Short-range ordering of heavy-element columns in nickel-based superalloys

In order to obtain comprehensive performance, heavy elements were added into superalloys for solid-solution hardening. It is found that, rather than adopting random distributions, the heavy atoms prefer to form short-range ordered columns with heavy-element stripes 1–2 nm in length. This is supported by our ab initio calculations. Owing to the strong bonding strength between the refractory elements and nickel atoms, this short-range ordering may be beneficial to the mechanical performances of superalloys.     

Recursive Partitioning to Identify Potential Causes of Differential Item Functioning in Cross-National Data

Differential item functioning (DIF) assessment is key in score validation. When DIF is present scores may not accurately reflect the construct of interest for some groups of examinees, leading to incorrect conclusions from the scores. Given rising immigration, and the increased reliance of educational policymakers on cross-national assessments such as Programme for International Student Assessment, Trends in International Mathematics and Science Study, and Progress in International Reading Literacy Study (PIRLS), DIF with regard to native language is of particular interest in this context. However, given differences in language and cultures, assuming similar cross-national DIF may lead to mistaken assumptions about the impact of immigration status, and native language on test performance. The purpose of this study was to use model-based recursive partitioning (MBRP) to investigate uniform DIF in PIRLS items across European nations. Results demonstrated that DIF based on mother's language was present for several items on a PIRLS assessment, but that the patterns of DIF were not the same across all nations.     

Parametrized preference structures and some geometrical interpretation

Order structures such as linear orders, semiorders and interval orders are often used to model preferences in decision-making problems. In this paper we introduce a family of preference structures where the mutual indifference threshold belongs to a specific family parametrized by extended reals α. This family includes interval orders (α=1), tangent circle orders (α=0) and a new preference structure called ‘diamond order’ (α=−∞). All these preference relations present an asymmetric part which is shown to be always quasi-transitive and to be transitive for α > 1. Diamond orders present ‘forbidden configurations’ which can occur in the case of tangent circle orders. © 1997 John Wiley & Sons, Ltd.     

On measurement properties of continuation ratio models

Three classes of polytomous IRT models are distinguished. These classes are the adjacent category models, the cumulative probability models, and the continuation ratio models. So far, the latter class has received relatively little attention. The class of continuation ratio models includes logistic models, such as the sequential model (Tutz, 1990), and nonlogistic models, such as the acceleration model (Samejima, 1995) and the nonparametric sequential model (Hemker, 1996). Four measurement properties are discussed. These are monotone likelihood ratio of the total score, stochastic ordering of the latent trait by the total score, stochastic ordering of the total score by the latent trait, and invariant item ordering. These properties have been investigated previously for the adjacent category models and the cumulative probability models, and for the continuation ratio models this is done here. It is shown that stochastic ordering of the total score by the latent trait is implied by all continuation ratio models, while monotone likelihood ratio of the total score and stochastic ordering on the latent trait by the total score are not implied by any of the continuation ratio models. Only the sequential rating scale model implies the property of invariant item ordering. Also, we present a Venn-diagram showing the relationships between all known polytomous IRT models from all three classes.     

The person response function as a tool in person-fit research

Item responses that do not fit an item response theory (IRT) model may cause the latent trait value to be inaccurately estimated. In the past two decades several statistics have been proposed that can be used to identify nonfitting item score patterns. These statistics all yieldscalar values. Here, the use of the person response function (PRF) for identifying nonfitting item score patterns was investigated. The PRF is afunction and can be used for diagnostic purposes. First, the PRF is defined in a class of IRT models that imply an invariant item ordering. Second, a person-fit method proposed by Trabin & Weiss (1983) is reformulated in a nonparametric IRT context assuming invariant item ordering, and statistical theory proposed by Rosenbaum (1987a) is adapted to test locally whether a PRF is nonincreasing. Third, a simulation study was conducted to compare the use of the PRF with the person-fit statistic ZU3. It is concluded that the PRF can be used as a diagnostic tool in person-fit research.The authors are grateful to Coen A. Bernaards for preparing the figures used in this article, and to Wilco H.M. Emons for checking the calculations.     

