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Assessing Direct and Indirect Effects in Multilevel Designs with Latent Variables

Author

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  • STEPHEN W. RAUDENBUSH

    (University of Michigan)

  • ROBERT SAMPSON

    (University of Chicago)

Abstract

Researchers commonly ask whether relationships between exogenous predictors, X, and outcomes, Y, are mediated by a third set of variables, Z. Simultaneous equations decompose the relationship between X and Y into an indirect component, operating through Z, and a direct component, the relationship between X and Y given Z. Often, X, Y, and/or Z are measured with error. Structural equation modeling is widely used in this scenario. However, sociological data commonly have a nested structure (students within schools, residents within local areas). Hierarchical linear models represent such multilevel data well and can handle errors of measurement, but have not incorporated simultaneous equations for direct and indirect effects. This article incorporates the study of such mediated effects into the hierarchical linear model, naturally extending the analysis to include unbalanced, multilevel designs and missing data. The authors illustrate the approach by examining the extent to which neighborhood social control mediates the relationship between neighborhood social composition and violence in Chicago.

Suggested Citation

  • Stephen W. Raudenbush & Robert Sampson, 1999. "Assessing Direct and Indirect Effects in Multilevel Designs with Latent Variables," Sociological Methods & Research, , vol. 28(2), pages 123-153, November.
    • Handle: RePEc:sae:somere:v:28:y:1999:i:2:p:123-153
    DOI: 10.1177/0049124199028002001
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    References listed on IDEAS

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    1. Francisco L. Rivera-Batiz, 1992. "Quantitative Literacy and the Likelihood of Employment among Young Adults in the United States," Journal of Human Resources, University of Wisconsin Press, vol. 27(2), pages 313-328.
    2. Geoffrey Woodhouse & Min Yang & Harvey Goldstein & Jon Rasbash, 1996. "Adjusting for Measurement Error in Multilevel Analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 159(2), pages 201-212, March.
    3. 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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    Cited by:

    1. Glass, Thomas A. & McAtee, Matthew J., 2006. "Behavioral science at the crossroads in public health: Extending horizons, envisioning the future," Social Science & Medicine, Elsevier, vol. 62(7), pages 1650-1671, April.
    2. Joop J. Hox & Cora J. M. Maas & Matthieu J. S. Brinkhuis, 2010. "The effect of estimation method and sample size in multilevel structural equation modeling," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 64(2), pages 157-170, May.

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