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HLM 8
階層線性模型與非線性模型軟體
Hierarchical Linear Modeling
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產品介紹!

HLM軟體自202062起採年租式販售

軟體分為兩個版本

1. 標準版 Standard
- 軟體限個人使用,限裝電腦二台,同時僅一台電腦使用(桌上型或筆記型) 
   若要更換電腦,需先將其中一台電腦授權停止後,才能到新電腦安裝啟動!
- 租用期間原廠提供技術支援 (由於原廠政策,請由使用者自行聯繫原廠) 
- 可享續租優惠價格
- 針對課程,老師必須使用註冊的EMAIL自行聯繫原廠,申請學生版 (sales@ssicentral.com)

2. 基礎版 Basic
- 軟體限個人使用,限裝電腦一台使用
- 原廠『不』提供技術支援服務
- 原廠『不』提供續租優惠價格
- 無法申請學生版


版本比較表如下表:

您可以連結以下網址,來預覽所有YouTube的教學影片

http://www.youtube.com/user/SoftHomeHLM

 

HLM 功能展示

HLM是由A.S. Bryk, S.W. Raudenbush & R.T. Congdon.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) ,Hierarchical Multivariate Linear Models (HMLM)等

http://www.fed.cuhk.edu.hk/en/jep/2001/2001001c.htm

階層線性模式之理論與應用:以「影響自然科成績之因素的研究」為分析實例 http://www.ntut.edu.tw/~s5270042/resourcelite1.htm 學校效能研究領域的發展 手冊

HLM 6: Hierarchical Linear and Nonlinear Modeling (manual).(297pages)

Q:如何建立 MDM 檔

A: HLM的 MDM 檔 可以來自 ASCII, SYSTAT, SPSS,SAS 建議您用SYSTAT最直覺若用ASCII須自定反應變數, 若您有其他的統計軟體檔案格式可用MYSTAT做轉換, 凡本公司客戶,可獲得免費MYSTAT版本

Q:有客戶反應HLM無法讀取SPSS之格式,如何解決? A:建議您可以另存成 ASCII, SYSTAT 來讀取凡本公司客戶,可免費為您試轉一次

Q: Windows 7 相容?

32,64bit都相容

Q:有試用版 A: http://www.ssicentral.com/hlm/student.html

 

在社會研究和其他領域,研究數據通常具有層次結構。就是說,單個研究主題可以被分類或安排在具有自身影響研究質量的組中。在這種情況下,可以將個人視為1級學習單元,而將其安排在其中的組為2級學習單元。這可以進一步擴展,其中將第二級單位組織為第三級的另一組單元,並且將第三級單位組織為第四級的另一組單元。在教育(1級學生,2級教師,3級學校,4級學區)和社會學(1級個人,2級社區)等領域,例子很多。顯然,對此類數據的分析需要專用軟件。已經開發了層次線性和非線性模型(也稱為多層次模型),以允許在單個分析中研究任何層次的關係,同時又不忽略與層次的每個層次相關的可變性。

HLM使模型適合於結果變量,該結果變量將使用每個級別指定的變量生成線性模型,該模型具有解釋性變量,這些解釋變量可解釋每個級別的差異。 HLM不僅估計每個級別的模型係數,而且還預測與每個級別的每個採樣單元相關的隨機效應。儘管由於該領域的數據中普遍使用分層結構,所以在教育研究中通常使用它,但它適合與來自具有分層結構的任何研究領域的數據一起使用。這包括縱向分析,在縱向分析中,可以將一個人的重複測量嵌套在要研究的一個人中。另外,儘管以上示例暗示了該層次結構的任何級別的成員都僅嵌套在更高級別的成員中,HLM還可以提供這樣一種情況,即成員不一定是“嵌套的”,而是“交叉的”,就像學生在學習期間可能是各種教室的成員那樣。

HLM允許連續,計數,序數和名義結果變量,並假設結果的期望與一組解釋變量的線性組合之間存在函數關係。此關係由適當的鏈接功能定義,例如,身份鏈接(連續結果)或對數鏈接(二進制結果)。

由於對多變量結果模型的興趣不斷增加,例如重複的測量數據,Jennrich&Schluchter(1986)和Goldstein(1995)的貢獻導致將多變量模型包含在大多數可用的分層線性建模程序中。這些模型使研究人員可以研究層次結構最低級別的方差可以採用各種形式/結構的情況。該方法還為研究人員提供了擬合潛在變量模型的機會(Raudenbush&Bryk,2002年),層次結構的第一級代表了易犯的觀測數據與潛在的“真實”數據之間的關聯。最近在這方面受到關注的應用是項目響應模型的分析,其中個體的“能力”或“潛在特徵”是基於給定響應的概率與呈現給個人的項目特徵的函數。

