HLM 7.0 階層線性模式軟體

HLM 7 包括改進的介面、 HTML 輸出和新的統計功能，如交叉分類隨機效應線性模型三級資料，和四級資料線性和非線性模型。

階層線性模式 (Hierarchical linear Model，簡稱 HLM)，HLM所發展的階層模型(Hierarchical Linear and Nonlinear Modeling)軟體，包含線性和非線性部分，HLM可以讀取大部份統計軟體的檔案如 SPSS, SAS, SYSTAT及STATA等等。HLM常用於社會科學和行為科學，因為它常有巢狀結構(Nested Structure)的資料，因此需用次模型(Sub-Model)或階層模型(Hierarchical Model)，HLM就是設計來專門解決此類問題的，HLM提供的模型包括2-level models、3-level models、Hierarchical Generalized Linear Models (HGLM)和Hierarchical Multivariate Linear Models (HMLM)等。

HLM 7 includes an improved interface, HTML output, and new statistical features such as cross-classified random effects for linear models for three-level data, and linear and nonlinear models for four-level data.

HLM 7 offers unprecedented flexibility in modeling multilevel and longitudinal data. With the same full array of graphical procedures and residual files along with the speed of computation, robustness of convergence, and user-friendly interface of HLM 6, HLM 7 highlights include three new procedures that handle binary, count, ordinal and multinomial (nominal) response variables as well as continuous response variables for normal-theory hierarchical linear models:
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)

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.