儿童汉字读音声旁一致性意识的发展

采用同音判断的方法,考察儿童对汉字读音声旁一致性意识的发展。一个熟悉字和一个不熟悉字共用同一个声旁,该声旁在提示整字发音时可以是一致的,即包含此声旁的所有汉字都发音相同,也可以是不一致的。要求儿童猜测不熟悉字的读音,作出它与熟悉字“是”、“否”同音的判断。结果发现,儿童很早就意识到汉字的结构以及声旁和形旁在表音、表义功能上的分工。随着年级的升高,声旁一致性对猜测不熟悉汉字读音的影响增强。小学四年级语文能力较高的儿童已经开始意识到声旁的一致性;六年级儿童总体上说来已发展了一致性意识。初二年级的学生发展了声旁一致性意识,且没有能力差异。大学生的声旁一致性意识最强。     

Troubles with Theoretical Virtues: Resisting Theoretical Utility Arguments in Metaphysics

In this paper we examine theoretical utility arguments in metaphysics. While philosophers claim a procedural continuity with science when using such arguments, we argue that examining famous instances from the history of science expose their fundamental flaws. We find that arguments from theoretical utility invoke considerations that are not truth conducive and that justifications for claims that a theory possesses theoretical virtues often assume the truth of the theory such virtues are supposed to support. We conclude that theoretical utility arguments provide no epistemic grounds for metaphysical inquiry.     

自我概念一致性与幸福感间的关系：文化的调节作用

自我概念一致性已经被证明对于享乐的和自我实现的幸福感具有促进作用。然而文化可以影响它们之间的关系。西方人崇尚个人主义, 具有独立型自我构念和低水平的辩证思维, 因此他们的自我概念一致性较高, 且对幸福感具有强烈的促进作用。而东方人崇尚集体主义, 具有互依型自我构念和高水平的辩证思维, 因此他们的自我概念一致性较低, 且对幸福感的促进作用微弱。未来的研究需要在特质以外的人格水平上, 检验不同分类标准下的自我概念一致性与文化和多种类型的幸福感间的关系。并且探索文化发挥作用的其他机制, 解析文化与自我概念相互作用, 共同影响幸福感的深层机制。     

Bootstrap区间估计在认知诊断模型误设中的应用

认知诊断模型选择是认知诊断评估中重要研究问题之一。在实际应用中实践者并不知道真正拟合数据的模型,通常会用模型拟合指标检验模型与数据的拟合程度。从测量结果质量来看,除保证模型与数据拟合之外,还需要重点评价模型诊断结果的信度和效度等。考虑到以往研究大都采用基于信息量的拟合指标去判定模型与数据的匹配性,本研究提出综合考虑模型拟合指标与信度指标用于模型选择或评价模型误设。考虑实验因素为真实模型或分析模型（DINA模型、G-DINA模型、R-RUM模型）、样本量、题量和属性个数,在五因素（3×3×2×2×2）实验设计条件下,比较Bootstrap区间估计的属性分类一致性信度平均数与标准误和常用的拟合统计量-2LL、AIC、BIC对正确模型的选择率。结果表明：-2LL在题目数量多的情况下表现较好,而AIC、BIC在被试量较大的情况下表现较好,在不同的研究条件下,-2LL、AIC、BIC的模型选择率很不稳定,而用Bootstrap法估计的属性分类一致性信度平均数和标准误在不同研究条件的模型选择率较稳定,总体表现较好。     

On Consistent Subsets of Large Sets of Satisfiable Sentences

We extend some results of Adam Kolany to show that large sets of satisfiable sentences generally contain equally large subsets of mutually consistent sentences. In particular, this is always true for sets of uncountable cofinality, and remains true for sets of denumerable cofinality if we put appropriate bounding conditions on the sentences. The results apply to both the propositional and the predicate calculus. To obtain these results, we use delta sets for regular cardinals, and, for singular cardinals, a generalization of delta sets. All of our results are theorems in ZFC.     

