the biases from missing data, and provide better control of Type I and Type II errors than LOCF ANOVA. Empirical research and analytic proof have demonstrated that the behaviour of LOCF is uncertain, and in many situations it has not been conservative. Using LOCF as a composite measure of safety, tolerability and efficacy can lead to erroneous conclusions regarding the effectiveness of a drug. This approach also violates the fundamental basis of statistics as it involves testing an outcome that is not a physical parameter of the population, but rather a quantity that can be influenced by investigator behaviour, trial design, etc. Practice should shift away from using LOCF ANOVA as the primary analysis and focus on likelihood‐based, mixed‐effects model approaches developed under the MAR framework, with missing not at random methods used to assess robustness of the primary analysis. Copyright © 2004 John Wiley & Sons, Ltd.     

Bias Correction in the Dynamic Panel Data Model with a Nonscalar Disturbance Covariance Matrix

Approximation formulae are developed for the bias of ordinary and generalized Least Squares Dummy Variable (LSDV) estimators in dynamic panel data models. Results from Kiviet [Kiviet, J. F. (1995), on bias, inconsistency, and efficiency of various estimators in dynamic panel data models, J. Econometrics68:53-78; Kiviet, J. F. (1999), Expectations of expansions for estimators in a dynamic panel data model: some results for weakly exogenous regressors, In: Hsiao, C., Lahiri, K., Lee, L-F., Pesaran, M. H., eds., Analysis of Panels and Limited Dependent Variables, Cambridge: Cambridge University Press, pp. 199-225] are extended to higher-order dynamic panel data models with general covariance structure. The focus is on estimation of both short- and long-run coefficients. The results show that proper modelling of the disturbance covariance structure is indispensable. The bias approximations are used to construct bias corrected estimators which are then applied to quarterly data from 14 European Union countries. Money demand functions for M1, M2 and M3 are estimated for the EU area as a whole for the period 1991: I-1995: IV. Significant spillovers between countries are found reflecting the dependence of domestic money demand on foreign developments. The empirical results show that in general plausible long-run effects are obtained by the bias corrected estimators. Moreover, finite sample bias, although of moderate magnitude, is present underlining the importance of more refined estimation techniques. Also the efficiency gains by exploiting the heteroscedasticity and cross-correlation patterns between countries are sometimes considerable.     

ROBUST METHODS FOR DEPENDENT CELL PEDIGREE DATA

This paper studies a robust approach to the analysis of cell pedigree data, building on the work of Huggins & Marschner (1991) which discussed M-estimation for the so-called bifurcating autoregressive process. The study allows for incomplete observation of the pedigree, and incorporates the possibility of additive effects outliers, as discussed in the time series literature. Some properties of the proposed estimation procedure are studied, including a Monte Carlo investigation of robustness in the presence of contamination.     

再谈历史心理学

西方心理史学,心态史学和历史心理学的研究目的虽不相同,但都把历史心理现象作为主要研究对象。因此有必要将三者结合创立历史心理学。历史心理学研究历史上个体心理和社会心理及其对历史事件和历史进程的影响。     

现象学的问题如何发生--德里达对胡塞尔发生现象学的解读

德里达对胡塞尔的发生现象学有他自己的特殊理解,他研究了观念的历史,以及根据所形成的心理习惯构成的观念,当然,这也是观念对象构成的历史过程。发生是一个陌生的过程:在即将来临的历史中包含着一种中断。德里达描述了从某种确定的目的论返回到胡塞尔所新近确立的起源问题的运动结构。德里达研究稳定的结构与发生现象学之间的关系,在这个过程中,德里达提出了关于解构的基本问题。     

论社会进步及其评价尺度

马克思的唯物史观告诉我们,社会进步是人类社会发展的必然趋势,但是,由于社会发展过程的复杂性,对于具体的历史事件作用,就存在着评价问题。社会进步的评价尺度其实就蕴含在社会进步的内涵当中。由于社会进步可以归结为生产力水平的提高与人的自由与解放,这两项内容就应当成为社会进步的衡量标准。生产力水平的提高与人的自由与解放的统一也就是马克思主义的历史尺度与价值尺度的统一,这是马克思主义在社会进步评价问题上的重要原则。