Statistics Assignment代写:近代历史
ASSIGNMENT代写

Statistics Assignment代写:近代历史

2017-02-15 12:43

本章捆绑各种类似的研究与建模反应多元多层框架。作为开始,本章首先阐明近代历史的单变量分析技术在多级分类数据上下文。然后它逐渐提出了文学上可用拟合多元多级模型分类和连续数据。更将在本章回顾了证据缺失值部分观察多元多级数据集。 多元多级模型可以被认为是一组多个因变量在自然层次结构。虽然多变量分析增加了复杂性在多级上下文中,它是一个重要的工具,促进执行一个测试的一些解释变量的联合效应在多个因变量(Snijders &丛林(2000)。这些模型有增加的力量建构效度的分析在现实世界中复杂的概念。考虑一个学校可以测量的有效性研究三种不同的输出变量数学成绩,阅读能力在学校和幸福。这些数据收集学生聚集在学校通过暗示一个层次的人自然。虽然当然可以单独处理三个结果,它无法显示整体对学校效能。因此多变量分析在这些类型的场景中会更可取,因为它有能力减少1型误差和提高统计能力(Maeyer、Rymenans Petegem和马瑞医生)(草案)》。 分层性质的多变量模型不像单变量响应模型。让我们关注上面示例;它意味着两层多变量模型。但在现实中它有三个层次。在这种情况下,测量是一级单位,二级单位和学校的学生水平三个单位。

Statistics Assignment代写:近代历史

This chapter tying up the various similar studies related to modeling responses multivariately in a multilevel frame work. As a start, this chapter begins by laying out the recent history of univariate techniques for analyzing categorical data in a multilevel context. Then it gradually presents the literature available on fitting multivariate multilevel models for categorical and continuous data. More over this chapter reviews the evidence for imputing missing values for partially observed multivariate multilevel data sets.
A multivariate multilevel model can be considered as a collection of multiple dependent variables in a hierarchical nature. Though the multivariate analysis increases the complexity in a multilevel context, it is an essential tool which facilitates to carry out a single test of the joint effects of some explanatory variables on several dependent variables (Snijders & Bosker (2000). These models have the power of increasing the construct validity of the analysis for complex concepts in the real world. Consider a study on school effectiveness which can be measured on three different output variables math achievement, reading proficiency and well-being at school. These data are collected on students those who are clustered within schools by implying a hierarchical nature. Although it is certainly possible to handle three outcomes separately, it is unable to show the overall picture about school effectiveness. Therefore multivariate analysis would be more preferable in these types of scenarios since it has the capability of decreasing the type 1 error and increasing the statistical power (Maeyer, Rymenans, Petegem and Bergh) (Draft).
Hierarchical natures of multivariate models are not like as the univariate response models. Let us focus on above example; it implies a two level multivariate model. But in reality it has three levels. In this case, the measurements are the level 1 units, the students the level 2 units and the schools the level three units.
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