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HLM 丨 分层线性模型软件

HLM是分层线性模型软件,新版本为Version 7.03,适用于Windows 64 bit. 包含线性和非线性部分,可以读取大部份统计软件的数据如 SPSS, SAS, SYSTAT及STATA等等。


HLM处理多层次数据,进行线性和非线性的阶层模型分析。在HLM中,不仅改善了原有的界面,而且增加了新的统计功能。比如对线性模型增加了交叉随机效应;对三层数据增加了多项式模型。该工具能处理多层次数据,进行线性和非线性的阶层模型分析。


HLM程序包能够根据结果变量来产生带说明变量(expl lanatory variable,利用在每层指定的变量来说明每层的变异性)的线性模型.HLM不仅仅估计每一层的模型系数,也预测与每层的每个采样单元相关的随机因子(random effects).虽然HLM常用在教育学研究领域(该领域中的数据通常具有分层结构),但它也适合用在其它任何具有分层结构数据的领域.这包括纵向分析( longitudinal analysis),在这种情况下,在个体被研究时的重复测量可能是嵌套(nested)的.另外,虽然上面的示例暗示在这个分层结构的任意层次上的成员(除了处于高层次的)是嵌套(nested)的,HLM同样可以处理成员关系为"交叉(crossed)",而非必须是"嵌套(nested)"的情况,在这种情况下,一个学生在他的整个学习期间可以是多个不同教室里的成员.   


HLM程序包可以处理连续,计数,序数和名义结果变量(outcome varible),及假定一个在结果期望值和一系列说明变量(explanatory variable)的线性组合之间的函数关系.这个关系通过合适的关联函数来定义,例如identity关联(连续值结果)或logit关联(二元结果)。


HLM 7中的新功能

HLM 7为多级和纵向数据建模提供了前所未有的灵活性。HLM 7具有相同的完整图形化 程序和残留文件以及计算速度,收敛稳健性和用户友好界面,HLM 7重点包括三个处理二进制,计数,序数和多项(标称)的新程序响应变量以及正常理论分层线性模型的连续响应变量:

四级嵌套模型:

用于横截面数据的四级嵌套模型(例如,学校内教室内学生项目响应的模型)。

纵向数据的四级模型(例如,社区内人员内的时间点内的项目)。


四向交叉分类和嵌套混合物:

随着时间的推移,在学校内移动教师的学生,或者按照原籍国和目的地国家进行交叉分类的移民中的项目反应,一再采取措施。

随同时居住在某个社区并就读特定学校的人员采取一再措施。


具有以来随机效应的分层模型:

空间以来的邻域效应。

社交网络互动。

HLM 7还通过使用自适应高斯-厄米特正交(AGH)和高阶拉普拉斯近似来较大的可能性,在估计分层广义线性模型方面提供了新的灵活性。当簇大小较小且方差分量较大时,AGH方法已被证明非常有效。高阶拉普拉斯方法需要更大的簇大小,但允许任意大量的随机效应(当簇大小很大是很重要)。



其它功能:

● 数据的新的图形显示技术

● 大大扩展了拟合模型的图形能力

● 在分层和混合模型中显示带或不带下标的模型等式-方便保存发表。详细的呈现分布假设和关联函数(link function)

● 带有便利的Windows界面的适用于线性模型和非线性关联函数(link function)处理的交叉分类(Cross-classified)随机因子模型

● 在二层分层的广义线性模型(HGLM)中的带EM演算法的适用于稳定收敛(stable convergence)和精确评估的高阶Laplace近似值

●针对三层数据的多项式和序数模型

●方便的从多种其他的软件包中导入数据,包括新版本的SAS,SPSS,和STATA等

●Residual文件能够直接保存成SPSS(.sav)或STATA(.dta)格式文件

●基于MDM文件格式进行分析,替换掉先前的极不灵活的SSM文件格式


英文介绍


In social research and other fields, research data often have a hierarchical structure. That is, the individual subjects of study may be classified or arranged in groups which themselves have qualities that influence the study. In this case, the individuals can be seen as level-1 units of study, and the groups into which they are arranged are level-2 units. This may be extended further, with level-2 units organized into yet another set of units at a third level. Examples of this abound in areas such as education (students at level 1, schools at level 2, and school districts at level 3) and sociology (individuals at level 1, neighborhoods at level 2). It is clear that the analysis of such data requires specialized software. Hierarchical linear and nonlinear models (also called multilevel models) have been developed to allow for the study of relationships at any level in a single analysis, while not ignoring the variability associated with each level of the hierarchy.


The HLM program can fit models to outcome variables that generate a linear model with explanatory variables that account for variations at each level, utilizing variables specified at each level. HLM not only estimates model coefficients at each level, but it also predicts the random effects associated with each sampling unit at every level. While commonly used in education research due to the preva lence of hierarchical structures in data from this field, it is suitable for use with data from any research field that have a hierarchical structure. This includes longitudinal analysis, in which an individual's repeated measurements can be nested within the individuals being studied. In addition, although the examples above implies that members of this hierarchy at any of the levels are nested exclusively within a member at a higher level, HLM can also provide for a situation where membership is not necessarily "nested", but "crossed", as is the case when a student may have been a member of various classrooms during the duration of a study period.
 

The HLM program allows for continuous, count, ordinal, and nominal outcome variables and assumes a functional relationship between the expectation of the outcome and a linear combination of a set of explanatory variables. This relationship is defined by a suitable link function, for example, the identity link (continuous outcomes) or logit link (binary outcomes).

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