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HLM 8—分層線性模型分析軟件

軟件試用 獲取報價

軟件簡介

HLM處理多層次數據( Hierarchical Data )，進行線性和非線性的階層模型分析。在HLM中，不僅改善了原有的界面，而且增加了新的統計功能。比如對線性模型增加了交叉隨機效應 ( Cross-classified random effects )；對三層數據增加了多項式模型 ( Multinomial Models )。該工具能處理多層次數據( Hierarchical Data ) ，進行線性和非線性的階層模型分析。

社會研究和其它領域中，研究的數據通常是分層( hierarchical )結構的。也就是說,單獨研究的課題可能會被分類或重新劃分到具有不同特性的組中。在這種情況下,個體可以被看成是研究的第一層( level-1 )單元,而那些區分開他們的組也就是第二層( level-2 )單元。這可以被進一步的延伸，第二層( level-2 )的單元也可以被劃分到第三層單元中。在這個方面很典型的示例，比如教育學( 學生位于第一層，學校位于第二層，學校分布是第三層 )，又比如社會學( 個體在第一層,相鄰的個體在第二層 )。很明顯在分析這樣的數據時，需要專業的軟件。分層線性和非線性模型( 也稱為多層模型 )的建立是被用來研究單個分析中的任意層次間的關系的,而不會在研究中忽略掉分層模型中各個層次間相關的變異性。

HLM程序包能夠根據結果變量來產生帶說明變量( expl lanatory variable )，利用在每層指定的變量來說明每層的變異性 )的線性模型。HLM不僅僅估計每一層的模型系數,也預測與每層的每個采樣單元相關的隨機因子( random effects ).雖然HLM常用在教育學研究領域( 該領域中的數據通常具有分層結構 )，但它也適合用在其它任何具有分層結構數據的領域.這包括縱向分析( longitudinal analysis )，在這種情況下,在個體被研究時的重復測量可能是嵌套( nested )的。另外，雖然上面的示例暗示在這個分層結構的任意層次上的成員( 除了處于最高層次的 )是嵌套( nested )的,HLM同樣可以處理成員關系為"交叉( crossed )"，而非必須是"嵌套( nested )"的情況，在這種情況下，一個學生在他的整個學習期間可以是多個不同教室里的成員。

HLM程序包可以處理連續，計數，序數和名義結果變量( outcome varible )，及假定一個在結果期望值和一系列說明變量( explanatory variable )的線性組合之間的函數關系。這個關系通過合適的關聯函數來定義，例如identity關聯( 連續值結果 )或logit關聯( 二元結果 )。

HLM 8 軟件功能

? 數據的新的圖形顯示技術
? 大大擴展了擬合模型的圖形能力
? 在分層或混合模型中顯示帶或不帶下標的模型等式-方便保存發表.詳細地呈現分布假設和關聯函數( link function )
? 帶有便利Windows界面的適用于線性模型和非線性關聯函數( link function )處理的交叉分類( Cross-classified )隨機因子模型
? 在二層分層的廣義線性模型( HGLM )中的帶EM演算法的適用于穩定收斂( stable convergence )和精確評估的高階Laplace近似值
? 針對3層數據的多項式和序數模型
? 方便地從多種其它的軟件包中導入數據,包括最新版本的SAS,SPSS和STATA等
? Residual文件能夠直接保存成SPSS( *.sav )或STATA( *.dta )格式文件
? 基于MDM文件格式進行分析,替換掉先前的極不靈活的SSM文件格式

英文介紹

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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