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所在平台: Udemy |
课程主页: https://www.udemy.com/course/multilevel-sem-modeling-with-xxm/
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课程名称：使用xxM进行多层结构方程模型（SEM）建模 课程概述：多层建模是用于描述层次线性模型、嵌套模型、混合效应模型、随机效应模型和分割设计的术语。这些统计模型用于估计在多个层次上变化的参数，并可能在任何层次上包含观察变量和潜在变量。多层模型是线性模型，特别是线性回归的推广，尽管它们可以扩展到非线性模型。xxM是一个R包，能够估计具有复杂层次依赖数据结构的多层SEM模型，既包含观察变量也包含潜在变量。该包是在休斯顿大学由Dr. Paras Mehta及其团队开发。xxM实施了一种称为n级结构方程建模（NL-SEM）的建模框架，允许指定具有任意数量层次的模型。由于允许在所有层次上使用观察变量和潜在变量，因此可以为每个层次及跨层次指定传统的SEM模型。同时，观察变量的随机效应允许在层次内和跨层次存在。Mehta声称xxM是全球唯一能够估计在无限层次上观察变量和潜在变量对SEM名义网络的影响的软件工具。 xxM可以有效建模和估计的复杂依赖数据结构包括： - 层次嵌套数据（例如：学生、教室、学校） - 纵向数据（长格式或宽格式） - 具有切换分类的纵向数据（例如：学生更换教室） - 跨分类数据（例如：学生嵌套在小学和中学中） - 部分嵌套（例如：在教室中表现不佳的学生接受辅导） xxM的模型规格采用“乐高积木式”构建模型的方法。通过理解这些基本构建块，可以通过重复相同的构建步骤构建非常复杂的多层模型。 本课程为期六节课，是对如何使用xxM执行这些关键基本构建步骤的概述和教程。为了传达对xxM核心模型规格和构建概念的实际理解，课程详细介绍了七个完整的示例。完成此课程后，学员将能够构建与自己研究项目相关的更复杂的多层模型。课程中详细描述的七个完整示例包括： 1. 简化的两层双变量随机截距模型； 2. 两层随机斜率模型； 3. 多层确认性因子分析（CFA）； 4. 随机斜率多层CFA； 5. 随机斜率“宽型”和“长型”潜在增长曲线模型示例； 6. 完整演示包含观察和潜在变量的三层层次模型。 所有必要的软件、数据、手册、幻灯片和课程材料均提供在课程视频相关的“资源”文件夹中，以便学员有效地指定和估计所有七个课程模型示例。
Multilevel modeling is a term alternately used to describe hierarchical linear models, nested models, mixed-effects models, random-effects models, and split-plot designs. They are statistical models for estimating parameters that vary at more than one level and which may contain both observed and latent variables at any level. They are generalizations of linear models, particularly linear regression, although they may be extended to non-linear models.xxM is an R package which can estimate multilevel SEM models characterized by complex level-dependent data structures containing both observed and latent variables. The package was developed at the University of Houston by a collaborative team headed by Dr. Paras Mehta. xxM implements a modeling framework called n-Level Structural Equation Modeling (NL-SEM) which allows the specification of models with any number of levels. Because observed and latent variables are allowed at all levels, a conventional SEM model may be specified for each level and across any levels. Also, the random-effects of observed variables are allowed both within and across levels. Mehta claims that xxM is the only software tool in the world that is capable of estimating the effects of both observed and latent variables in a SEM nomological network across an unlimited number of levels.Some of the complex dependent data structures that can be effectively modeled and estimated with xxM include:⦁ Hierarchically nested data (e.g. students, classrooms, schools)⦁ Longitudinal data (long or wide)⦁ Longitudinal data with switching classification (e.g. students changing classrooms)⦁ Cross-classified data (e.g. students nested within primary and secondary schools)⦁ Partial nesting (e.g. underperforming students in a classroom receive tutoring)Model specification with xxM uses a "LEGO-like building block" approach for model construction. With an understanding of these basic building blocks, very complex multilevel models may be constructed by repeating the same key building steps.This six-session Multilevel SEM Modeling with xxM course is an overview and tutorial of how to perform these key basic building block steps using xxM. To convey a practical understanding of implementing the core model specification and construction concepts of xxM, seven complete illustrative examples are detailed over the six class sessions. One who completes this course will then be able to construct more complex multilevel models tied to their own research projects. The seven complete examples detailed in the course begin with: (1) a streamlined two-level bivariate random-intercepts model; and (2) a two-level random-slopes model. Then a (3) multilevel confirmatory factor analysis (CFA) and a (4) random-slopes multilevel CFA are detailed, followed by random-slopes (5) 'wide' and (6) 'long' latent growth curve model examples. Finally, a (7) three-level hierarchical model containing both observed and latent variables is fully demonstrated. All of the necessary software, data, manuals, slides and course materials to productively specify and estimate all seven of the course model examples are provided and included in 'resources' folders associated with the video lessons.
