HLM还提供了估计分层广义线性模型的自适应Gauss-Hermite Quadrature (AGH)和
高阶拉普拉斯Laplace近似最大似然法。AGH的方法已经被证明是有效的,尤其当集群规模小,方差分量大的时候。高阶Laplace方法需要较大的集群大学,但允许任意数量的随机效应(当集群较大时非常重要)。
HLM新版本的输出,为统计模型提供优雅的符号,包括视觉上有吸引力的表格、用户可以剪切和复制输出内容到结果中。
HLM fits 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.
HLM包括纵向分析,其中个体的重复测量可以嵌入在被研究的个体内。
HLM程序允许连续计数、序数和名义变量和假设结果的期望和一组解释变量的线性组合的函数关系。这种关系是由一个合适的链接函数定义的,例如,身份链接(连续结果)或logit链接(二进制结果)。
HLM还提供了估计分层广义线性模型的自适应Gauss-Hermite Quadrature (AGH)和
高阶拉普拉斯Laplace近似最大似然法。AGH的方法已经被证明是有效的,尤其当集群规模小,方差分量大的时候。高阶Laplace方法需要较大的集群大学,但允许任意数量的随机效应(当集群较大时非常重要)。