IDEAS home Printed from https://ideas.repec.org/p/osf/osfxxx/hmy45_v1.html
   My bibliography  Save this paper

About Still Nonignorable Consequences of (Partially) Ignoring Missing Item Responses in Large-scale Assessment

Author

Listed:
  • Robitzsch, Alexander

Abstract

In recent literature, alternative models for handling missing item responses in large-scale assessments are proposed. In principle, based on simulations and arguments based test theory (Rose, 2013). In those approaches, it is argued that missing item responses should never be scored as incorrect, but rather treated as ignorable (e.g., Pohl et al., 2014). The present contribution shows that these arguments have limited validity and illustrates the consequences in a country comparison in the PIRLS 2011 study. A different treatment of missing item responses than recoding them as incorrect leads to significant changes in country rankings, which induces nonignorable consequences regarding the results' validity. Additionally, two alternative item response models based on different assumptions for missing item responses are proposed.

Suggested Citation

  • Robitzsch, Alexander, 2023. "About Still Nonignorable Consequences of (Partially) Ignoring Missing Item Responses in Large-scale Assessment," OSF Preprints hmy45_v1, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:hmy45_v1
    DOI: 10.31219/osf.io/hmy45_v1
    as

    Download full text from publisher

    File URL: https://osf.io/download/5f8f3c19054378000b827031/
    Download Restriction: no
    File URL: https://libkey.io/10.31219/osf.io/hmy45_v1?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. Frederic Lord, 1974. "Estimation of latent ability and item parameters when there are omitted responses," Psychometrika, Springer;The Psychometric Society, vol. 39(2), pages 247-264, June.
    2. Jouni Kuha & Myrsini Katsikatsou & Irini Moustaki, 2018. "Latent variable modelling with non‐ignorable item non‐response: multigroup response propensity models for cross‐national analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(4), pages 1169-1192, October.
    3. Thomas Warm, 1989. "Weighted likelihood estimation of ability in item response theory," Psychometrika, Springer;The Psychometric Society, vol. 54(3), pages 427-450, September.
    4. Guillermo Rosas & Yael Shomer & Stephen R. Haptonstahl, 2015. "No News Is News: Nonignorable Nonresponse in Roll‐Call Data Analysis," American Journal of Political Science, John Wiley & Sons, vol. 59(2), pages 511-528, February.
    5. Norman Rose & Matthias Davier & Benjamin Nagengast, 2017. "Modeling Omitted and Not-Reached Items in IRT Models," Psychometrika, Springer;The Psychometric Society, vol. 82(3), pages 795-819, September.
    6. Ofer Harel & Joseph L. Schafer, 2009. "Partial and latent ignorability in missing-data problems," Biometrika, Biometrika Trust, vol. 96(1), pages 37-50.
    7. Francesco Bartolucci & Giorgio E. Montanari & Silvia Pandolfi, 2018. "Latent Ignorability and Item Selection for Nursing Home Case-Mix Evaluation," Journal of Classification, Springer;The Classification Society, vol. 35(1), pages 172-193, April.
    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. Robitzsch, Alexander, 2020. "About Still Nonignorable Consequences of (Partially) Ignoring Missing Item Responses in Large-scale Assessment," OSF Preprints hmy45, Center for Open Science.
    2. Jouni Kuha & Myrsini Katsikatsou & Irini Moustaki, 2018. "Latent variable modelling with non‐ignorable item non‐response: multigroup response propensity models for cross‐national analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(4), pages 1169-1192, October.
    3. Simon Grund & Oliver Lüdtke & Alexander Robitzsch, 2021. "On the Treatment of Missing Data in Background Questionnaires in Educational Large-Scale Assessments: An Evaluation of Different Procedures," Journal of Educational and Behavioral Statistics, , vol. 46(4), pages 430-465, August.
