An Alternative Approach for Nonlinear Latent Variable Models

In the last decades there has been an increasing interest in nonlinear latent variable models. Since the seminal paper of Kenny and Judd, several methods have been proposed for dealing with these kinds of models. This article introduces an alternative approach. The methodology involves fitting some third-order moments in addition to the means and covariances. This article discusses how the model equations can be formulated and how several standard tests, like the model fit and Lagrange multiplier tests, can be performed. The new method compares favorably with the maximum likelihood method in several studies and can provide evidence of interaction that earlier approaches might ignore.     

Intrapersonal and Interpersonal Theories of Motivation from an Attributional Perspective

Two related attribution theories of motivation are examined. One, an intrapersonal theory, includes self-directed thoughts (particularly expectancy of success) and self-directed emotions (pride, guilt, and shame). The second is an interpersonal theory and includes beliefs about the responsibility of others and other-directed affects of anger and sympathy. These two theories are respectively guided by the disparate metaphors of the person as a scientist and the person as a judge. Some experimental evidence supporting the conceptions and the range of phenomena that they incorporate are examined.     

回溯推理的另类研究

回溯推理可分为一般回溯推理和特殊回溯推理,一般回溯推理是或然性推理,特殊回溯推理是必然性推理,回溯推理是一种独特的推理形式,在一定程度上丰富了推理的内容。     

Meta‐Analytic Methodology and Inferences About the Efficacy of Formative Assessment

In a recent article published in EM:IP, Kingston and Nash report on the results of a meta‐analysis on the efficacy of formative assessment. They conclude that the average effect of formative assessment on student achievement is about .20 SD units. This would seem to dispel the myth that effects between .40 and .70 can be attributed to formative assessment. They also find that there is considerable variability in effect sizes across studies, and that only the content area in which the treatment is situated explains a significant proportion of study variability. However, there are issues in the meta‐analytic methodology employed by the authors that make their findings somewhat equivocal. This commentary focuses on four methodological concerns about the Kingston and Nash meta‐analysis: (1) the approach taken to select studies for inclusion, (2) the application of study inclusion criteria, (3) the extent to which the effect sizes being combined are biased, and (4) the relationship between effect size magnitude and characteristics of outcome measures. After examining these issues in the context of the Kingston and Nash review, it appears that considerable uncertainty remains about the effect that formative assessment practices have on student achievement.     

全国翻译专业资格(水平)考试英语二级笔译试题效度分析

全国翻译专业资格(水平)考试(CATTI)是为加强我国外语翻译专业人才建设于2003年形成的一项新兴考试。本文选取2010年下半年至2011年下半年三次二级笔译试题为研究对象,从内在效度、外在效度、使用效度这三个方面对该试题进行效度分析,以期对提高试卷质量有一些帮助。     

成就动机的归因模型建构及其在教育实践中的运用

教育是归因理论最重要的应用领域之一,学生对其学业成败的归因在决定其成就动机和学习行为方面有重要作用．而当今成就动机研究几乎成了动机研究的同义语,关心学生学业成就的教育心理学家占据了动机研究的中心舞台．在此背景下,归因理论的介入给其研究注入了新的活力,成为该领域的主旋律,对教育教学实践起着积权的指导意义．文章拟结合动机研究的发展历程,对成就动机的归因模型建构加以探析,并讨论其在教育教学实践中的意义及运用．     

Bias Reduction in Quasi-Experiments With Little Selection Theory but Many Covariates

Abstract: In observational studies, selection bias will be completely removed only if the selection mechanism is ignorable, namely, all confounders of treatment selection and potential outcomes are reliably measured. Ideally, well-grounded substantive theories about the selection process and outcome-generating model are used to generate the sample of covariates. However, covariate selection is more heuristic in actual practice. Using two empirical data sets in a simulation study, we investigate four research questions about bias reduction when the selection mechanism is not known but many covariates are measured: (1) How important is the conceptual heterogeneity of the covariate domains in the data set? (2) How important is the number of covariates assessing each domain? (3) What are the joint effects of this conceptual heterogeneity and of the number of covariates per domain? (4) What happens to bias reduction when the set of covariates is deliberately impoverished by removing the covariates most responsible for selection bias, thus ensuring a slightly smaller but still heterogeneous set of covariates? The results indicate: (1) increasingly more bias is reduced as the number of covariate domains and the number of covariates per domain increase, though the rate of bias reduction is diminishing in each case; (2) sampling covariates from multiple heterogeneous covariate domains is more important than choosing many measures from fewer domains; (3) the most heterogeneous set of covariate domains removes almost all of the selection bias when at least five covariates are assessed in each domain; and (4) omitting the most crucial covariates generally replicates the pattern of results due to the number of domains and the number of covariates per domain, but the amount of bias reduction is less than when all variables are included and will surely not satisfy all consumers of causal research.     