Multilevel Modeling with Correlated Effects

When there exist omitted effects, measurement error, and/or simultaneity in multilevel models, explanatory variables may be correlated with random components, and standard estimation methods do not provide consistent estimates of model parameters. This paper introduces estimators that are consistent under such conditions. By employing generalized method of moments (GMM) estimation techniques in multilevel modeling, the authors present a series of estimators along a robust to efficient continuum. This continuum depends on the assumptions that the analyst makes regarding the extent of the correlated effects. It is shown that the GMM approach provides an overarching framework that encompasses well-known estimators such as fixed and random effects estimators and also provides more options. These GMM estimators can be expressed as instrumental variable (IV) estimators which enhances their interpretability. Moreover, by exploiting the hierarchical structure of the data, the current technique does not require additional variables unlike traditional IV methods. Further, statistical tests are developed to compare the different estimators. A simulation study examines the finite sample properties of the estimators and tests and confirms the theoretical order of the estimators with respect to their robustness and efficiency. It further shows that not only are regression coefficients biased, but variance components may be severely underestimated in the presence of correlated effects. Empirical standard errors are employed as they are less sensitive to correlated effects when compared to model-based standard errors. An example using student achievement data shows that GMM estimators can be effectively used in a search for the most efficient among unbiased estimators. This research was supported by the National Academy of Education/Spencer Foundation and the National Science Foundation, grant number SES-0436274. We thank the editor, associate editor, and referees for detailed feedback that helped improve the paper.     

Reference-dependent utility with shifting reference points and incomplete preferences

Many empirical studies have shown that people's preferences are reference-dependent. Previous theoretical studies of reference-dependence assumed that the reference point was fixed and then imposed the usual assumptions of decision theory, in particular completeness of preferences. This paper gives preference foundations for additive reference-dependent utility when the reference point varies across decisions and is one of the options in the decision maker's opportunity set. This decision situation is common, for example because usually the retention of the status quo is an available option, but is difficult to handle axiomatically because it implies incompleteness of preferences. The results of this paper provide tools to extend existing theories of reference-dependent preferences, such as prospect theory, to new and empirically important decision contexts.     

The Temporal Consistency of Personality Effects: Evidence from the British Household Panel Survey

Personality traits have been posited to function as stable influences on political attitudes and behavior. Although personality traits themselves exhibit high levels of temporal stability, it is not yet known whether the effects of these traits are marked by comparable temporal consistency. To address this question, this research note examines data from Wave 13 (2003–2004), Wave 15 (2005–2006) and Wave 17 (2007–2008) of the British Household Panel Survey (BHPS). Twenty‐seven behavioral and 14 attitudinal dependent variables are studied. Consistency of effects is gauged via a series of multilevel models in which personality effects are permitted to vary by year. High levels of temporal consistency are observed for personality traits as represented by the Big Five framework.     

Heuristic cognitive diagnosis when the Q‐matrix is unknown

Cognitive diagnosis models of educational test performance rely on a binary Q‐matrix that specifies the associations between individual test items and the cognitive attributes (skills) required to answer those items correctly. Current methods for fitting cognitive diagnosis models to educational test data and assigning examinees to proficiency classes are based on parametric estimation methods such as expectation maximization (EM) and Markov chain Monte Carlo (MCMC) that frequently encounter difficulties in practical applications. In response to these difficulties, non‐parametric classification techniques (cluster analysis) have been proposed as heuristic alternatives to parametric procedures. These non‐parametric classification techniques first aggregate each examinee's test item scores into a profile of attribute sum scores, which then serve as the basis for clustering examinees into proficiency classes. Like the parametric procedures, the non‐parametric classification techniques require that the Q‐matrix underlying a given test be known. Unfortunately, in practice, the Q‐matrix for most tests is not known and must be estimated to specify the associations between items and attributes, risking a misspecified Q‐matrix that may then result in the incorrect classification of examinees. This paper demonstrates that clustering examinees into proficiency classes based on their item scores rather than on their attribute sum‐score profiles does not require knowledge of the Q‐matrix, and results in a more accurate classification of examinees.     

非参数认知诊断方法下诊断结果的概率化表征

非参数认知诊断分类方法非常适合课堂评估,其诊断结果采用0-1形式而缺乏概率化表征,不能精细地区分被试属性掌握程度的差异或变化,还缺乏可用于评价真实测验分类结果的信度和效度指标。要刻画被试属性掌握程度的差异,首要的问题是要为非参数认知诊断方法提供一种可以量化属性掌握概率的方法。针对此问题,基于二项分布和玻尔兹曼分布提出非参数认知诊断方法下诊断结果的概率化表征方法,并用于构建分类准确性和分类一致性指标。模拟研究与实测数据分析结果显示：概率化表征方法与非参数认知诊断方法的分类结果高度一致;概率化表征方法与认知诊断模型所得的属性掌握概率十分接近;概率化表征方法所得的属性（模式）掌握概率可用于计算属性（模式）分类准确性和分类一致性指标,在实际测验情景下可作为信度和效度指标,评价诊断结果的重测一致率和判准率。