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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 8,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。


在社会研究和其他领域,研究数据通常具有等级结构。也就是说,个体研究对象可以被分类或安排成组,这些组本身具有影响研究的品质。在这种情况下,个人可以被视为一级学习单元,他们被安排的组是二级单元。这可以进一步扩展,将第二级单元组织成第三级的另一组单元,将第三级单元组织成第四级的另一组单元。教育(1级学生,2级教师,3级学校和4级学区)和社会学(1级人员,2级社区)等领域的例子比比皆是。很明显,对这些数据的分析需要专门的软件。


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)。


由于对多变量结果模型(如重复测量数据)的兴趣增加,Jennrich&Schluchter(1986)和Goldstein(1995)的贡献导致大多数可用的分层线性建模程序中包含多变量模型。这些模型允许研究人员研究层次低层的方差可以采用各种形式/结构的情况。该方法还为研究人员提供了拟合潜变量模型的机会(Raudenbush&Bryk,2002),其中层次结构的第一级表示易错的,观察到的数据与潜在的“真实”数据之间的关联。


HLM 8的新功能

从不完整的数据估计HLM

在HLM 8中,增加了从不完整数据估计HLM的能力。这是一种完全自动化的方法,可以生成和分析来自不完整数据的多重推算数据集。该模型是完全多变量的,使分析师能够通过辅助变量加强估算,这意味着用户指定HLM程序自动搜索数据以发现哪些变量具有缺失值,然后估计多变量分层线性模型(“插补模型”),其中具有遗漏值的所有变量在具有完整数据的所有固定截距和随机系数的灵活组合。


固定截距和随机系数的灵活组合

HLM 8的另一个新特性是固定截距和随机系数(FIRC)的灵活组合现在包含在HLM2,HLM3,HLM4,HCM2和HCM3中。多级因果研究中可能出现的一个问题是随机效应可能与治疗分配相关。例如,假设治疗是非随机分配给嵌套在学校内的学生,如果随机截距与治疗效果相关,则估计具有随机学校截距的两级模型将产生偏差,传统的策略是为学校指定固定效应模型。然而,这种方法假设均匀的治疗效果,可能导致对平均治疗效果的偏倚估计,不正确的标准误差和不适当的解释。HLM 7允许分析人员在解决这些问题的模型中将固定截距与随机系数相结合,并促进更丰富的总结,包括对治疗效果变化的估计和单位特定治疗效果的经验贝特斯估计,这种方法在Bloom,Raudenbush,Weiss和Porter(2017)中提出。


HLM与Windows 7,10兼容。


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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