    4. Matthias von Davier & Youngmi Cho & Tianshu Pan, 2019. "Effects of Discontinue Rules on Psychometric Properties of Test Scores," Psychometrika, Springer;The Psychometric Society, vol. 84(1), pages 147-163, March.
    5. Vernon T. Farewell & Li Su & Christopher Jackson, 2019. "Partially hidden multi-state modelling of a prolonged disease state defined by a composite outcome," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(4), pages 696-711, October.
    6. Torberg Falch & Justina AV Fischer, 2008. "Does a generous welfare state crowd out student achievement? Panel data evidence from international student tests," TWI Research Paper Series 31, Thurgauer Wirtschaftsinstitut, Universität Konstanz.
    7. Steger, Diana & Schroeders, Ulrich & Wilhelm, Oliver, 2019. "On the dimensionality of crystallized intelligence: A smartphone-based assessment," Intelligence, Elsevier, vol. 72(C), pages 76-85.
    8. Michela Battauz & Ruggero Bellio, 2011. "Structural Modeling of Measurement Error in Generalized Linear Models with Rasch Measures as Covariates," Psychometrika, Springer;The Psychometric Society, vol. 76(1), pages 40-56, January.
    9. Xiang Liu & James Yang & Hui Soo Chae & Gary Natriello, 2020. "Power Divergence Family of Statistics for Person Parameters in IRT Models," Psychometrika, Springer;The Psychometric Society, vol. 85(2), pages 502-525, June.
    10. Klaas Sijtsma & Jules L. Ellis & Denny Borsboom, 2024. "Recognize the Value of the Sum Score, Psychometrics’ Greatest Accomplishment," Psychometrika, Springer;The Psychometric Society, vol. 89(1), pages 84-117, March.
    11. repec:zbw:rwidps:0023 is not listed on IDEAS
    12. Schmidt, Christoph & Fertig, Michael, 2002. "The Role of Background Factors for Reading Literacy: Straight National scores in the Pisa 2000 Study," CEPR Discussion Papers 3544, C.E.P.R. Discussion Papers.
    13. Fertig, Michael, 2003. "Educational Production, Endogenous Peer Group Formation and Class Composition – Evidence from the PISA 2000 Study," IZA Discussion Papers 714, Institute of Labor Economics (IZA).
    14. Elina Tsigeman & Sebastian Silas & Klaus Frieler & Maxim Likhanov & Rebecca Gelding & Yulia Kovas & Daniel Müllensiefen, 2022. "The Jack and Jill Adaptive Working Memory Task: Construction, Calibration and Validation," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-29, January.
    15. Martin Biehler & Heinz Holling & Philipp Doebler, 2015. "Saddlepoint Approximations of the Distribution of the Person Parameter in the Two Parameter Logistic Model," Psychometrika, Springer;The Psychometric Society, vol. 80(3), pages 665-688, September.
    16. A. R. Linero, 2017. "Bayesian nonparametric analysis of longitudinal studies in the presence of informative missingness," Biometrika, Biometrika Trust, vol. 104(2), pages 327-341.
    17. Liu, Huacong & Fernandez, Frank & Dutz, Gregor, 2022. "Educational attainment, use of numeracy at work, and gender wage gaps: Evidence from 12 middle-income countries," International Journal of Educational Development, Elsevier, vol. 92(C).
    18. Alexander Robitzsch, 2021. "A Comprehensive Simulation Study of Estimation Methods for the Rasch Model," Stats, MDPI, vol. 4(4), pages 1-23, October.
    19. Laurent Bouton & Paola Conconi & Francisco Pino & Maurizio Zanardi, 2021. "The Tyranny of the Single-Minded: Guns, Environment, and Abortion," The Review of Economics and Statistics, MIT Press, vol. 103(1), pages 48-59, March.
    20. J. R. Lockwood & Katherine E. Castellano & Benjamin R. Shear, 2018. "Flexible Bayesian Models for Inferences From Coarsened, Group-Level Achievement Data," Journal of Educational and Behavioral Statistics, , vol. 43(6), pages 663-692, December.
    21. J. R. Lockwood & Daniel F. McCaffrey, 2017. "Simulation-Extrapolation with Latent Heteroskedastic Error Variance," Psychometrika, Springer;The Psychometric Society, vol. 82(3), pages 717-736, September.

    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:hmy45_v1. 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.