.png){kind=icon}
返回
Multilevel Factor Mixture Modeling for Within-Level Constructs: A Simulation Study
.png){kind=icon}
PRE
AI
DOI：10.1080/10705511.2025.2543288.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Multilevel factor mixture modeling (ML FMM) allows researchers to identify unobserved heterogeneous groups at both within and between levels. However, most FMM applications using multilevel data ignore nesting, using single-level FMM. We investigate the appropriateness of single-level FMM when the multilevel construct is conceptualized at the within level. Furthermore, the efficacy of ML FMM in detecting heterogeneity at different levels is examined through Monte Carlo simulation. The single-level FMM can be used for within constructs, but ML FMM offers higher class enumeration and classification accuracy. We discuss the dynamics of within and between classes of multilevel data and provide practical guidelines.
Keyword:
Factor analysis
information criteria
latent class
mixture
model selection
multilevel
期刊
S
IF:
3.2
论文数:
94
被引数:
2.1W
机构
暂无机构信息
引用论文
Covariate inclusion in factor mixture modeling: Evaluating one-step and three-step approaches under model misspecification and overfitting因子混合模型中的协变量纳入：在模型误设和过拟合条件下评估一步法和三步法.png)
IF0
PREAI
分享收藏The Efficacy of Common Fit Indices for Enumerating Classes in Growth Mixture Models When Nested Data Structure Is IgnoredSage Open
OAAI
Sage OpenIF0
OAAI分享收藏Statistical Power to Detect the Correct Number of Classes in Latent Profile Analysis在潜在轮廓分析中检测正确课程数量的统计能力
OAAI
OAAI分享收藏
查看更多内容