Mplus是一个统计建模软件为研究者提供了一个灵活的工具来分析数据，提供了多种选择，具有易于使用的图形界面和展示数据分析结果的模式,估计和算法。Mplus允许一起分析横断面和纵向数据，单层和多层数据，来自不同的母体的数据，无论可见或不可见的异质。可分析可见的变量是continuous, censored, binary, ordered categorical (ordinal), unordered categorical (nominal), counts或这些变量类型的组合。Mplus对遗漏值、复杂的调查数据和多层次的数据也有特别的功能。此外，Mplus对在蒙地卡罗模拟研究的任何可产生和分析数据的模型具有强大功能。The generality of the Mplus modeling framework comes from the unique use of both continuous and categorical latent variables. Continuous latent variables are used to represent factors corresponding to unobserved constructs, random effects corresponding to individual differences in development, random effects corresponding to variation in coefficients across groups in hierarchical data, frailties corresponding to unobserved heterogeneity in survival time, liabilities corresponding to genetic susceptibility to disease, and latent response variable values corresponding to missing data. Categorical latent variables are used to represent latent classes corresponding to homogeneous groups of individuals, latent trajectory classes corresponding to types of development in unobserved populations, mixture components corresponding to finite mixtures of unobserved populations, and latent response variable categories corresponding to missing data. 主要功能: 1. 回归分析和路径 2. 探索性因素分析 3. 验证性因素分析、结构方程建模 4. 生长建模与生存分析 5. 混合建模横断面数据 6. 纵向数据混合建模 7. 多造型复杂调查数据 8. 多层次混合建模 9. 蒙特卡洛研究作者: 结构方程爱好者 时间: 2011-7-2 21:39
也建议单开Mplus组，毕竟Mplus is the future for SEM。作者: pabbs 时间: 2011-7-2 22:29
这个才是软件的大牛！！！
不是很容易学会！！
不过值得转眼！！！！！！！作者: wmhhotel 时间: 2011-7-2 22:34
Mplus是一个统计建模软件为研究者提供了一个灵活的工具来分析数据，提供了多种选择，具有易于使用的图形界面和展示数据分析结果的模式,估计和算法。Mplus允许一起分析横断面和纵向数据，单层和多层数据，来自不同的母体的数据，无论可见或不可见的异质。可分析可见的变量是continuous, censored, binary, ordered categorical (ordinal), unordered categorical (nominal), counts或这些变量类型的组合。Mplus对遗漏值、复杂的调查数据和多层次的数据也有特别的功能。此外，Mplus对在蒙地卡罗模拟研究的任何可产生和分析数据的模型具有强大功能。The generality of the Mplus modeling framework comes from the unique use of both continuous and categorical latent variables. Continuous latent variables are used to represent factors corresponding to unobserved constructs, random effects corresponding to individual differences in development, random effects corresponding to variation in coefficients across groups in hierarchical data, frailties corresponding to unobserved heterogeneity in survival time, liabilities corresponding to genetic susceptibility to disease, and latent response variable values corresponding to missing data. Categorical latent variables are used to represent latent classes corresponding to homogeneous groups of individuals, latent trajectory classes corresponding to types of development in unobserved populations, mixture components corresponding to finite mixtures of unobserved populations, and latent response variable categories corresponding to missing data. 主要功能: 1. 回归分析和路径 2. 探索性因素分析 3. 验证性因素分析、结构方程建模 4. 生长建模与生存分析 5. 混合建模横断面数据 6. 纵向数据混合建模 7. 多造型复杂调查数据 8. 多层次混合建模 9. 蒙特卡洛研究作者: 数据分析 时间: 2011-7-2 22:39
