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Analysis of a Two-Level Structural Equation Model With Missing Data

Author

Listed:
  • Wai-Yin Poon

    (The Chinese University of Hong Kong, Shatin, Hong Kong, wypoon@cuhk.edu.hk)

  • Hai-Bin Wang

    (Xiamen University, Xiamen, China)

Abstract

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.

Suggested Citation

  • Wai-Yin Poon & Hai-Bin Wang, 2010. "Analysis of a Two-Level Structural Equation Model With Missing Data," Sociological Methods & Research, , vol. 39(1), pages 25-55, August.
    • Handle: RePEc:sae:somere:v:39:y:2010:i:1:p:25-55
    DOI: 10.1177/0049124110371312
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    References listed on IDEAS

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    1. Jiajuan Liang & Peter Bentler, 2004. "An EM algorithm for fitting two-level structural equation models," Psychometrika, Springer;The Psychometric Society, vol. 69(1), pages 101-122, March.
    2. Yau, Linda H. -Y. & Lee, Sik-Yum & Poon, Wai-Yin, 1993. "Covariance structure analysis with three-level data," Computational Statistics & Data Analysis, Elsevier, vol. 15(2), pages 159-178, February.
    3. Xin-Yuan Song & Sik-Yum Lee, 2002. "Analysis of structural equation model with ignorable missing continuous and polytomous data," Psychometrika, Springer;The Psychometric Society, vol. 67(2), pages 261-288, June.
    4. Harvey Goldstein & Roderick McDonald, 1988. "A general model for the analysis of multilevel data," Psychometrika, Springer;The Psychometric Society, vol. 53(4), pages 455-467, December.
    5. Kevin Y Au, 1999. "Intra-cultural Variation: Evidence and Implications for International Business," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 30(4), pages 799-812, December.
    6. Roderick McDonald, 1993. "A general model for two-level data with responses missing at random," Psychometrika, Springer;The Psychometric Society, vol. 58(4), pages 575-585, December.
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