软件介绍

HLM 8—分层线性模型分析软件
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软件简介

HLM处理多层次数据( Hierarchical Data )，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 ( Cross-classified random effects )；对三层数据增加了多项式模型 ( Multinomial Models )。该工具能处理多层次数据( Hierarchical Data ) ，进行线性和非线性的阶层模型分析。

社会研究和其它领域中，研究的数据通常是分层( hierarchical )结构的。也就是说,单独研究的课题可能会被分类或重新划分到具有不同特性的组中。在这种情况下,个体可以被看成是研究的第一层( level-1 )单元,而那些区分开他们的组也就是第二层( level-2 )单元。这可以被进一步的延伸，第二层( level-2 )的单元也可以被划分到第三层单元中。在这个方面很典型的示例，比如教育学( 学生位于第一层，学校位于第二层，学校分布是第三层 )，又比如社会学( 个体在第一层,相邻的个体在第二层 )。很明显在分析这样的数据时，需要专业的软件。分层线性和非线性模型( 也称为多层模型 )的建立是被用来研究单个分析中的任意层次间的关系的,而不会在研究中忽略掉分层模型中各个层次间相关的变异性。

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( 除了处于最高层次的 )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 8 软件功能

• 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
• 在分层或混合模型中显示带或不带下标的模型等式-方便保存发表.详细地呈现分布假设和关联函数( link function )
• 带有便利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3层数据的多项式和序数模型
• 方便地从多种其它的软件包中导入数据,包括最新版本的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

HLM - Hierarchical Linear and Nonlinear Modeling ( HLM )

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 prevalence 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 ).

Four-level nested models:
• Four-level nested models for cross-sectional data ( for example, models for item response within students within classrooms within schools ).
• Four-level models for longitudinal data ( for example items within time points within persons within neighborhoods ).

Four-way cross-classified and nested mixtures:
• Repeated measures on students who are moving across teachers within schools over time, or item responses nested within immigrants who are cross-classified by country of origin and country of destination.
• Repeated measures on persons who are simultaneously living in a given neighborhood and attending a given school.

Hierarchical models with dependent random effects:
• Spatially dependent neighborhood effects.
• Social network interactions.

HLM 7 also offers new flexibility in estimating hierarchical generalized linear models through the use of Adaptive Gauss-Hermite Quadrature ( AGH ) and high-order Laplace approximations to maximum likelihood. The AGH approach has been shown to work very well when cluster sizes are small and variance components are large. the high-order Laplace approach requires somewhat larger cluster sizes but allows an arbitrarily large number of random effects ( important when cluster sizes are large ).

New HTML output that supplies elegant notation for statistical models including visually attractive tables is also now available, allowing the user to cut and paste output of interest into manuscripts.

HLM 7 manual
• A hard copy of the HLM 7 manual is not available.
• PDF copies of the HLM 7 manual are available via the HLM 7 Manual option on the Help menu of the full, rental, trial, and student editions of HLM 7 for Windows.

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软件简介

HLM 8 软件功能

英文介绍

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HLM 8—分层线性模型分析软件
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