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lavaan.survey: An R Package for Complex Survey Analysis of Structural Equation Models

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  • Oberski, Daniel

Abstract

This paper introduces the R package lavaan.survey, a user-friendly interface to design-based complex survey analysis of structural equation models (SEMs). By leveraging existing code in the lavaan and survey packages, the lavaan.survey package allows for SEM analyses of stratified, clustered, and weighted data, as well as multiply imputed complex survey data. lavaan.survey provides several features such as SEMs with replicate weights, a variety of resampling techniques for complex samples, and finite population corrections, features that should prove useful for SEM practitioners faced with the common situation of a sample that is not iid.

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  • Oberski, Daniel, 2014. "lavaan.survey: An R Package for Complex Survey Analysis of Structural Equation Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 57(i01).
  • Handle: RePEc:jss:jstsof:v:057:i01
    DOI: http://hdl.handle.net/10.18637/jss.v057.i01
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    1. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    2. Lumley, Thomas, 2004. "Analysis of Complex Survey Samples," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 9(i08).
    3. Su, Yu-Sung & Gelman, Andrew & Hill, Jennifer & Yajima, Masanao, 2011. "Multiple Imputation with Diagnostics (mi) in R: Opening Windows into the Black Box," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i02).
    4. Femke Roosma & John Gelissen & Wim Oorschot, 2013. "The Multidimensionality of Welfare State Attitudes: A European Cross-National Study," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 113(1), pages 235-255, August.
    5. Albert Satorra, 1989. "Alternative test criteria in covariance structure analysis: A unified approach," Psychometrika, Springer;The Psychometric Society, vol. 54(1), pages 131-151, March.
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