The Model-Size Effect on Traditional and Modified Tests of Covariance Structures

The integration of modern methods for causal inference with latent class analysis (LCA) allows social, behavioral, and health researchers to address important questions about the determinants of latent class membership. In this article, 2 propensity score techniques, matching and inverse propensity weighting, are demonstrated for conducting causal inference in LCA. The different causal questions that can be addressed with these techniques are carefully delineated. An empirical analysis based on data from the National Longitudinal Survey of Youth 1979 is presented, where college enrollment is examined as the exposure (i.e., treatment) variable and its causal effect on adult substance use latent class membership is estimated. A step-by-step procedure for conducting causal inference in LCA, including multiple imputation of missing data on the confounders, exposure variable, and multivariate outcome, is included. Sample syntax for carrying out the analysis using SAS and R is given in an appendix.     

民办大学英语学习者成败归因调查与分析

本研究以燕京理工学院2011级学生英语学习成败归因问卷调查的数据及其在2011年英语入学考试中的成绩为基础,探索民办大学英语学习者成败归因的特点与趋势。研究发现高分组和低分组的被试的归因趋势不存在显著性差异,主要归因于兴趣(内部、稳定、可控)、努力和方法(内部、不稳定、可控)这三种因素。     

A Tutorial in Bayesian Potential Outcomes Mediation Analysis

Statistical mediation analysis is used to investigate intermediate variables in the relation between independent and dependent variables. Causal interpretation of mediation analyses is challenging because randomization of subjects to levels of the independent variable does not rule out the possibility of unmeasured confounders of the mediator to outcome relation. Furthermore, commonly used frequentist methods for mediation analysis compute the probability of the data given the null hypothesis, which is not the probability of a hypothesis given the data as in Bayesian analysis. Under certain assumptions, applying the potential outcomes framework to mediation analysis allows for the computation of causal effects, and statistical mediation in the Bayesian framework gives indirect effects probabilistic interpretations. This tutorial combines causal inference and Bayesian methods for mediation analysis so the indirect and direct effects have both causal and probabilistic interpretations. Steps in Bayesian causal mediation analysis are shown in the application to an empirical example.     

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．     

从致使力的角度探讨英语致使句的生成

认知语言学认为句式是由概念决定的,要分析英语致使句式就要分析致使概念.而致使概念的核心是致使力,它是致使概念的决定因素,对概念的其他部分起着制约作用.因此要分析英语致使句的生成,关键是分析致使力的性质和特征.通过分析致使力的根本性质,把握语言层面上的各种句式的差异.典型的致使力决定了原型致使句的生成.     

Cause and Event: Supporting Causal Claims through Logistic Models

Efforts to identify and support credible causal claims have received intense interest in the research community, particularly over the past few decades. In this paper, we focus on the use of statistical procedures designed to support causal claims for a treatment or intervention when the response variable of interest is dichotomous. We identify seven key features of logistic regression studies that should play a critical role in estimating a causal effect and discuss their implications for causal inference. These include elaboration of research design, clarification of link function, model specification, challenges and limitations of sample size, interpretation of treatment effect through odds ratios, statistical tests and examination of model fit, and the potential for multilevel logistic models in pursuit of causal claims. Our recommendations are intended to guide researchers in the critical evaluation of logistic regression models for analyses culminating in causal claims and to promote stronger design and modeling strategies for reliable causal inference.     

客观归责理论的归责标准研究

客观归责理论是解决结果归属的重要理论,其在过失犯中应用居多。客观归责的两个步骤,首先是在结果和法所不允许的风险之间存在因果关系,其次是在这一因果流程未被阻断或改变的前提下,损害结果在规范目的的范围内,满足这两个条件的行为人才具有责任。客观归责理论所确立的归责流程致力于限制责任范围,将那些没有刑法规范意义的因果流程从责任范围内剔除出去,旨在通过一系列规则体系限制人们因自己的行为招致的外部风险承担。     