Regression Discontinuity Designs With Multiple Rating-Score Variables

Abstract

In the absence of a randomized control trial, regression discontinuity (RD) designs can produce plausible estimates of the treatment effect on an outcome for individuals near a cutoff score. In the standard RD design, individuals with rating scores higher than some exogenously determined cutoff score are assigned to one treatment condition; those with rating scores below the cutoff score are assigned to an alternate treatment condition. Many education policies, however, assign treatment status on the basis of more than one rating-score dimension. We refer to this class of RD designs as “multiple rating score regression discontinuity” (MRSRD) designs. In this paper, we discuss five different approaches to estimating treatment effects using MRSRD designs (response surface RD; frontier RD; fuzzy frontier RD; distance-based RD; and binding-score RD). We discuss differences among them in terms of their estimands, applications, statistical power, and potential extensions for studying heterogeneity of treatment effects.     

心理实验设计与评价的初步分析

文章对心理实验设计与评价做了初步分析,重点探讨了影响心理实验设计、实施、解析、推广、评价等工作的重要因素——心理实验效度,提出了提高心理实验效度的方法,即依据研究目的,进行最佳实验设计及依据实验设计,进行最佳实验控制。并对心理实验设计的评价标准进行了举例分析。     

试论论证的有效性

辨别有效论证与非有效论证是逻辑哲学的中心任务。论证可从多方面进行评价,在此,只涉及逻辑的有效性问题。演绎系统内有效性与系统外有效性之间是协调一致的。非形式论证的有效性与它们的逻辑形式表达之间的关系是复杂多样的。     

Assessing Sensitivity to Unmeasured Confounding Using a Simulated Potential Confounder

ABSTRACT

A major obstacle to developing evidenced-based policy is the difficulty of implementing randomized experiments to answer all causal questions of interest. When using a nonexperimental study, it is critical to assess how much the results could be affected by unmeasured confounding. We present a set of graphical and numeric tools to explore the sensitivity of causal estimates to the presence of an unmeasured confounder. We characterize the confounder through two parameters that describe the relationships between (a) the confounder and the treatment assignment and (b) the confounder and the outcome variable. Our approach has two primary advantages over similar approaches that are currently implemented in standard software. First, it can be applied to both continuous and binary treatment variables. Second, our method for binary treatment variables allows the researcher to specify three possible estimands (average treatment effect, effect of the treatment on the treated, effect of the treatment on the controls). These options are all implemented in an R package called treatSens. We demonstrate the efficacy of the method through simulations. We illustrate its potential usefulness in practice in the context of two policy applications.     

What response rates are needed to make reliable inferences from student evaluations of teaching?

This paper addresses the determination of statistically desirable response rates in students’ surveys, with emphasis on assessing the effect of underlying variability in the student evaluation of teaching (SET). We discuss factors affecting the determination of adequate response rates and highlight challenges caused by non-response and lack of randomization. Estimates of underlying variability were obtained for a period of 4?years, from online evaluations at the University of British Columbia (UBC). Simulations were used to examine the effect of underlying variability on desirable response rates. The UBC response rates were compared to those reported in the literature. Results indicate that small differences in underlying variability may not impact desired rates. We present acceptable response rates for a range of variability scenarios, class sizes, confidence level, and margin of error. The stability of estimates observed at UBC, over a 4-year period, indicates that valid model-based inferences of SET could be made.     

基于GRU网络的格兰杰因果网络重构

传统格兰杰因果依赖线性动力学,无法适应非线性应用场景的需求,因此提出一种基于GRU网络的格兰杰因果网络重构方法。该方法将整个网络重构划分为每个目标节点的邻居节点选择问题,针对每个目标节点构建基于GRU网络的格兰杰因果模型,在循环神经网络中引入简单的门控机制控制信息的更新方式,并对网络输入权重施加组稀疏惩罚以提取节点间的格兰杰因果关系。然后集成每一个子网络,获得最终完整的因果网络结构,并在GRU网络建模训练过程中考虑采用正则化的优化方法。通过线性矢量自回归、非线性矢量自回归、非均匀嵌入时滞矢量自回归、Lorenz-96模型及DREAM3竞赛数据集的实验表明,所提网络鲁棒性较强、有效性较高,在网络重构性能上具有明显的优越性。