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所在平台: Udemy |
课程主页: https://www.udemy.com/course/structural-equation-modeling-sem-with-lavaan/
课程评论：没有评论
课程名称：使用lavaan的结构方程模型（SEM） 课程概述：本课程是一门“动手实践”的课程，旨在教授如何使用R软件中的lavaan包来指定、估计和解释基于协方差的结构方程模型（SEM），这些模型涉及潜在变量。lavaan（注意在'lavaan'中的“小写L”使用）是潜在变量分析的缩写，表明了开发者Yves Rosseel的长期目标：“提供一套工具，以探索、估计和理解一系列潜在变量模型，包括因子分析、结构方程、纵向、多层次、潜在类、项目反应和缺失数据模型。”课程通过许多“实时”实例（包含R脚本和数据集）来展示和教授如何： (1) 在lavaan语法中指定SEM模型； (2) 配置并评估模型； (3) 执行确认性因子分析（CFA）； (4) 插补和替换缺失数据； (5) 估计中介效应和其他间接效应； (6) 估计和评估多组模型，同时确立测量不变性； (7) 指定和估计潜在（增长）曲线模型，包括使用随机（和潜在）截距和斜率。R的lavaan包是世界一流的“专业级”SEM软件，全球成千上万的SEM专家、研究生及大学教师均在使用。
This "hands-on" course teaches one how to use the R software lavaan package to specify, estimate the parameters of, and interpret covariance-based structural equation (SEM) models that use latent variables. "lavaan" (note the purposeful use of lowercase "L" in 'lavaan') is an acronym for latent variable analysis, and the name suggests the long-term goal of the developer, Yves Rosseel: "to provide a collection of tools that can be used to explore, estimate, and understand a wide family of latent variable models, including factor analysis, structural equation, longitudinal, multilevel, latent class, item response, and missing data models." The course uses and executes many "live" examples (with included R scripts and datasets) using no-cost R and RStudio software to demonstrate and teach how to: (1) specify a SEM model in lavaan syntax; (2) fit and then evaluate your model; (3) perform a CFA; (4) impute and replace missing data; (5) estimate mediating and other indirect effects; (6) estimate and evaluate multigroup models, simultaneously establishing measurement invariance; and (7) specifying and estimating latent (growth) curve models, including the use of random (and latent) intercepts and slopes. The R lavaan package is world-class 'professional-grade' SEM software, used by thousands of SEM experts, graduate students, and college and university faculty around the world.