从逻辑学到心理学——归纳推理的心理学意义初探

逻辑学对归纳推理的理论阐述有诸多困难,如逻辑学对归纳推理的理论界定本身就包含着矛盾、逻辑学也没有在归纳推理和类比推理之间作出区分、逻辑学希望为归纳推理制定规则的努力也被证明是行不通的等。而心理学对归纳推理的研究则采取了和逻辑学的研究不同的策略,如心理学对归纳推理作了精确的定义、心理学描述归纳推理的内在心理规律而不是制定规则、心理学也在努力建立归纳推理的理论模型等。     

英语学习成败归因调查研究

文章以320名非英语专业大二学生为研究对象,对研究对象英语学习成败归因倾向及归因倾向与英语学习成绩之间的关系进行了研究。不同英语水平的学生在成功归因上的差异不明显,但对失败的归因在学习环境、运气好坏、语言天赋和任务难度等方面都有显著差异,学生对学习成败的不同归因倾向对其学习行为和学习成绩都有很大的影响。     

Exploring a latent cause theory of classical conditioning

We frame behavior in classical conditioning experiments as the product of normative statistical inference. According to this theory, animals learn an internal model of their environment from experience. The basic building blocks of this internal model are latent causes-explanatory constructs inferred by the animal that partition observations into coherent clusters. Generalization of conditioned responding from one cue to another arises from the animal's inference that the cues were generated by the same latent cause. Through a wide range of simulations, we demonstrate where the theory succeeds and where it fails as a general account of classical conditioning.     

含有预设的推理与推理的有效性

推理的全部前提可以看作这一推理的预设。日常的具体推理往往是省略的推理，有些前提并未明确给出，在这种情况下，对预设作全面的分析就尤为重要。与此相关，考察经典逻辑和模态逻辑中的某些推理规则，讨论这些推理规则在日常推理中的有效性问题，是十分必要的。     

Using causal models to bridge the divide between big data and educational theory

An extraordinary amount of data is becoming available in educational settings, collected from a wide range of Educational Technology tools and services. This creates opportunities for using methods from Artificial Intelligence and Learning Analytics (LA) to improve learning and the environments in which it occurs. And yet, analytics results produced using these methods often fail to link to theoretical concepts from the learning sciences, making them difficult for educators to trust, interpret and act upon. At the same time, many of our educational theories are difficult to formalise into testable models that link to educational data. New methodologies are required to formalise the bridge between big data and educational theory. This paper demonstrates how causal modelling can help to close this gap. It introduces the apparatus of causal modelling, and shows how it can be applied to well-known problems in LA to yield new insights. We conclude with a consideration of what causal modelling adds to the theory-versus-data debate in education, and extend an invitation to other investigators to join this exciting programme of research.

Practitioner notes

What is already known about this topic

• ‘Correlation does not equal causation’ is a familiar claim in many fields of research but increasingly we see the need for a causal understanding of our educational systems.
• Big data bring many opportunities for analysis in education, but also a risk that results will fail to replicate in new contexts.
• Causal inference is a well-developed approach for extracting causal relationships from data, but is yet to become widely used in the learning sciences.

What this paper adds

• An overview of causal modelling to support educational data scientists interested in adopting this promising approach.
• A demonstration of how constructing causal models forces us to more explicitly specify the claims of educational theories.
• An understanding of how we can link educational datasets to theoretical constructs represented as causal models so formulating empirical tests of the educational theories that they represent.

Implications for practice and/or policy

• Causal models can help us to explicitly specify educational theories in a testable format.
• It is sometimes possible to make causal inferences from educational data if we understand our system well enough to construct a sufficiently explicit theoretical model.
• Learning Analysts should work to specify more causal models and test their predictions, as this would advance our theoretical understanding of many educational systems.

     

具有稳定结构的模糊逻辑模型

本文利用多边矩阵理论定义的正交性及对称性,给出了一种具有双因果关系的模糊稳定性逻辑分析模型,这种稳定的逻辑分析模型结构清晰、简单,并且可用于解决许多复杂问题.     

