IDEAS home Printed from https://ideas.repec.org/a/sae/jedbes/v36y2011i2p257-276.html
   My bibliography  Save this article

A Comparison of Imputation Methods for Bayesian Factor Analysis Models

Author

Listed:
  • Edgar C. Merkle

Abstract

Imputation methods are popular for the handling of missing data in psychology. The methods generally consist of predicting missing data based on observed data, yielding a complete data set that is amiable to standard statistical analyses. In the context of Bayesian factor analysis, this article compares imputation under an unrestricted multivariate normal model (Multiple Imputation [MI]) to imputation under the statistical model of interest (Data Augmentation [DA]). The former method is popular in applied research, but the latter method is more straightforward from a Bayesian perspective. Simulations demonstrate that DA yields less-biased parameter estimates for moderate sample sizes and high missingness proportions. MI, however, yields less-biased parameter estimates for large sample sizes with misspecified models. The incorporation of auxiliary variables in DA is also addressed, and BUGS code is provided.

Suggested Citation

  • Edgar C. Merkle, 2011. "A Comparison of Imputation Methods for Bayesian Factor Analysis Models," Journal of Educational and Behavioral Statistics, , vol. 36(2), pages 257-276, April.
  • Handle: RePEc:sae:jedbes:v:36:y:2011:i:2:p:257-276
    DOI: 10.3102/1076998610375833
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.3102/1076998610375833
    Download Restriction: no

    File URL: https://libkey.io/10.3102/1076998610375833?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. Sturtz, Sibylle & Ligges, Uwe & Gelman, Andrew, 2005. "R2WinBUGS: A Package for Running WinBUGS from R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 12(i03).
    2. Jae Kwang Kim, 2004. "Fractional hot deck imputation," Biometrika, Biometrika Trust, vol. 91(3), pages 559-578, September.
    3. Richard Scheines & Herbert Hoijtink & Anne Boomsma, 1999. "Bayesian estimation and testing of structural equation models," Psychometrika, Springer;The Psychometric Society, vol. 64(1), pages 37-52, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mariateresa Ciommi & Chiara Gigliarano & Francesco M. Chelli & Mauro Gallegati, 2022. "It is the Total that Does [Not] Make the Sum: Nature, Economy and Society in the Equitable and Sustainable Well-Being of the Italian Provinces," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 161(2), pages 491-522, June.
    2. Vinícius Diniz Mayrink & Renato Valladares Panaro & Marcelo Azevedo Costa, 2021. "Structural equation modeling with time dependence: an application comparing Brazilian energy distributors," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(2), pages 353-383, June.
    3. Terrence D. Jorgensen & Aditi M. Bhangale & Yves Rosseel, 2024. "Two-Stage Limited-Information Estimation for Structural Equation Models of Round-Robin Variables," Stats, MDPI, vol. 7(1), pages 1-34, February.

