IDEAS home Printed from https://ideas.repec.org/a/jss/jstsof/v057i01.html
   My bibliography  Save this article

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

Author

Listed:
  • 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.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: https://www.jstatsoft.org/index.php/jss/article/view/v057i01/v57i01.pdf
    Download Restriction: no
    File URL: https://www.jstatsoft.org/index.php/jss/article/downloadSuppFile/v057i01/lavaan.survey_1.1.tar.gz
    Download Restriction: no
    File URL: https://www.jstatsoft.org/index.php/jss/article/downloadSuppFile/v057i01/v57i01.R
    Download Restriction: no
    File URL: https://www.jstatsoft.org/index.php/jss/article/downloadSuppFile/v057i01/liss.imp.rda
    Download Restriction: no
    File URL: https://www.jstatsoft.org/index.php/jss/article/downloadSuppFile/v057i01/erratum-2014-03-15.txt
    Download Restriction: no
    File URL: https://libkey.io/http://hdl.handle.net/10.18637/jss.v057.i01?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gerko Vink & Laurence E. Frank & Jeroen Pannekoek & Stef Buuren, 2014. "Predictive mean matching imputation of semicontinuous variables," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 68(1), pages 61-90, February.
    2. Raphael Nishimura & James Wagner & Michael Elliott, 2016. "Alternative Indicators for the Risk of Non-response Bias: A Simulation Study," International Statistical Review, International Statistical Institute, vol. 84(1), pages 43-62, April.
    3. Christian Seiler, 2013. "Nonresponse in Business Tendency Surveys: Theoretical Discourse and Empirical Evidence," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 52.
    4. Cheng, Xiaoyue & Cook, Dianne & Hofmann, Heike, 2015. "Visually Exploring Missing Values in Multivariable Data Using a Graphical User Interface," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 68(i06).
    5. Rashid, S. & Mitra, R. & Steele, R.J., 2015. "Using mixtures of t densities to make inferences in the presence of missing data with a small number of multiply imputed data sets," Computational Statistics & Data Analysis, Elsevier, vol. 92(C), pages 84-96.
    6. repec:jss:jstsof:45:i01 is not listed on IDEAS
    7. Humera Razzak & Christian Heumann, 2019. "Hybrid Multiple Imputation In A Large Scale Complex Survey," Statistics in Transition New Series, Polish Statistical Association, vol. 20(4), pages 33-58, December.
    8. Razzak Humera & Heumann Christian, 2019. "Hybrid Multiple Imputation In A Large Scale Complex Survey," Statistics in Transition New Series, Statistics Poland, vol. 20(4), pages 33-58, December.
    9. Josse, Julie & Husson, François, 2016. "missMDA: A Package for Handling Missing Values in Multivariate Data Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 70(i01).
    10. Adel Bosch & Steven F. Koch, 2021. "Individual and Household Debt: Does Imputation Choice Matter?," Working Papers 202141, University of Pretoria, Department of Economics.
    11. Walter L. Leite & Burak Aydin & Dee D. Cetin-Berber, 2021. "Imputation of Missing Covariate Data Prior to Propensity Score Analysis: A Tutorial and Evaluation of the Robustness of Practical Approaches," Evaluation Review, , vol. 45(1-2), pages 34-69, February.
    12. repec:jss:jstsof:45:i03 is not listed on IDEAS
    13. Huaiyu Zang & Hang J. Kim & Bin Huang & Rhonda Szczesniak, 2023. "Bayesian causal inference for observational studies with missingness in covariates and outcomes," Biometrics, The International Biometric Society, vol. 79(4), pages 3624-3636, December.
    14. Speidel, Matthias & Drechsler, Jörg & Jolani, Shahab, 2018. "R package hmi: a convenient tool for hierarchical multiple imputation and beyond," IAB-Discussion Paper 201816, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    15. Denver M. Y. Brown & Carah Porter & Faith Hamilton & Fernanda Almanza & Christina Narvid & Megan Pish & Diego Arizabalo, 2022. "Interactive Associations between Physical Activity and Sleep Duration in Relation to Adolescent Academic Achievement," IJERPH, MDPI, vol. 19(23), pages 1-11, November.
    16. Noémi Kreif & Richard Grieve & Iván Díaz & David Harrison, 2015. "Evaluation of the Effect of a Continuous Treatment: A Machine Learning Approach with an Application to Treatment for Traumatic Brain Injury," Health Economics, John Wiley & Sons, Ltd., vol. 24(9), pages 1213-1228, September.
    17. Abhilash Bandam & Eedris Busari & Chloi Syranidou & Jochen Linssen & Detlef Stolten, 2022. "Classification of Building Types in Germany: A Data-Driven Modeling Approach," Data, MDPI, vol. 7(4), pages 1-23, April.
    18. Boonstra Philip S. & Little Roderick J.A. & West Brady T. & Andridge Rebecca R. & Alvarado-Leiton Fernanda, 2021. "A Simulation Study of Diagnostics for Selection Bias," Journal of Official Statistics, Sciendo, vol. 37(3), pages 751-769, September.
    19. Maciej Berk{e}sewicz & Herman Cherniaiev & Robert Pater, 2021. "Estimating the number of entities with vacancies using administrative and online data," Papers 2106.03263, arXiv.org.
    20. Joost Ginkel & Pieter Kroonenberg, 2014. "Using Generalized Procrustes Analysis for Multiple Imputation in Principal Component Analysis," Journal of Classification, Springer;The Classification Society, vol. 31(2), pages 242-269, July.
    21. Christopher J Greenwood & George J Youssef & Primrose Letcher & Jacqui A Macdonald & Lauryn J Hagg & Ann Sanson & Jenn Mcintosh & Delyse M Hutchinson & John W Toumbourou & Matthew Fuller-Tyszkiewicz &, 2020. "A comparison of penalised regression methods for informing the selection of predictive markers," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-14, November.
    22. Liangyuan Hu & Lihua Li, 2022. "Using Tree-Based Machine Learning for Health Studies: Literature Review and Case Series," IJERPH, MDPI, vol. 19(23), pages 1-13, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jss:jstsof:v:057:i01. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Christopher F. Baum (email available below). General contact details of provider: http://www.jstatsoft.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.