IDEAS home Printed from https://ideas.repec.org/p/osf/osfxxx/5eym8.html
   My bibliography  Save this paper

Commentary on/Request for Retraction of Hülsheger (2016): Implausible Assumptions, False Positives, and a Misleading Statement

Author

Listed:
  • Lang, Jonas W. B.

    (University of Exeter)

Abstract

This commentary calls for the retraction of Hülsheger (2016) on the basis of three major issues. The first issue is the use of time-varying psychological constructs as time-invariant predictors in growth models. This assumption leads to biased estimates and false positives (as demonstrated by simulation), questioning the validity of the research report’s findings. The second issue is that the model used in the paper includes an excessive number of random effects relative to the number of observations so that modern mixed-effects modeling software by default does not even fit the model in the article. More appropriate analytical solutions are discussed for both methodological issues. Finally, the third issue is that Hülsheger (2016) used an author note to associate the problematic analyses to the author of this commentary – without consent or awareness of this author.

Suggested Citation

  • Lang, Jonas W. B., 2024. "Commentary on/Request for Retraction of Hülsheger (2016): Implausible Assumptions, False Positives, and a Misleading Statement," OSF Preprints 5eym8, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:5eym8
    DOI: 10.31219/osf.io/5eym8
    as

    Download full text from publisher

    File URL: https://osf.io/download/672e9f7b98eb8ad44a0ddf0a/
    Download Restriction: no
    File URL: https://libkey.io/10.31219/osf.io/5eym8?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. Doran, Harold & Bates, Douglas & Bliese, Paul & Dowling, Maritza, 2007. "Estimating the Multilevel Rasch Model: With the lme4 Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 20(i02).
    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. repec:jss:jstsof:20:i01 is not listed on IDEAS
    2. Minjeong Jeon & Sophia Rabe-Hesketh, 2012. "Profile-Likelihood Approach for Estimating Generalized Linear Mixed Models With Factor Structures," Journal of Educational and Behavioral Statistics, , vol. 37(4), pages 518-542, August.
    3. repec:jss:jstsof:36:c01 is not listed on IDEAS
    4. Alexander Robitzsch, 2021. "A Comprehensive Simulation Study of Estimation Methods for the Rasch Model," Stats, MDPI, vol. 4(4), pages 1-23, October.
    5. Harold Doran, 2023. "A Collection of Numerical Recipes Useful for Building Scalable Psychometric Applications," Journal of Educational and Behavioral Statistics, , vol. 48(1), pages 37-69, February.
    6. repec:jss:jstsof:40:i05 is not listed on IDEAS
    7. Wickelmaier, Florian & Strobl, Carolin & Zeileis, Achim, 2012. "Psychoco: Psychometric Computing in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i01).
    8. Brzezińska Justyna, 2018. "Item Response Theory Models in the Measurement Theory with the Use of ltm Package in R," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 22(1), pages 11-25, March.
    9. Li, Kai & Chen, Pei-Ying & Yan, Erjia, 2019. "Challenges of measuring software impact through citations: An examination of the lme4 R package," Journal of Informetrics, Elsevier, vol. 13(1), pages 449-461.
    10. repec:jss:jstsof:39:i12 is not listed on IDEAS
    11. Sarrias, Mauricio, 2016. "Discrete Choice Models with Random Parameters in R: The Rchoice Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i10).
    12. De Boeck, Paul & Partchev, Ivailo, 2012. "IRTrees: Tree-Based Item Response Models of the GLMM Family," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(c01).
    13. Bates, Douglas & Mächler, Martin & Bolker, Ben & Walker, Steve, 2015. "Fitting Linear Mixed-Effects Models Using lme4," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i01).
    14. Annalina Sarra & Lara Fontanella & Fausto D’Egidio & Paolo Frattone, 2014. "The dimensional assessment of personality in drug addicts: a mixed-effects Rasch model approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(6), pages 3025-3036, November.
    15. Ting Wang & Benjamin Graves & Yves Rosseel & Edgar C. Merkle, 2022. "Computation and application of generalized linear mixed model derivatives using lme4," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 1173-1193, September.
    16. Meng-Jie Wang & Kumar Yogeeswaran & Sivanand Sivaram & Kyle Nash, 2021. "Examining spread of emotional political content among Democratic and Republican candidates during the 2018 US mid-term elections," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-12, 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:osf:osfxxx:5eym8. 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: OSF (email available below). General contact details of provider: https://osf.io/preprints/ .

    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.