父母依恋对大学生人际适应的影响

应用自编大学生父母依恋问卷、亲子联结问卷、自尊量表、信任他人量表、人际能力问卷、领悟社会支持量表对251名大学生施测,对自编依恋问卷进行构想效度分析,并建立父母依恋影响大学生人际适应的路径模型。结果表明：1）大学生父母依恋问卷构想效度良好;2）父母依恋对人际能力、社会支持感知既有显著的直接效应,又以自尊、信任他人为中介产生间接效应;3）父母依恋对大学生人际适应有显著影响,依恋的内部工作模型理论具有跨文化适应性。     

运动觉、布局无关运动与路径整合

路径整合是空间导航的一个重要子领域。本研究采用了一种三角形完成任务的新变式来研究路径整合问题，发现在剥夺被试的视觉输入的前提下，去除被试部分运动觉后，其绩效有不同水平的下降；同时还发现在编码过程中与路径布局无关的不同方向旋转间并无显著性差异。此外，由于本研究是在室外空间中所做的现场研究，故得到的结果优于室内所做的经典研究，进而验证了前人的一个猜测。     

Maximum Likelihood Methods in Treating Outliers and Symmetrically Heavy-Tailed Distributions for Nonlinear Structural Equation Models with Missing Data

By means of more than a dozen user friendly packages, structural equation models (SEMs) are widely used in behavioral, education, social, and psychological research. As the underlying theory and methods in these packages are vulnerable to outliers and distributions with longer-than-normal tails, a fundamental problem in the field is the development of robust methods to reduce the influence of outliers and the distributional deviation in the analysis. In this paper we develop a maximum likelihood (ML) approach that is robust to outliers and symmetrically heavy-tailed distributions for analyzing nonlinear SEMs with ignorable missing data. The analytic strategy is to incorporate a general class of distributions into the latent variables and the error measurements in the measurement and structural equations. A Monte Carlo EM (MCEM) algorithm is constructed to obtain the ML estimates, and a path sampling procedure is implemented to compute the observed-data log-likelihood and then the Bayesian information criterion for model comparison. The proposed methodologies are illustrated with simulation studies and an example. The research described herein was fully supported by a grant (CUHK 4243/03H) from the Rearch Grants Council of the Hong Kong Special Administration Region. The authors are thankful to the Editor, the Associate Editor, and anonymous reviewers for valuable comments which improve the paper significantly, and are grateful to ICPSR and the relevant funding agency for allowing the use of their data. Requests for reprints should be sent to S. Y. Lee, Department of Statistics, The Chinese University of Hong Kong, Shatin, N. T., Hong Kong.     

A comparative evaluation of factor- and component-based structural equation modelling approaches under (in)correct construct representations

Structural equation modelling (SEM) has evolved into two domains, factor-based and component-based, dependent on whether constructs are statistically represented as common factors or components. The two SEM domains are conceptually distinct, each assuming their own population models with either of the statistical construct proxies, and statistical SEM approaches should be used for estimating models whose construct representations correspond to what they assume. However, SEM approaches have often been evaluated and compared only under population factor models, providing misleading conclusions about their relative performance. This is partly because population component models and their relationships have not been clearly formulated. Also, it is of fundamental importance to examine how robust SEM approaches can be to potential misrepresentation of constructs because researchers may often lack clear theories to determine whether a factor or component is more representative of a given construct. Addressing these issues, this study begins by clarifying several population component models and their relationships and then provides a comprehensive evaluation of four SEM approaches – the maximum likelihood approach and factor score regression for factor-based SEM as well as generalized structured component analysis (GSCA) and partial least squares path modelling (PLSPM) for component-based SEM – under various experimental conditions. We confirm that the factor-based SEM approaches should be preferred for estimating factor models, whereas the component-based SEM approaches should be chosen for component models. Importantly, the component-based approaches are generally more robust to construct misrepresentation than the factor-based ones. Of the component-based approaches, GSCA should be chosen over PLSPM, regardless of whether or not constructs are misrepresented.