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LISREL V 9.3——结构方程模式分析软件

SSI创建于1971年,旨在应用统计理论,开发新的统计软件和完善已有的统计软件。产品被广泛应用于统计、社会科学、医药健康、教育、经济、工商管理、市场、环境科学、工程以及其他研究领域。每年,通过应用SSI软件得出的结果而在国际性刊物上发表的论文不计其数。
LISREL被公认为最为专业的结构方程建模 (Structural Equation Modeling, 简称 SEM) 分析工具。通过运用路径图 (Path Diagram,又称通径图)直观地构造结构模型是LISREL的一个重要特点。在LISREL中,新增了多层次分析(Multilevel Modeling) 、广义线性模型 (Generalized Linear Regression, 又称通用线性模型)。

在过去的四十五年中,LISREL模型、方法和软件已成为结构方程模型(SEM)的同义词。SEM使社会科学、管理科学、行为科学、生物科学、教育科学等领域的研究人员对他们的理论进行了实证评估。这些理论通常归结为两种理论模型,可观测变量和不可观测变量。如果为理论模型的观测变量收集数据,那么LISREL可以用来将模型拟合为数据。


LISREL不仅能处理结构方程模型,还能用于其他统计应用中:
LISREL:结构方程模型
PRELIS:数据处理与基本统计分析
MULTILEV:分层线性和非线性建模
SURVEYGLIM:广义线性模型
MAPGLIM:多级数据的广义线性建模


LISREL可以用来处理
标准结构方程模型
多级结构方程模型


处理数据类型包括
分类和连续变量的完整和不完整的复杂调查数据
分类变量和连续变量的完全不完全随机样本数据

PRELIS
数据处理
数据转换
数据生成
计算矩阵
计算样本矩的渐近协方差矩阵
归责的匹配
多重估算
多元线性回归分析
Logistic回归
单变量多元删失回归
ML和MINRES探索性因子分析


MULTILEV
MULTILEV拟合简单随机和复杂调查设计中的多级线性和非线性模型到多级数据。它允许具有连续和明确的响应变量的模型。


SURVEYGLIM
SURVEYGLIM拟合简单随机和复杂调查设计中的广义线性模型(GLIMs)到数据中。可用的抽样分布模型如下:
Multinomial
Bernoulli
Binomial
Negative Binomial
Poisson
Normal
Gamma
Inverse Gaussian


MAPGLIM
MAPGLIM执行 Maximum A Priori (MAP)法来拟合广义线性模型到多级数据中。


操作系统
LISREL软件只适用于Windows操作系统。


Announcing the release of LISREL version 9.1
SSI has enjoyed great success over the years in the development and publishing of statistical software and is proud to announce the release of LISREL 9.1.
In an effort to meet the growing demands of our LISREL 8 user community, SSI has developed LISREL 9.1, 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.
The development of LISREL was partially supported by an SBIR grant R43 AA014999-01 from NIAAA.
Background
Structural equation modeling (SEM) was introduced initially as a way of analyzing a covariance or correlation matrix. Typically, one would read this matrix into LISREL and estimate the model by maximum likelihood. If raw data was available without miSSIng values, one could also use PRELIS first to estimate an asymptotic covariance matrix to obtain robust estimates of standard errors and chi-squares.
The new LISREL features are summarized next.

Combining LISREL and PRELIS functionality
Modern structural equation modeling is based on raw data. With LISREL 9.1, if raw data is available in a LISREL data system file or in a text file, one can read the data into LISREL and formulate the model using either SIMPLIS syntax or LISREL syntax. It is no longer necessary to estimate an asymptotic covariance matrix with PRELIS and read this into LISREL. The estimation of the asymptotic covariance matrix and the model is now done in LISREL9. One can also use the EM or MCMC multiple imputation methods in LISREL to fit a model to the imputed data.
If requested, LISREL 9.1 will automatically perform robust estimation of standard errors and chisquare goodness of fit measures under non-normality. If the data contain missing values, LISREL 9 will automatically use Full information maximum likelihood (FIML) to estimate the model.
Alternatively, users may choose to impute the missing values by EM or MCMC and estimate the model based on the imputed data. Several new sections of the output are also included.
Examples in the folder \ls9ex illustrate these new features.

FIML for ordinal and continuous variables
LISREL 9.1 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
Examples to illustrate this feature are given in the folders \orfimlex and \ls9ex.

Three-level Multilevel Generalized Linear Models
Cluster or multi-stage samples designs are frequently used for populations with an inherent hierarchical structure. Ignoring the hierarchical structure of data has serious implications. The use of alternatives such as aggregation and disaggregation of information to another level can induce an increase in co-linearity among predictors and large or biased standard errors for the estimates.
The collection of models called Generalized Linear Models (GLIMs) have become important, and practical, statistical tools. The basic idea of GLIMs is an adaption of standard regreSSIon to quite different kinds of data. The variables may be dichotomous, ordinal (as with a 5-point Likert scale), counts (number of arrest records), or nominal. The motivation is to tailor the regreSSIon relationship connecting the outcome to relevant independent variables so that it is appropriate to the properties of the dependent variable. The statistical theory and methods for fitting Generalized Linear Models (GLIMs) to survey data was implemented in LISREL 8.8. Researchers from the social and economic sciences are often applying these methods to multilevel data and consequently, inappropriate results are obtained. The LISREL 9.1 statistical module for the analysis of multilevel data allows for design weights. Two estimation methods, MAP (maximization of the posterior distribution) and QUAD (adaptive quadrature) for fitting generalized linear models to multilevel data are available. The LISREL module allows for a wide
variety of sampling distributions and link functions.
Examples in the folder \mglimex illustrate these new features.

Four and Five-level Multilevel Linear Models for continuous outcome variables Social science research often entails the analysis of data with a hierarchical structure. A
frequently cited example of multilevel data is a dataset containing measurements on children nested within schools, with schools nested within education departments.
The need for statistical models that take account of the sampling scheme is well recognized and it has been shown that the analysis of survey data under the assumption of a simple random sampling scheme may give rise to misleading results.
Multilevel models are particularly useful in the modeling of data from complex surveys. Cluster or multi-stage samples designs are frequently used for populations with an inherent hierarchical structure. Ignoring the hierarchical structure of data has serious implications. The use of alternatives such as aggregation and disaggregation of information to another level can induce an increase in co-linearity among predictors and large or biased standard errors for the estimates. In order to address concerns regarding the appropriate analyses of survey data, the LISREL multilevel module for continuous data now also handles up to five levels and features an option for users to include design weights on levels 1, 2 , 3, 4 or 5 of the hierarchy.
Examples are given in the \mlevelex folder. New filename extensions
All LISREL syntax files have extension .lis (previously .ls8), all PRELIS syntax files have extension .prl (previously .pr2). The LISREL spreadsheet has been renamed LISREL data system file and has extension .lsf (previously .psf)
To ensure backwards compatibility, users can still run previously created syntax files using a .psf file, but to open an existing .psf file using the graphical user’s interface, the user has to rename it to .lsf.
Running LISREL in batch mode
Any of the LISREL programs can be run into batch mode by using a .bat file with the following script:
"c:\program files (x86)\LISREL9\MLISREL9"

where
Program name = LISREL, PRELIS, MULTILEV, MAPGLIM or SURVEYGLIM

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