Analysis of a Two-Level Structural Equation Model With Missing Data
Publication in refereed journal


摘要Structural equation models are widely used to model relationships among latent unobservable constructs and observable variables. In some studies, the data set used for analysis is comprised of observations that are drawn from a known hierarchical structure and involves missing entries. A two-level structural equation model can be used to analyze such data sets. Direct maximum likelihood methods for analyzing two-level structural equation models are available in software, such as LISREL and Mplus. These software programs also have options to handle missing observations. The authors develop an alternative procedure that uses an expectation maximization (EM) type algorithm. Using appropriate approximations, the procedure can be implemented using simple statistical software in combination with a basic structural equation modeling program. The authors address the implementation of the procedure in detail and provide syntax codes in R, which is available in the public domain, to implement the proposed procedure. The discussion of the procedure is made with reference to the analysis of a data set that studies job characteristic variables. The authors also use simulation studies to examine the performance of the proposed procedure. The results indicate that the proposed method, which is easily accessible to users, represents a reliable alternative for analyzing two-level structural equation models with missing data.
著者Poon WY, Wang HB
期刊名稱Sociological Methods and Research
出版社SAGE Publications (UK and US)
頁次25 - 55
關鍵詞missing data; multilevel research; structural equation model
Web of Science 學科類別Mathematical Methods In Social Sciences; Social Sciences, Mathematical Methods; SOCIAL SCIENCES, MATHEMATICAL METHODS; Sociology; SOCIOLOGY

上次更新時間 2021-25-02 於 00:44