LISREL V 9.3——结构方程模式分析软件

LISREL (LInear Structural RELations)是由K.G. Joreskog & D. Sorbom所发展的结构方程模型(Structural Equation Modeling)软件. LISREL被公认为最专业的结构方程模块（ Structural Equation Modeling, 简称 SEM ）分析工具，其权威性不容其它类似软件取代。

LISREL的内容包含多层次分析(multilevel analysis),二阶最小平方估测(two-stage least-squares estimation),主成份分析(principal component analysis)等等.

1.发展研究者之理论基础模式。
2.建构变项间之因果关系的径路图。
3.将径路图转化为一套结构等式，并指定其测量模式。
4.选择输入矩阵类型(相关矩阵或变异数－ 共变量矩阵)，并对研究者假设之理论模式进行测量与验证。

LISREL最新的特色包含对遗漏值的最大概似估计法、多元结构等式模型(multilevel structural equation modeling) 、以recursive modeling为基础的正式推论、multiple imputation和非线性多元回归模型以及各式各样操作界面的改进，包括使用长的数据和文件名称。

LISREL最新版本提供了更强大的分析统计功能。

LISREL 用于结构方程建模
PRELIS 用于数据处理和基本统计分析
MULTILEV 用于分层线性和非线性建模
SURVEYGLIM 用于广义线性建模
CATFIRM 用于类别响应变量的formative inference-based 递归建模(FIRM)
CONFIRM 用于连续响应变量的formative inference-based 递归建模(FIRM)
MAPGLIM 用于多层数据的广义线性建模

LISREL用于：

PRELIS用于：

Logistic回归

ML和MINRES探索性因子分析
MULTILEV 可从通过简单随机和复杂调查设计得到的多层数据中拟合出多层线性和非线性模型。它允许模型带有类别和连续响应变量
SURVEYGLIM 可从通过简单随机和复杂调查设计得到的数据中拟合广义线性模型(GLIMs)

CATFIRM 执行类别输出变量的formal inference-based递归建模
CONFIRM 执行连续输出变量的formal inference-based递归建模
MAPGLIM 执行最大因果(MAP)方法来拟合多层数据的广义线性模型

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
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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