LISREL
(Linear Structural Relations 線性結構關係)
特色包含
組織潛在的曲線模型(Structured latent curve models)、順序的變量的要素分析(Factor analysis
of ordinal variables) 、對於多層的數據的綜合線性模型(GLIMs)
(Generalized linear models (GLIMs) for multilevel data)、觀測殘餘(Observational
residuals)、轉換成圖形用戶界面(GUI)及Writing parameter estimates, standard
error estimates and measures of fit to a PSF。
由於LISREL在探討多變項因果關係上的強力優勢，使得LISREL在社會學研究上似乎有愈來愈受重視的趨勢，LISREL係屬於「結構等式模式(structural equation modeling，SEM)」家族的一員，因此LISREL的最大能耐亦在於探討多變項或單變項之間的因果關係。SEM一族的成員包含「共變數結構分析(covariance structure analysis)」、「潛在變項分析(latent variable analysis)」、「驗證性因素分析(comfirmatory factor analysis)」、以及「LISREL分析(LISREL analysis)」等等，SEM結合了多元迴歸與因素分析，可以同時分析一堆互為關連之依變項間的關係。SEM之使用步驟如下：
1.發展研究者之理論基礎模式。
2.建構變項間之因果關係的徑路圖。
3.將徑路圖轉化為一套結構等式，並指定其測量模式。
4.選擇輸入矩陣類型(相關矩陣或變異數－共變數矩陣)，並對研究者假設之理論模式進行測量與驗證。
SSI has developed LISREL,
which is on the cutting edge of current technology. The program
has been tested extensively on the Microsoft Windows platform
with Windows7, Vista and XP operating systems.
LISREL
supports Structural Equation Modeling for a mixture of
ordinal and continuous variables for simple random samples and
complex survey data.
The LISREL implementation allows for the use of design weights
to fit SEM models to a mixture of continuous and ordinal
manifest variables with or without missing values with optional
specification of stratum and/or cluster variables. It also deals
with the issue of robust standard error estimation and the
adjustment of the chi-square goodness of fit statistic.
This method is based on adaptive quadrature and a user can
specify any one of the following four link functions:
o Logit
o Probit
o Complementary Log-log
o Log-Log
Today, however, LISREL
for Windows is no longer limited to SEM. The latest LISREL for
Windows includes the following statistical applications.
● LISREL for structural equation modeling.
● PRELIS for data manipulations and basic statistical analyses.
● MULTILEV for hierarchical linear and non-linear modeling.
● SURVEYGLIM for generalized linear modeling.
● CATFIRM for formative inference-based recursive modeling for
categorical response variables.
● CONFIRM for formative inference-based recursive modeling for
continuous response variables.
● MAPGLIM for generalized linear modeling for multilevel data. New!
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