IDEAS home Printed from https://ideas.repec.org/a/bla/jorssc/v69y2020i4p841-861.html
   My bibliography  Save this article

Small sample corrections for Wald tests in latent variable models

Author

Listed:
  • Brice Ozenne
  • Patrick M. Fisher
  • Esben Budtz‐J⊘rgensen

Abstract

Latent variable models are commonly used in psychology and increasingly used for analysing brain imaging data. Such studies typically involve a small number of participants (n

Suggested Citation

  • Brice Ozenne & Patrick M. Fisher & Esben Budtz‐J⊘rgensen, 2020. "Small sample corrections for Wald tests in latent variable models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(4), pages 841-861, August.
    • Handle: RePEc:bla:jorssc:v:69:y:2020:i:4:p:841-861
    DOI: 10.1111/rssc.12414
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/rssc.12414
    Download Restriction: no
    File URL: https://libkey.io/10.1111/rssc.12414?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. Bo‐Cheng Wei & Yue‐Qing Hu & Wing‐Kam Fung, 1998. "Generalized Leverage and its Applications," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 25(1), pages 25-37, March.
    2. Kauermann G. & Carroll R.J., 2001. "A Note on the Efficiency of Sandwich Covariance Matrix Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1387-1396, December.
    3. Rosseel, Yves, 2012. "lavaan: An R Package for Structural Equation Modeling," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i02).
    4. White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
    5. Kenneth Bollen, 1996. "An alternative two stage least squares (2SLS) estimator for latent variable equations," Psychometrika, Springer;The Psychometric Society, vol. 61(1), pages 109-121, March.
    6. Jolynn Pek & Hao Wu, 2015. "Profile Likelihood-Based Confidence Intervals and Regions for Structural Equation Models," Psychometrika, Springer;The Psychometric Society, vol. 80(4), pages 1123-1145, December.
    7. Klaus Holst & Esben Budtz-Jørgensen, 2013. "Linear latent variable models: the lava-package," Computational Statistics, Springer, vol. 28(4), pages 1385-1452, August.
    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. Brice Ozenne & Esben Budtz-Jørgensen & Sebastian Elgaard Ebert, 2023. "Controlling the familywise error rate when performing multiple comparisons in a linear latent variable model," Computational Statistics, Springer, vol. 38(1), pages 1-23, March.

    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. Brice Ozenne & Esben Budtz-Jørgensen & Sebastian Elgaard Ebert, 2023. "Controlling the familywise error rate when performing multiple comparisons in a linear latent variable model," Computational Statistics, Springer, vol. 38(1), pages 1-23, March.
    2. A. Colin Cameron & Douglas L. Miller, 2010. "Robust Inference with Clustered Data," Working Papers 106, University of California, Davis, Department of Economics.
    3. Klaus Holst & Esben Budtz-Jørgensen, 2013. "Linear latent variable models: the lava-package," Computational Statistics, Springer, vol. 28(4), pages 1385-1452, August.
    4. Alexander Robitzsch, 2022. "Comparing the Robustness of the Structural after Measurement (SAM) Approach to Structural Equation Modeling (SEM) against Local Model Misspecifications with Alternative Estimation Approaches," Stats, MDPI, vol. 5(3), pages 1-42, July.
    5. Zachary F. Fisher & Kenneth A. Bollen, 2020. "An Instrumental Variable Estimator for Mixed Indicators: Analytic Derivatives and Alternative Parameterizations," Psychometrika, Springer;The Psychometric Society, vol. 85(3), pages 660-683, September.
    6. Signe M Jensen & Hanne Hauger & Christian Ritz, 2018. "Mediation analysis for logistic regression with interactions: Application of a surrogate marker in ophthalmology," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-7, February.
    7. Shaobo Jin & Fan Yang-Wallentin & Kenneth A. Bollen, 2021. "A unified model-implied instrumental variable approach for structural equation modeling with mixed variables," Psychometrika, Springer;The Psychometric Society, vol. 86(2), pages 564-594, June.
    8. Sanchez-Ruiz, Paul & Wood, Matthew S. & Michaelis, Timothy L. & Suarez, Jaime, 2023. "Entrepreneurs as prime targets: Insights from Mexican ventures on the link between venture visibility and crime of varying severity," Journal of Business Venturing, Elsevier, vol. 38(6).
    9. Christian Gische & Manuel C. Voelkle, 2022. "Beyond the Mean: A Flexible Framework for Studying Causal Effects Using Linear Models," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 868-901, September.
    10. Paul Dudgeon, 2017. "Some Improvements in Confidence Intervals for Standardized Regression Coefficients," Psychometrika, Springer;The Psychometric Society, vol. 82(4), pages 928-951, December.
    11. Soriano, José Luis & Mejía-Trejo, Juan, 2022. "Modelado de Ecuaciones Estructurales en el campo de las Ciencias de la Administración [Structural Equations Modeling in the Management Sciences]," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 33(1), pages 242-263, June.
    12. Clarysse, Bart & Andries, Petra & Boone, Sarah & Roelandt, Jolien, 2023. "Institutional logics and founders' identity orientation: Why academic entrepreneurs aspire lower venture growth," Research Policy, Elsevier, vol. 52(3).
    13. Elissa Burghgraeve & Jan De Neve & Yves Rosseel, 2021. "Estimating Structural Equation Models Using James–Stein Type Shrinkage Estimators," Psychometrika, Springer;The Psychometric Society, vol. 86(1), pages 96-130, March.
    14. Keke Lai, 2019. "Creating Misspecified Models in Moment Structure Analysis," Psychometrika, Springer;The Psychometric Society, vol. 84(3), pages 781-801, September.
    15. Sonia Nawrocka & Hans De Witte & Margherita Pasini & Margherita Brondino, 2023. "A Person-Centered Approach to Job Insecurity: Is There a Reciprocal Relationship between the Quantitative and Qualitative Dimensions of Job Insecurity?," IJERPH, MDPI, vol. 20(7), pages 1-27, March.
    16. Md. Mominur Rahman & Bilkis Akhter, 2021. "The impact of investment in human capital on bank performance: evidence from Bangladesh," Future Business Journal, Springer, vol. 7(1), pages 1-13, December.
    17. Das, Debojyoti & Bhatia, Vaneet & Kumar, Surya Bhushan & Basu, Sankarshan, 2022. "Do precious metals hedge crude oil volatility jumps?," International Review of Financial Analysis, Elsevier, vol. 83(C).
    18. P.A.V.B. Swamy & I-Lok Chang & Jatinder S. Mehta & William H. Greene & Stephen G. Hall & George S. Tavlas, 2016. "Removing Specification Errors from the Usual Formulation of Binary Choice Models," Econometrics, MDPI, vol. 4(2), pages 1-21, June.
    19. Carlo Altavilla & Raffaella Giacomini & Giuseppe Ragusa, 2017. "Anchoring the yield curve using survey expectations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(6), pages 1055-1068, September.
    20. Fernando Rios-Avila & Gustavo Canavire-Bacarreza, 2018. "Standard-error correction in two-stage optimization models: A quasi–maximum likelihood estimation approach," Stata Journal, StataCorp LP, vol. 18(1), pages 206-222, March.

    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:bla:jorssc:v:69:y:2020:i:4:p:841-861. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    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.