Digital Module 16: Longitudinal Data Analysis

In this digital ITEMS module, Dr. Jeffrey Harring and Ms. Tessa Johnson introduce the linear mixed effects (LME) model as a flexible general framework for simultaneously modeling continuous repeated measures data with a scientifically defensible function that adequately summarizes both individual change as well as the average response. The module begins with a nontechnical overview of longitudinal data analyses drawing distinctions with cross-sectional analyses in terms of research questions to be addressed. Nuances of longitudinal designs, timing of measurements, and the real possibility of missing data are then discussed. The three interconnected components of the LME model—(1) a model for individual and mean response profiles, (2) a model to characterize the covariation among the time-specific residuals, and (3) a set of models that summarize the extent that individual coefficients vary—are discussed in the context of the set of activities comprising an analysis. Finally, they demonstrate how to estimate the linear mixed effects model within an open-source environment (R). The digital module contains sample R code, diagnostic quiz questions, hands-on activities in R, curated resources, and a glossary.     

Using the Information Structure Model to Compare Profile-Based Information Filtering Systems

In the IR field it is clear that the value of a system depends on the cost and benefit profiles of its users. It would seem obvious that different users would prefer different systems. In the TREC-9 filtering track, systems are evaluated by a utility measure specifying a given cost and benefit. However, in the study of decision systems it is known that, in some cases, one system may be unconditionally better than another. In this paper we employ a decision theoretic approach to find conditions under which an Information Filtering (IF) system is unconditionally superior to another for all users regardless of their cost and benefit profiles.It is well known that if two IF systems have equal precision the system with better recall will be preferred by all users. Similarly, with equal recall, better precision is universally preferred. We confirm these known results and discover an unexpected dominance relation in which a system with lower recall will be universally preferred provided its precision is sufficiently higher.     

全面建设小康社会与发展经济学

全面建设小康社会意味着中国这个世界上最大的发展中国家向富强、民主、文明迈出了更大的步伐 ;而与此同时 ,作为研究发展中国家经济发展问题的发展经济学却始终面临着学科危机。“全面建设小康社会”这一本土化理论与其所带来的丰富实践 ,为发展经济学的发展注入新的活力     

对我国体育竞赛市场运作环境的分析

随着我国经济体制改革的不断深化和体育产业化的不断扩大,我国的体育竞赛活动由原来的行政指令性计划举办正在向市场运作的方向转换.因此,体育竞赛若要步入市场,就必须研究市场环境,而微观环境则是体育竞赛活动进行市场运作能否成功的关键因素.通过对我国体育竞赛活动的微观环境的构成进行分析,有利于我们对体育竞赛市场化运作的充分认识,从而能够准确把握我国体育竞赛市场运作的一般规律.     

甘肃省县级体育发展的外部环境因素分析

采用专家访谈、文献资料调研、实证研究等方法,对甘肃省县级体育发展的社会、经济诸方面外部环境因素进行分析,旨在对当前甘肃省县级体育现代化发展状况及今后的发展趋势有一个宏观性的把握。结果表明:1)甘肃省县级体育现代化发展的社会经济基础薄弱,总体水平欠发达,且位于黄河以西、黄河以东地区的县差距较大;2)经济水平固然是县级体育事业发展的第一制约因素,这里除了文化氛围和体育意识外,体育地理应该是起决定作用的;3)甘肃省县级体育是一种政府主导型体育,县体育局在县级体育发展中具有很强的放大或收缩效应。     

论城市休闲健身运动的可持续发展

物质生活水平提高后,城市居民的精神生活及健康需求成为休闲健身运动可持续发展的动力;政府和媒介的宣传,有力地促进了全民休闲健身意识的提高;持续稳定的经济发展,给城市休闲健身运动的快速发展奠定了基石。坚持休闲健身运动多元化、趣味化、娱乐化,加速培养休闲健身运动产业的经营、管理以及中介人才是实现我国休闲健身运动健康、快速、可持续发展的重要途径。     

哪些研究问题需要用分层线性模型回答

采用常规的回归分析或方差分析处理行为科学与社会科学研究中经常遇到的嵌套数据，不能满足独立观察的假定，得到的标准误差较小，导致I型错误扩大化，同时，也不利于对不同层次变量不同作用的探讨，分层线性则明确区分数据层次，通过对个体水平变量和组别水平变量的分层综合分析，避免了上述弊病，因而可对个体水平的变量进行更准确的预测和更合理的解释，通过实例介绍了将研究问题与分层线性模型有机结合的方法。     

体育无形资产的经济学分析

阐述体育无形资产的概念,从体育无形资产的授权性及交易的安全性方面分析体育无 形资产的特点,并对体育无形资产的经营管理进行经济学分析。建议我国应进一步完善与体育 无形资产相关的法规政策,从而正确引导我国体育无形资产的经营开发。     

三维影相的新算法——修正的直接线性变换

通过引入摄影机镜头的内、外方位元素的约束条件,导出了一种修正的DLT(MDLT)方法。该方法用于影片和录相的实测,得到一些有意义的结果。当测量的标定控制点点数较少(6～8个),且这些点的位置在空间和成像面分布不均匀时,用MDLT计算的结果比DLT的结果要好,误差可降低80％以上。     

我国行政腐败的制度经济学分析

腐败问题一直是各国政府的一个顽症。也是各国政府都在花大力气去惩治的一个问题。目前，我国政府出台了很多法律法规、政策用以遏制腐败，惩治腐败。但是，腐败现象仍然盛行，腐败官员也层出不穷。应用制度经济学的原理从腐败的成本收益、我国关系经济模式的现状、政府权力界定以及我国在腐败与反腐败问题上的制度不均衡等角度出发。对我国行政腐败的成因予以理论分析，并提出相关对策。