IDEAS home Printed from https://ideas.repec.org/a/sae/jedbes/v37y2012i1p31-56.html
   My bibliography  Save this article

The Consequences of Ignoring Individuals' Mobility in Multilevel Growth Models

Author

Listed:
  • Wen Luo

    (University of Wisconsin–Milwaukee)

  • Oi-man Kwok

    (Texas A&M University)

Abstract

In longitudinal multilevel studies, especially in educational settings, it is fairly common that participants change their group memberships over time (e.g., students switch to different schools). Participant’s mobility changes the multilevel data structure from a purely hierarchical structure with repeated measures nested within individuals and individuals nested within clusters to a cross-classified structure with repeated measures cross-classified by both individuals and clusters. If researchers fail to consider the cross-classified data structure and simply use the hierarchical linear models (HLMs) instead of the more appropriate cross-classified random-effects models (CCREMs) to analyze the data, there will be biases in the estimates of variance components and inaccurate statistical inference regarding the fixed effects. In addition, the impact of such model misspecification depends on factors including the rate of mobility and the pattern of mobility.

Suggested Citation

  • Wen Luo & Oi-man Kwok, 2012. "The Consequences of Ignoring Individuals' Mobility in Multilevel Growth Models," Journal of Educational and Behavioral Statistics, , vol. 37(1), pages 31-56, February.
  • Handle: RePEc:sae:jedbes:v:37:y:2012:i:1:p:31-56
    DOI: 10.3102/1076998610394366
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.3102/1076998610394366
    Download Restriction: no

    File URL: https://libkey.io/10.3102/1076998610394366?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. Upali W. Jayasinghe & Herbert W. Marsh & Nigel Bond, 2003. "A multilevel cross‐classified modelling approach to peer review of grant proposals: the effects of assessor and researcher attributes on assessor ratings," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 166(3), pages 279-300, October.
    2. Bieke Fraine & Georges Landeghem & Jan Damme & Patrick Onghena, 2005. "An Analysis of WellBeing in Secondary School with Multilevel Growth Curve models and Multilevel Multivariate Models," Quality & Quantity: International Journal of Methodology, Springer, vol. 39(3), pages 297-316, June.
    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. Stephen A Gallo & Afton S Carpenter & Scott R Glisson, 2013. "Teleconference versus Face-to-Face Scientific Peer Review of Grant Application: Effects on Review Outcomes," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-9, August.
    2. Albert Banal-Estañol & Qianshuo Liu & Inés Macho-Stadler & David Pérez-Castrillo, 2021. "Similar-to-me Effects in the Grant Application Process: Applicants, Panelists, and the Likelihood of Obtaining Funds," Working Papers 1289, Barcelona School of Economics.
    3. Patrícia Martinková & Dan Goldhaber & Elena Erosheva, 2018. "Disparities in ratings of internal and external applicants: A case for model-based inter-rater reliability," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-17, October.
    4. Wen Luo & Oi-Man Kwok, 2010. "Proportional Reduction of Prediction Error in Cross-Classified Random Effects Models," Sociological Methods & Research, , vol. 39(2), pages 188-205, November.
    5. David G Pina & Darko Hren & Ana Marušić, 2015. "Peer Review Evaluation Process of Marie Curie Actions under EU’s Seventh Framework Programme for Research," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-15, June.
    6. Manuel Bagues & Mauro Sylos-Labini & Natalia Zinovyeva, 2017. "Does the Gender Composition of Scientific Committees Matter?," American Economic Review, American Economic Association, vol. 107(4), pages 1207-1238, April.
    7. Yuetong Chen & Hao Wang & Baolong Zhang & Wei Zhang, 2022. "A method of measuring the article discriminative capacity and its distribution," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(6), pages 3317-3341, June.
    8. Smyth, Emer & Russell, Helen, 2021. "Fathers and children from infancy to middle childhood," Research Series, Economic and Social Research Institute (ESRI), number RS130.
    9. Stephen A Gallo & Joanne H Sullivan & Scott R Glisson, 2016. "The Influence of Peer Reviewer Expertise on the Evaluation of Research Funding Applications," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-18, October.
    10. Mario Paolucci & Francisco Grimaldo, 2014. "Mechanism change in a simulation of peer review: from junk support to elitism," Scientometrics, Springer;Akadémiai Kiadó, vol. 99(3), pages 663-688, June.
    11. Marsh, Herbert W. & Jayasinghe, Upali W. & Bond, Nigel W., 2011. "Gender differences in peer reviews of grant applications: A substantive-methodological synergy in support of the null hypothesis model," Journal of Informetrics, Elsevier, vol. 5(1), pages 167-180.
    12. Nkafu Anumendem & Bieke De Fraine & Patrick Onghena & Jan Van Damme, 2013. "The impact of coding time on the estimation of school effects," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(2), pages 1021-1040, February.
    13. Bornmann, Lutz & Mutz, Rüdiger & Hug, Sven E. & Daniel, Hans-Dieter, 2011. "A multilevel meta-analysis of studies reporting correlations between the h index and 37 different h index variants," Journal of Informetrics, Elsevier, vol. 5(3), pages 346-359.
    14. Wiltrud Kuhlisch & Magnus Roos & Jörg Rothe & Joachim Rudolph & Björn Scheuermann & Dietrich Stoyan, 2016. "A statistical approach to calibrating the scores of biased reviewers of scientific papers," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 79(1), pages 37-57, January.
    15. Jens Jirschitzka & Aileen Oeberst & Richard Göllner & Ulrike Cress, 2017. "Inter-rater reliability and validity of peer reviews in an interdisciplinary field," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(2), pages 1059-1092, November.
    16. Carole J. Lee & Cassidy R. Sugimoto & Guo Zhang & Blaise Cronin, 2013. "Bias in peer review," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(1), pages 2-17, January.
    17. Bornmann, Lutz & Mutz, Rüdiger & Daniel, Hans-Dieter, 2008. "Latent Markov modeling applied to grant peer review," Journal of Informetrics, Elsevier, vol. 2(3), pages 217-228.
    18. Gaëlle Vallée-Tourangeau & Ana Wheelock & Tushna Vandrevala & Priscilla Harries, 2022. "Peer reviewers’ dilemmas: a qualitative exploration of decisional conflict in the evaluation of grant applications in the medical humanities and social sciences," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-11, December.
    19. Lieven J. R. Pauwels & Robert Svensson, 2015. "Schools and Child Antisocial Behavior," SAGE Open, , vol. 5(2), pages 21582440155, June.
    20. Elena A. Erosheva & Patrícia Martinková & Carole J. Lee, 2021. "When zero may not be zero: A cautionary note on the use of inter‐rater reliability in evaluating grant peer review," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(3), pages 904-919, July.

    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:37:y:2012:i:1:p:31-56. 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.