LISREL软件简介 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。 目前几乎可在各平台执行包含Windows, Mac OS 9 X, Solaris, AIX, RISC ,OpenVMS , Linux等. 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. LISREL 8.71于2004年10月更新。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 8.7 的特色主要有下列几点： 1. 可以分析完整 data 和不完整 data 时的 Multilevel Structural Equation Model ，以及非线性 Multilevel Model (Two-level nonlinear regression models) ，技术明显领先其它同类软件。 2. 唯一提供 Efficient Full Information Maximum Likelihood (FIML) 方法处理 SEM 中 missing data 的问题，模型解释力最强。分析的样本大小和变量个数的多寡完全不受限制，提供最大的数据处理能力。 3. 提供最具说服力的验证性因素分析 (Confirmatory Factor Analysis; CFA) 和探索性因素分析 (Exploratory Data Analysis; EFA) 报告。并利用 Formal Inference-based Recursive Modeling (FIRM) 方法检测类别变量和连续变量间的复杂统计关系 由于LISREL在探讨多变项因果关系上的强力优势，使得LISREL在社会学研究上似乎有愈来愈受重视的趋势，LISREL系属于「结构等式模式(structural equation modeling，SEM)」家族的一员，因此LISREL的最大能耐亦在于探讨多变项或单变项之间的因果关系。SEM一族的成员包含「共变量结构分析(covariance structure analysis)」、「潜在变项分析(latent variable analysis)」、「验证性因素分析(comfirmatory factor analysis)」、以及「LISREL分析(LISREL analysis)」等等，SEM结合了多元回归与因素分析，可以同时分析一堆互为关连之依变项间的关系。SEM之使用步骤如下： 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 8.8于2006年7月25日正式发布最。最新版本提供了更强大的分析统计功能。 New features in LISREL 8.8 Structured latent curve models The LISREL CO command has been extended to include the exponential (EXP) and natural logarithm (LOG) operators as well as parentheses. This allows LISREL users to fit, for example, the structured latent curve models outlined in Browne (1993). View an illustrative example. Factor analysis of ordinal variables Classical exploratory factor analysis assumes that the observed variables are continuous. The PRELIS OFA command implements exploratory factor analysis of ordinal variables as described in Jöreskog & Moustaki (2006). View an illustrative example. Generalized linear models (GLIMs) for multilevel data The new statistical application MAPGLIM fits generalized linear models to multilevel data. Users can select from the multinomial, Bernoulli, Poisson, binomial, negative binomial, Normal, Gamma and inverse Gaussian sampling distributions. The corresponding link functions include the log, cumulative logit, cumulative probit, complementary log-log and logit link functions. View an illustrative example. Observational residuals Bollen and Arminger (1991) introduced observational residuals for structural equation models. LISREL 8.8 for Windows allows users to compute observational residuals along with latent variable scores for the latent variables of the model. This implementation is described and illustrated in Jöreskog, Sörbom & Wallentin (2006) View an illustrative example. Writing parameter estimates, standard error estimates and measures of fit to a PSF The PV, SV and GF keywords on the LISREL OU command or the SIMPLIS LISREL output command have been extended to allow users to save the parameter estimates, standard error estimates and measures of fit to a PSF. This is especially useful for Monte Carlo studies.Changes to the graphical user interface (GUI) The main window of LISREL 8.8 for Windows is now entitled LISREL for Windows. The revised Export Data option on the File menu of the main window allows users to export data to various data formats such as SPSS, SAS, SYSTAT, Statistica, etc. 本文来源：https://www.wddqw.com/doc/9bd6c02ecf1755270722192e453610661ed95a05.html