老年羽毛球运动的健身价值与锻炼方法

分析了羽毛球运动的特点与老年羽毛球运动的现状，着重结合老年人身体生理特征论述了该项目健身的主要技术和锻炼方法，对老年健身有一定的指导作用。     

中国太极拳健康文化系统的研究

从构成要素、结构功能、技理与文化举隅等方面阐释了中国太极拳文化系统的3个子系统,即运动健身、运动健心和运动养生,每个子系统分别包含5个构成要素。认为:运动健身子系统主要适合青少年、中年人群;运动养生子系统主要适合老、弱、病人群;运动健心子系统连接其他两个子系统。指出:运动健身子系统与运动养生子系统非截然割裂,只是适宜对象侧重不同。     

学校体育辐射对家庭健身的影响

运用问卷调查法、实验法,对苏州市275名中学生的家庭健身情况进行调查,发现尽管每个家庭都有一定的健身器材,但参与体育活动的比例不是很高。针对这一现象,设计了辐射活动内容,意在让学生把在体育课、校园网上所掌握的体育知识、技术技能,带给自己的家庭,以此来达到“一个学生带动一个家庭”的健身辐射行动。实验结果表明:通过辐射活动,使学校体育有效地为家庭服务,从而为社会服务,起到了积极效应,充分体现了实施《全民健身计划纲要》的重要性。此活动,不仅提高了家庭的整体健康,而且促进了家庭成员之间的交流,有利于中学生的健康成长。     

高技术企业智力资本IPO-M系统价值配适性分析

从系统论及共变配合论的角度出发,在重构智力资本系统结构基础上,验证智力系统配适度及其对企业价值的解释力。通过模型设定表明,智力资本系统配适度对价值增值有很强的解释力,该结论为企业及其利益相关者进行智力资本系统管理、投资与决策提供适用的工具。     

关系嵌入、结构嵌入与知识整合效能:人—环境匹配视角的分析框架

社会网是研究知识管理的重要视角,以往研究多从正面效应进行探讨,而忽略了个体因素所发挥的能动作用。因此,从社会网的关系嵌入和结构嵌入两个方面,构建了一个更加均衡的模型,论述了社会网在促进知识整合与合作满意度的同时,也存在增加角色负荷的潜在消极作用。从人—环境匹配角度强调了个体与社会网的互动机制,揭示了个体通过能力和价值观的调整,使得社会网对知识整合的作用得以实现的过程。     

基于二叉排序树的最佳适应算法

当前操作系统在管理内存时,常采用最佳适应算法对空闲内存块进行分配,但该算法存在效率不高、时空消耗大的缺点,对此提出基于二又排序树的最佳适应算法,改变原有的最佳适应算法中把所有空闲分区按容量大小顺序连接成空闲分区链的特点,而把所有空闲分区组建成一颗二叉排序树,进程发出请求时,根据二叉排序树的性质依次查找满足条件的空闲分区,并在分配后重组二叉排序树,保证二叉排序树的结构不被破坏,改善现有的最佳适应算法在查找过程中的效率问题.     

Validation of the child sex abuse attitude scale through confirmatory factor analysis

A validation study was conducted on the Child Sex Abuse Attitude Scale (CSAAS) using confirmatory factor analysis (CFA) to examine factor structure. The CSAAS was developed based on Festinger's (1957) theory of attitude development resulting in a 4‐factor first‐order structure (cognition, value, affect, and behavior) and a single‐factor 2nd‐order structure (attitude). A sample of 215 school psychologists, members of the National Association of School Psychologists, responded to the CSAAS survey. CFA results supported the hypothesized factor structure of the CSAAS, thus indicating the plausibility of a 4‐factor 1st‐order and a single‐factor higher order structure of the CSAAS.     

Detecting Misspecification in Mean Structures for Growth Curve Models: Performance of Pseudo R 2s and Concordance Correlation Coefficients

This study examined the performance of 4 correlation-based fit indexes (marginal and conditional pseudo R ²s; average and conditional concordance correlations) in detecting misspecification in mean structures in growth curve models. Their performance was also compared to that of 4 traditional SEM fit indexes. We found that the marginal pseudo R ² and average concordance correlation were able to detect misspecification in the marginal mean structure (average change trajectory). The conditional pseudo R ² and concordance correlation could detect misspecification when it occurred in the conditional mean structure (individual change trajectory) or in both mean structures. Compared to the SEM fit indexes, the correlation-based fit indexes were more robust to sample size but were less robust to data properties such as magnitude of population mean and measurement error. Theoretical and practical implications of the results and directions for future research are discussed.     

Mixed Mode Latent Class Analysis: An Examination of Fit Index Performance for Classification

This Monte Carlo study examines the performance of fit indices commonly used by applied researchers interested in finite mixture modeling for the purposes of classification. Conditions for the simulation study were selected to reflect conditions found in applied educational and psychological research. The factors included in the investigation were metric level of indicators, sample size, and class prevalence. All models contained a combination of categorical and continuous indicators. All categorical indicators were dichotomous, and continuous indicators were normally distributed. The fit indices examined were Akaike’s information criterion, Bayesian information criterion (BIC), sample size-adjusted Bayesian information criterion (SSBIC), integrated classification likelihood criterion with Bayesian-type approximation, and Lo–Mendell–Rubin likelihood ratio test. Overall, SSBIC tended to identify the correct solution with higher frequency than other indices. BIC tended to identify the correct solution with higher frequency than the other indices in models with more continuous than categorical indicators, or when rare classes were absent.     

Combining IRT and SEM: A Hybrid Model for Fitting Responses and Response Certainties

This article proposes a model-based procedure, intended for personality measures, for exploiting the auxiliary information provided by the certainty with which individuals answer every item (response certainty). This information is used to (a) obtain more accurate estimates of individual trait levels, and (b) provide a more detailed assessment of the consistency with which the individual responds to the test. The basis model consists of 2 submodels: an item response theory submodel for the responses, and a linear-in-the-coefficients submodel that describes the response certainties. The latter is based on the distance-difficulty hypothesis, and is parameterized as a factor-analytic model. Procedures for (a) estimating the structural parameters, (b) assessing model–data fit, (c) estimating the individual parameters, and (d) assessing individual fit are discussed. The proposal was used in an empirical study. Model–data fit was acceptable and estimates were meaningful. Furthermore, the precision of the individual trait estimates and the assessment of the individual consistency improved noticeably.