ScholarGate
助手

方法对比

并排查看您选择的方法;存在差异的行会高亮显示。

偏最小二乘结构方程模型×探索性结构方程模型×
领域心理测量学心理测量学
方法族Latent structureLatent structure
起源年份19852009
提出者Herman WoldTihomir Asparouhov, Bengt Muthén
类型Component-based structural equation modelHybrid exploratory-confirmatory factor modeling
开创性文献Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (2nd ed.). Sage Publications. ISBN: 9781483377445Asparouhov, T., & Muthén, B. (2009). Exploratory structural equation modeling. Structural Equation Modeling, 16(3), 397-438. DOI ↗
别名PLS-SEM, PLS path modelingESEM
相关55
摘要PLS-SEM is a variance-based approach to structural equation modeling developed by Herman Wold (1985) that estimates latent variable models by maximizing the variance explained in dependent variables. Unlike covariance-based SEM, PLS-SEM is particularly useful for exploratory research, small to medium samples, complex models with many constructs, and non-normal data.Exploratory Structural Equation Modeling (ESEM) is a hybrid approach that combines exploratory factor analysis (EFA) with confirmatory factor analysis (CFA) and path modeling, developed by Asparouhov and Muthén (2009). ESEM relaxes restrictive zero-loading assumptions of traditional CFA, allowing all indicators to load on all factors, which can reveal cross-factor complexity and improve model fit while retaining the ability to test substantive structural theories.
ScholarGate数据集
  1. v1
  2. 3 来源
  3. PUBLISHED
  1. v1
  2. 3 来源
  3. PUBLISHED

前往搜索 下载幻灯片

ScholarGate方法对比: Partial Least Squares Structural Equation Modeling · Exploratory Structural Equation Modeling. 于 2026-06-17 检索自 https://scholargate.app/zh/compare