HML 多層次線性分析軟體 (Hierarchical Linear Modeling 階層線性模型分析)
HLM 可以讀取 SPSS、SAS、SYSTAT 及 STATA 等統計軟體的檔案。
回歸分析雖是經濟計量研究中常用的研究方法，但是實際研究中的數據往往具有層次結構的特點，如對於不同地區個體經濟發展指標的測量，個體嵌套於地區。對於這類數據的分析往往需要考慮更高層次如區域的特徵。
在社會研究等領域，研究數據往往具有層次結構。個別學科可歸類或排列級結成本身素質影響的研究。在這種情況下，個人可以視為一級學習單位和它們排列集結的是2級單位。這可能進一步擴大，2級單位組織起來而成為另一些單位為第三層次。這方面的例子俯拾皆是,如教育(學生在1級，學校在第2級、學區是第3級)、社會學(1級、街道級2)。顯然,這些數據的分析需要專門的軟體。階層線性和非線性模型(也稱多階層模型)已經開發出研究的關係，以便在任何層面的單一分析，而沒有忽略在各層次的變異。
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。
系統需求
Compatible with Windows 7, 8, 10.
新版特色功能
HLM 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 , HLM 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 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 Standard
Standard non-commercial, academic, and educational copy. Licenses are available for single users (two installs). Additional users can be added to your license with each receiving two installs.
HLM Basic
Basic non-commercial, academic, and educational copy. Licenses are available for single users (one install). Please note that no technical support is provided with the basic version.
The minimum system requirements for our software are as follows:
- A processor of at least 1 GHz
- At least 1 GB of RAM or at least 2 GB of Ram for 64-bit Windows
- At least 200 MB of free disk space on the C drive
- Windows 7, Windows 8, Windows 10, or Windows 11