    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. Lachaud, Michée A. & Bravo-Ureta, Boris E., 2022. "A Bayesian statistical analysis of return to agricultural R&D investment in Latin America: Implications for food security," Technology in Society, Elsevier, vol. 70(C).
    2. Liang, Zhongyao & Qian, Song S. & Wu, Sifeng & Chen, Huili & Liu, Yong & Yu, Yanhong & Yi, Xuan, 2019. "Using Bayesian change point model to enhance understanding of the shifting nutrients-phytoplankton relationship," Ecological Modelling, Elsevier, vol. 393(C), pages 120-126.
    3. Ali Noudoostbeni & Kiran Kaur & Hashem Salarzadeh Jenatabadi, 2018. "A Comparison of Structural Equation Modeling Approaches with DeLone & McLean’s Model: A Case Study of Radio-Frequency Identification User Satisfaction in Malaysian University Libraries," Sustainability, MDPI, vol. 10(7), pages 1-16, July.
    4. Marc Marí-Dell’Olmo & Miguel Ángel Martínez-Beneito, 2015. "A Multilevel Regression Model for Geographical Studies in Sets of Non-Adjacent Cities," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-12, August.
    5. Zhao, Qing & Boomer, G. Scott & Silverman, Emily & Fleming, Kathy, 2017. "Accounting for the temporal variation of spatial effect improves inference and projection of population dynamics models," Ecological Modelling, Elsevier, vol. 360(C), pages 252-259.
    6. David Kaplan & Chansoon Lee, 2018. "Optimizing Prediction Using Bayesian Model Averaging: Examples Using Large-Scale Educational Assessments," Evaluation Review, , vol. 42(4), pages 423-457, August.
    7. Adrian D Vickers & Claire Ainsworth & Reema Mody & Annika Bergman & Caroline S Ling & Jasmina Medjedovic & Michael Smyth, 2016. "Systematic Review with Network Meta-Analysis: Comparative Efficacy of Biologics in the Treatment of Moderately to Severely Active Ulcerative Colitis," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-21, October.
    8. Marco Gramatica & Peter Congdon & Silvia Liverani, 2021. "Bayesian modelling for spatially misaligned health areal data: A multiple membership approach," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(3), pages 645-666, June.
    9. Yong Li & Zeng Tao & Jun Yu, "undated". "Robust Deviance Information Criterion for Latent Variable Models," Working Papers CoFie-04-2012, Singapore Management University, Sim Kee Boon Institute for Financial Economics.
    10. repec:jss:jstsof:36:c01 is not listed on IDEAS
    11. Igor Filatotchev & Natalia Isachenkova & Tomasz Mickiewicz, 2007. "Corporate Governance, Managers' Independence, Exporting, and Performance of Firms in Transition Economies," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 43(5), pages 62-77, October.
    12. Abadi, Fitsum & Gimenez, Olivier & Jakober, Hans & Stauber, Wolfgang & Arlettaz, Raphaël & Schaub, Michael, 2012. "Estimating the strength of density dependence in the presence of observation errors using integrated population models," Ecological Modelling, Elsevier, vol. 242(C), pages 1-9.
    13. Díez-Mesa, Francisco & de Oña, Rocio & de Oña, Juan, 2018. "Bayesian networks and structural equation modelling to develop service quality models: Metro of Seville case study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 118(C), pages 1-13.
    14. Xiaojun Mao & Zhonglei Wang & Shu Yang, 2023. "Matrix completion under complex survey sampling," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(3), pages 463-492, June.
    15. Asim Ansari & Kamel Jedidi & Sharan Jagpal, 2000. "A Hierarchical Bayesian Methodology for Treating Heterogeneity in Structural Equation Models," Marketing Science, INFORMS, vol. 19(4), pages 328-347, August.
    16. Będowska-Sójka, Barbara & Kliber, Agata, 2022. "Can cryptocurrencies hedge oil price fluctuations? A pandemic perspective," Energy Economics, Elsevier, vol. 115(C).
    17. Earl W Duncan & Kerrie L Mengersen, 2020. "Comparing Bayesian spatial models: Goodness-of-smoothing criteria for assessing under- and over-smoothing," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-28, May.
    18. repec:jss:jstsof:40:i05 is not listed on IDEAS
    19. Ton de Waal & Wieger Coutinho, 2017. "Preserving Logical Relations while Estimating Missing Values," Romanian Statistical Review, Romanian Statistical Review, vol. 65(3), pages 47-59, September.
    20. Guy Abel & Jakub Bijak & Jonathan J. Forster & James Raymer & Peter W.F. Smith & Jackie S.T. Wong, 2013. "Integrating uncertainty in time series population forecasts: An illustration using a simple projection model," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 29(43), pages 1187-1226.
    21. Shin-Soo Kang & Kenneth Koehler & Michael Larsen, 2012. "Fractional imputation for incomplete two-way contingency tables," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 75(5), pages 581-599, July.
    22. Yong Li & Zhongxin Ni & Jie Zhang, 2011. "An Efficient Stochastic Simulation Algorithm for Bayesian Unit Root Testing in Stochastic Volatility Models," Computational Economics, Springer;Society for Computational Economics, vol. 37(3), pages 237-248, March.

    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:sae:jedbes:v:36:y:2011:i:2:p:257-276. 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: SAGE Publications (email available below). General contact details of provider: .

    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.