Maximum likelihood analysis of a two-level nonlinear structural equation model with fixed covariates
Publication in refereed journal


引用次數
替代計量分析
.

其它資訊
摘要In this article, a maximum likelihood (ML) approach for analyzing a rather general two-level structural equation model is developed for hierarchically structured data that are very common in educational and/or behavioral research. The proposed two-level model can accommodate nonlinear causal relations among latent variables as well as effects of fixed covariate in its various components. Methods for computing the ML estimates, and the Bayesian information criterion (BIC) for model comparison are established on the basis of powerful tools in statistical computing such as the Monte Carlo EM algorithm, Gibbs sampler, Metropolis-Hastings algorithm, conditional maximization, bridge sampling, and path sampling. The newly developed procedures are illustrated by results obtained from a simulation study and analysis of a real data set in education.
著者Lee SY, Song XY
期刊名稱Journal of Educational and Behavioral Statistics
出版年份2005
月份3
日期1
卷號30
期次1
出版社AMER EDUCATIONAL RESEARCH ASSOC
頁次1 - 26
國際標準期刊號1076-9986
語言英式英語
關鍵詞Bayesian information criterion; Gibbs sampler; Metropolis-Hastings algorithm; Monte Carlo EM algorithm; path sampling
Web of Science 學科類別Education & Educational Research; EDUCATION & EDUCATIONAL RESEARCH; Mathematical Methods In Social Sciences; Psychology; Psychology, Mathematical; PSYCHOLOGY, MATHEMATICAL; Social Sciences, Mathematical Methods; SOCIAL SCIENCES, MATHEMATICAL METHODS

上次更新時間 2022-10-01 於 23:54