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Asymptotic robustness of standard errors in multilevel structural equation models

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  • Yuan, Ke-Hai
  • Bentler, Peter M.

Abstract

Data in social and behavioral sciences are often hierarchically organized. Multilevel statistical methodology was developed to analyze such data. Most of the procedures for analyzing multilevel data are derived from maximum likelihood based on the normal distribution assumption. Standard errors for parameter estimates in these procedures are obtained from the corresponding information matrix. Because practical data typically contain heterogeneous marginal skewnesses and kurtoses, this paper studies how nonnormally distributed data affect the standard errors of parameter estimates in a two-level structural equation model. Specifically, we study how skewness and kurtosis in one level affect standard errors of parameter estimates within its level and outside its level. We also show that, parallel to asymptotic robustness theory in conventional factor analysis, conditions exist for asymptotic robustness of standard errors in a multilevel factor analysis model.

Suggested Citation

  • Yuan, Ke-Hai & Bentler, Peter M., 2006. "Asymptotic robustness of standard errors in multilevel structural equation models," Journal of Multivariate Analysis, Elsevier, vol. 97(5), pages 1121-1141, May.
    • Handle: RePEc:eee:jmvana:v:97:y:2006:i:5:p:1121-1141
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    References listed on IDEAS

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    1. Poon, Wai-Yin & Lee, Sik-Yum, 1994. "A distribution free approach for analysis of two-level structural equation model," Computational Statistics & Data Analysis, Elsevier, vol. 17(3), pages 265-275, March.
    2. Yuan, Ke-Hai & Bentler, Peter M., 1999. "On asymptotic distributions of normal theory MLE in covariance structure analysis under some nonnormal distributions," Statistics & Probability Letters, Elsevier, vol. 42(2), pages 107-113, April.
    3. Ke-Hai Yuan & Peter Bentler, 2004. "On the asymptotic distributions of two statistics for two-level covariance structure models within the class of elliptical distributions," Psychometrika, Springer;The Psychometric Society, vol. 69(3), pages 437-457, September.
    4. Yuan, Ke-Hai & Jennrich, Robert I., 1998. "Asymptotics of Estimating Equations under Natural Conditions," Journal of Multivariate Analysis, Elsevier, vol. 65(2), pages 245-260, May.
    5. Yuan, Ke-Hai & Bentler, Peter M., 2003. "Eight test statistics for multilevel structural equation models," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 89-107, October.
    6. 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.
    7. Ke-Hai Yuan & Peter Bentler, 2002. "On normal theory based inference for multilevel models with distributional violations," Psychometrika, Springer;The Psychometric Society, vol. 67(4), pages 539-561, December.
    8. Kano, Y., 1994. "Consistency Property of Elliptic Probability Density Functions," Journal of Multivariate Analysis, Elsevier, vol. 51(1), pages 139-147, October.
    9. 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.
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    Cited by:

    1. Jonathan Schweig, 2014. "Multilevel Factor Analysis by Model Segregation," Journal of Educational and Behavioral Statistics, , vol. 39(5), pages 394-422, October.
    2. Kirt Butler & Katsushi Okada, 2009. "The relative contribution of conditional mean and volatility in bivariate returns to international stock market indices," Applied Financial Economics, Taylor & Francis Journals, vol. 19(1), pages 1-15.

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