The role of secondary covariates when estimating latent trait population distributions

The U.S. National Assessment of Educational Progress (NAEP), the Third International Mathematics and Science Study (TIMSS), and the U.S. Adult Literacy Survey collect probability samples of students (or adults) who are administered brief examinations in subject areas such as mathematics and reading (cognitive variables), along with background demographic (primary) and educational environment (secondary) questions. The demographic questions are used in the primary reporting, while the numerous explanatory secondary variables, or covariates, are only directly utilized in subsequent secondary analyses. The covariates are also used indirectly to create the plausible values (multiple imputations) that are an integral part of analyses because of the use of sparse matrix sampling of cognitive items. The improvement in the precision of the primary reporting due to the inclusion of the covariates is assessed here and contrasted with the precision of reporting using plausible values created using only the primary demographic variables.The results demonstrate that the improvement in precision depends on the matrix sampling designs for the cognitive assessments. The improvements range from essentially none for the most common designs, to moderate for some less common designs. Consequently, two potential changes in the reporting procedures that could improve the statistical and operational efficiency of primary reporting are (a) eliminate or reduce the collection of covariates and increase the number of cognitive items, (b) to avoid delays, eliminate the covariates from the creation of plausible values used for the primary reports, but include them later when creating public-use files for secondary analyses. The potential improvements in statistical and operational efficiency must be weighed against the intrinsic interest in the covariates, and the potential for small discrepancies in the primary and secondary reporting.Thanks to Donald Rubin, Robert Mislevy, and John Barnard for their helpful comments and computing assistance. This work was supported by NCES Grant 84.902B980011.     

Divergence Behaviour of the Successive Geometric Mean Method of Pairwise Comparison Matrix Generation for a Multiple Stage,Multiple Objective Optimization Problem

This paper focuses on the divergence behaviour of the successive geometric mean (SGM) method used to generate pairwise comparison matrices while solving a multiple stage, multiple objective (MSMO) optimization problem. The SGM method can be used in the matrix generation phase of our three‐phase methodology to obtain pairwise comparison matrix at each stage of an MSMO optimization problem, which can be subsequently used to obtain the weight vector at the corresponding stage. The weight vectors across the stages can be used to convert an MSMO problem into a multiple stage, single objective (MSSO) problem, which can be solved using dynamic programming‐based approaches. To obtain a practical set of non‐dominated solutions (also referred to as Pareto optimal solutions) to the MSMO optimization problem, it is important to use a solution approach that has the potential to allow for a better exploration of the Pareto optimal solution space. To accomplish a more exhaustive exploration of the Pareto optimal solution space, the weight vectors that are used to scalarize the MSMO optimization problem into its corresponding MSSO optimization problem should vary across the stages. Distinct weight vectors across the stages are tied directly with distinct pairwise comparison matrices across the stages. A pairwise comparison matrix generation method is said to diverge if it can generate distinct pairwise comparison matrices across the stages of an MSMO optimization problem. In this paper, we demonstrate the SGM method's divergence behaviour when the three‐phase methodology is used in conjunction with an augmented high‐dimensional, continuous‐state stochastic dynamic programming method to solve a large‐scale MSMO optimization problem. Copyright © 2013 John Wiley & Sons, Ltd.     

儿童青少年内外化心理病理问题“共发”现象研究现状与展望

儿童青少年内外化心理病理问题存在共发现象,已受到发展心理学和心理病理学研究者的广泛关注。本文对当前关于儿童青少年内外化问题的描述性研究和解释性研究进行了梳理,对儿童青少年共发现象发生率、发展特点、预测因素和发展结果等方面的重要研究发现进行系统性介绍。最后,基于发展级联和个体与环境因素交互作用视角,对未来本领域的研究进行展望,以期系统、完整地揭示内外化问题共发的原因。     

A Conceptual Toolkit for Intensive Early Behavioral Intervention Teachers

Intensive early behavioral intervention (IEBI) for children with autism requires effective team work involving professional therapists, parents, teachers and others. It is important for all team members to employ a common conceptual and procedural vocabulary so that communication among them is clear, unambiguous, and effective. This article outlines the rationale of a small and practical vocabulary or conceptual toolkit that we have found useful in working with teachers and families in designing, implementing, and analyzing the details of behavioral programs to help autistic children learn to speak and understand language. Special attention is given to a heuristic device called the language matrix. Illustrative data describing the course of generative language acquisition by a child with autism taught according to the language matrix approach are included.     

A discriminated similarity matrix construction based on sparse subspace clustering algorithm for hyperspectral imagery

The clustering of hyperspectral images is a challenging task because of the high dimensionality of the data. Sparse subspace clustering (SSC) algorithm is one of the popularly used clustering algorithm for high dimensionality data. However, SSC has not fully used the spectral and spatial information during similarity matrix construction based on single sparse representation coefficient for hyperspectral Imagery (HSI) clustering. In this paper, two novel similarity matrix construction methods named as Cosine-Euclidean similarity matrix (abbreviated as CE) and Cosine-Euclidean dynamic weighting similarity matrix (abbreviated as CEDW) are proposed for HSI clustering. They can combine the high spectral information and rich spatial information. Firstly, CE utilizes the cosine similarity of spectral information based on overall sparse representation vectors and classical Euclidean distance of spatial information to construct a novel similarity matrix. Secondly, inheriting CE merits, dynamic weighting adjustment method is introduced to CEDW for some external influence factors to the HSI information. Several experiments on HSI demonstrated that the proposed algorithms are effective for HSI clustering.     

认知诊断中的Q矩阵及其作用

Q矩阵作为连接认知和测量的桥梁，在认知诊断中起重要作用。本文梳理了应用Q矩阵解决认知诊断相关问题的理论与方法。首先整理Q矩阵的相关概念、算法、性质及其在认知诊断中的作用；并根据Q矩阵可计算理论构念效度、可以构成格等，指出Q矩阵是特殊的关联矩阵；接着介绍Q矩阵理论研究方面的几个近期发展；并对Q矩阵未来的应用研究作出展望。期望本文能为测量工作者更灵活地利用Q矩阵提供参考和帮助。     

Behaviors of ADHD and Peer Relationship Difficulties in Chinese and American Youths: Role of Co-Occurring Behaviors of Depression and Anxiety

Abstract

The co-occurrence of behaviors of attention-deficit/hyperactivity disorder (ADHD) with behaviors of anxiety or behaviors of depression is the norm, but little is known on how the co-occurrence accounted for youths’ peer relations. The authors report results on difficult peer relations in relation to behaviors of ADHD, co-occurring behaviors of depression, and behaviors of anxiety from three studies on 862 youths in China and in the United States. Study 1 included 313 ethnically and socioeconomically diverse American youths; Study 2 included 250 youths who were adopted out of Chinese orphanages by American parents; and Study 3 included 299 youths from Beijing, China. Data on difficult peer relations and behaviors of ADHD, depression, and anxiety were collected with the third edition of Behavior Assessment System for Children-Self Report of Personality. In all three studies, each type of problems alone significantly predicted difficult peer relations, but behaviors of ADHD were not significant when co-occurring behaviors of depression or co-occurring behaviors of anxiety were considered. Despite that the youths in our study had different cultural and personal backgrounds, there was no evidence that behaviors of ADHD were detrimental to youths’ peer relations when behaviors of depression or anxiety were considered. Implications for intervention were discussed.