The effect of implicit theories on raters' inference in performance judgment: Consequences for the validity of student ratings of instruction

Performance judgment is a situation of incomplete information where raters' inference would play an important role. Consequently, the schematic nature of human cognition may introduce implicit personality theory bias in performance judgment. To demonstrate this, a causal model of performance rating judgment was framed from the theories of person perception and social cognition. The model yielded a good fit to the data obtained from a performance rating task where the availability of performance information was manipulated. The results supported the hypotheses that student raters' inferences are partly contaminated by their implicit theories of a good instructor. Student raters inferred traits and behaviors and provided ratings for corresponding items even when the instructor behavior was limited to a subset of performance data only. The findings imply that one aspect of invalidity in student ratings of instructors is the bias in human inference due to the implicit theories of effective instructional behavior.     

What Can We Learn About Effective Early Mathematics Teaching? A Framework for Estimating Causal Effects Using Longitudinal Survey Data

Abstract

This study investigates the impact of teacher characteristics and instructional strategies on the mathematics achievement of students in kindergarten and first grade and tackles the question of how best to use longitudinal survey data to elicit causal inference in the face of potential threats to validity due to nonrandom assignment to treatment. We develop a step-by-step approach to selecting a modeling and estimation strategy and find that teacher certification and courses in methods of teaching mathematics have a slightly negative effect on student achievement in kindergarten, whereas postgraduate education has a positive effect in first grade. Various teaching modalities, such as working with counting manipulatives, using math worksheets, and completing problems on the chalkboard, have positive effects on achievement in kindergarten, and pedagogical practices relating to explaining problem solving and working on problems from textbooks have positive effects on achievement in first grade. We show that the conclusions drawn depend on the estimation and modeling choices made and that several prior studies of teacher effects using longitudinal survey data likely neglected important features needed to establish causal inference.     

连词“缘”的语法化及相关问题考察

"缘"是中古时期新兴的一个表因连词,它的语法化经历了一个由表"凭借、依据"义的动词到工具介词再到表因连词的过程。这个语法化之所以能够完成,内部因素和外部因素的共同作用是一个不可或缺的重要条件。而且这种演变完全是在汉语内部完成的,与语言接触无关。     

具有两种因果关系模糊逻辑分析模型的稳定性结构

本文利用由多边矩阵理论定义的正交性及对称性,提出了一种具有两种因果关系的模糊稳定性逻辑分析模型,这种稳定的逻辑分析模型结构清晰、简单,可用于解决许多复杂问题.     

Avoiding Bias in Randomised Controlled Trials in Educational Research

Randomised controlled trials (RCTs) are often seen as the 'gold standard' of evaluative research. However, whilst randomisation will ensure comparable groups, trials are still vulnerable to a range of biases that can undermine their internal validity. In this paper we describe a number of common threats to the internal validity of RCTs and methods of countering them. We highlight a number of examples from randomised trials in education and health care where problems of execution and analysis of the RCT has undermined their internal validity. However, awareness of these potential biases can lead to careful planning to avoid or reduce their occurrence. If good quality randomised trials are to inform policy and practice in education then rigorous trials need to be designed that are the least susceptible to threats to their validity.     

大卫·刘易斯论因果性

现代哲学对于因果概念的讨论受惠于休谟,休谟关于因果概念有不同的定义。他除了从相继性、接近性和恒常联系三个特点定义因果关系,还从某种反事实条件关系描述了因果概念。大卫·刘易斯从事件之间的反事实依赖关系对因果性进行了细致分析,体现了剖析因果性概念的新视角,同时也充分表明大卫·刘易斯的模态实在论立场和反事实条件句逻辑在其系统哲学建构中的解释力。     

The impact of internal migration on educational outcomes: Evidence from Turkey

Similar to the relation between the inflows of immigrants and educational outcomes that are found in immigration studies, the spatial distribution of internal migrants within a given country also may influence educational outcomes, at least in the short run. This could be particularly true in Turkey, where inter-provincial mobility is high and where striking differences in educational resources and therefore educational success across regions persist. Using the 1990 and 2000 Turkish Censuses, this study exploits variations over time in the inflow of internal migrants across provinces to identify the causal effect of internal migration on natives’ educational outcomes. The evidence suggests that the inflow of migrants lowers natives’ completion rates for middle school and high school. Evidence also indicates that while the negative effects appear to be greater among native children from low-SES households, native high-SES households are able to mitigate these adverse effects for their children. Furthermore, the estimated effects exhibit some differences by children's gender and migrant status.