 在HLM 7中,引入了三種新的過程來處理多級和縱向數據建模方面的前所未有的靈活性,這些過程處理了二進制,計數,序數和多項式(標稱)響應變量以及法線理論分層線性模型的連續響應變量。 HLM 7引入了用於橫截面和縱向模型的四級嵌套模型以及四路交叉分類和嵌套的混合模型。增加了具有相關隨機效應的分層模型(空間設計)。另一個新功能是通過使用自適應高斯-赫爾姆正交(AGH)和高階Laplace逼近來最大程度地估計分層廣義線性模型的新靈活性。當簇大小較小且方差成分較大時,AGH方法已顯示出很好的效果。高階Laplace方法需要較大的群集大小,但允許任意數量的隨機效果(在群集大小較大時很重要)。

在HLM8中,增加了從不完整數據中估算HLM的功能。這是一種完全自動化的方法,可以根據不完整的數據生成和分析多個估算的數據集。該模型是完全多元的,使分析人員可以通過輔助變量來加強估算。這意味著用戶指定了HLM。程序會自動搜索數據以發現哪些變量缺少值,然後估算一個多元層次線性模型(“輸入模型”),在該模型中,所有具有缺失值的變量都將回歸到具有完整數據的所有變量上。然後,程序使用所得的參數估計值生成M個估算數據集,然後依次分析每個數據集。使用“魯賓規則”合併結果。

 HLM 8的另一個新功能是,HLM2,HLM3,HLM4,HCM2和HCM3現在包含固定攔截和隨機係數(FIRC)的靈活組合。在多層次因果關係研究中可能會引起關注的是,隨機效應可能與治療分配相關。例如,假設治療是非隨機分配給嵌套在學校內的學生的。如果隨機截距與治療效果相關,則估計帶有隨機學校截距的兩級模型將產生偏差。常規策略是為學校指定固定效果模型。但是,此方法假定處理效果均一,可能導致平均處理效果的估計偏差,錯誤的標準誤和不正確的解釋。HLM 8使分析人員可以在解決這些問題的模型中將固定截距與隨機係數結合起來,並提供更豐富的摘要,包括對治療效果變化的估算和對特定單位治療效果的經驗貝葉斯估算。 Bloom,Raudenbush,Weiss和Porter(2017)提出了這種方法。

 

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 and with level-3 units organized into another set of units at a fourth level. Examples of this abound in areas such as education (students at level 1, teachers at level 2, schools at level 3, and school districts at level 4) 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.

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 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).

Due to increased interest in multivariate outcome models, such as repeated measurement data, contributions by Jennrich & Schluchter (1986), and Goldstein (1995) led to the inclusion of multivariate models in most of the available hierarchical linear modeling programs. These models allow the researcher to study cases where the variance at the lowest level of the hierarchy can assume a variety of forms/structures. The approach also provides the researcher with the opportunity to fit latent variable models (Raudenbush & Bryk, 2002), with the first level of the hierarchy representing associations between fallible, observed data and latent, "true" data. An application that has received attention in this regard recently is the analysis of item response models, where an individuals "ability" or "latent trait" is based on the probability of a given response as a function of characteristics of items presented to an individual.

 In HLM 7, unprecedented flexibility in the modeling of multilevel and longitudinal data was introduced with the inclusion of 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. HLM 7 introduced four-level nested models for cross-sectional and longitudinal models and four-way cross-classified and nested mixture models. Hierarchical models with dependent random effects (spatial design) were added. Another new feature was 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).

In HLM8, the ability to estimate an HLM from incomplete data was added. This is a completely automated approach that generates and analyses multiply imputed data sets from incomplete data. The model is fully multivariate and enables the analyst to strengthen imputation through auxiliary variables. This means that the user specifies the HLM; the program automatically searches the data to discover which variables have missing values and then estimates a multivariate hierarchical linear model (”imputation model”) in which all variables having missed values are regressed on all variables having complete data. The program then uses the resulting parameter estimates to generate M imputed data sets, each of which is then analysed in turn. Results are combined using the “Rubin rules”.

 

 Another new feature of HLM 8 is that flexible combinations of Fixed Intercepts and Random Coefficients (FIRC) are now included in HLM2, HLM3, HLM4, HCM2, and HCM3. A concern that can arise in multilevel causal studies is that random effects may be correlated with treatment assignment. For example, suppose that treatments are assigned non-randomly to students who are nested within schools. Estimating a two-level model with random school intercepts will generate bias if the random intercepts are correlated with treatment effects. The conventional strategy is to specify a fixed effects model for schools. However, this approach assumes homogeneous treatment effects, possibly leading to biased estimates of the average treatment effect, incorrect standard errors, and inappropriate interpretation. HLM 8 allows the analyst to combine fixed intercepts with random coefficients in models that address these problems and to facilitate a richer summary including an estimate of the variation of treatment effects and empirical Bayes estimates of unit-specific treatment effects. This approach was proposed in Bloom, Raudenbush, Weiss and Porter (2017).