IDEAS home Printed from https://ideas.repec.org/a/sae/sagope/v8y2018i4p2158244018820380.html
   My bibliography  Save this article

Model Misspecification and Assumption Violations With the Linear Mixed Model: A Meta-Analysis

Author

Listed:
  • Brandon LeBeau
  • Yoon Ah Song
  • Wei Cheng Liu

Abstract

This meta-analysis attempts to synthesize the Monte Carlo (MC) literature for the linear mixed model under a longitudinal framework. The meta-analysis aims to inform researchers about conditions that are important to consider when evaluating model assumptions and adequacy. In addition, the meta-analysis may be helpful to those wishing to design future MC simulations in identifying simulation conditions. The current meta-analysis will use the empirical type I error rate as the effect size and MC simulation conditions will be coded to serve as moderator variables. The type I error rate for the fixed and random effects will be explored as the primary dependent variable. Effect sizes were coded from 13 studies, resulting in a total of 4,002 and 621 effect sizes for fixed and random effects respectively. Meta-regression and proportional odds models were used to explore variation in the empirical type I error rate effect sizes. Implications for applied researchers and researchers planning new MC studies will be explored.

Suggested Citation

  • Brandon LeBeau & Yoon Ah Song & Wei Cheng Liu, 2018. "Model Misspecification and Assumption Violations With the Linear Mixed Model: A Meta-Analysis," SAGE Open, , vol. 8(4), pages 21582440188, December.
    • Handle: RePEc:sae:sagope:v:8:y:2018:i:4:p:2158244018820380
    DOI: 10.1177/2158244018820380
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/2158244018820380
    Download Restriction: no
    File URL: https://libkey.io/10.1177/2158244018820380?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. Viechtbauer, Wolfgang, 2010. "Conducting Meta-Analyses in R with the metafor Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i03).
    2. Maas, Cora J. M. & Hox, J.J.Joop J., 2004. "The influence of violations of assumptions on multilevel parameter estimates and their standard errors," Computational Statistics & Data Analysis, Elsevier, vol. 46(3), pages 427-440, June.
    3. Browne, William J. & Draper, David & Goldstein, Harvey & Rasbash, Jon, 2002. "Bayesian and likelihood methods for fitting multilevel models with complex level-1 variation," Computational Statistics & Data Analysis, Elsevier, vol. 39(2), pages 203-225, April.
    4. William Browne & Harvey Goldstein, 2010. "MCMC Sampling for a Multilevel Model With Nonindependent Residuals Within and Between Cluster Units," Journal of Educational and Behavioral Statistics, , vol. 35(4), pages 453-473, August.
    5. Anne C. Black & Ofer Harel & D. Betsy McCoach, 2011. "Missing data techniques for multilevel data: implications of model misspecification," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(9), pages 1845-1865, September.
    6. Yee, Thomas W., 2010. "The VGAM Package for Categorical Data Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 32(i10).
    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. William J. Browne, 2022. "A celebration of Harvey Goldstein’s lifetime contributions: Memories of working with Harvey Goldstein on multilevel modelling methods and applications," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 753-758, July.
    2. Whitney S Beck & Ed K Hall, 2018. "Confounding factors in algal phosphorus limitation experiments," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-19, October.
    3. Bart Verkuil & Serpil Atasayi & Marc L Molendijk, 2015. "Workplace Bullying and Mental Health: A Meta-Analysis on Cross-Sectional and Longitudinal Data," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-16, August.
    4. Francesca Pilotto & Ingolf Kühn & Rita Adrian & Renate Alber & Audrey Alignier & Christopher Andrews & Jaana Bäck & Luc Barbaro & Deborah Beaumont & Natalie Beenaerts & Sue Benham & David S. Boukal & , 2020. "Meta-analysis of multidecadal biodiversity trends in Europe," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    5. repec:cup:judgdm:v:15:y:2020:i:6:p:972-988 is not listed on IDEAS
    6. Jonas Schmidt & Tammo H. A. Bijmolt, 2020. "Accurately measuring willingness to pay for consumer goods: a meta-analysis of the hypothetical bias," Journal of the Academy of Marketing Science, Springer, vol. 48(3), pages 499-518, May.
    7. Mario Herberz & Tobias Brosch & Ulf J. J. Hahnel, 2020. "Kilo what? Default units increase value sensitivity in joint evaluations of energy efficiency," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 15(6), pages 972-988, November.
    8. Jiang, Xianfeng & Packer, Frank, 2019. "Credit ratings of Chinese firms by domestic and global agencies: Assessing the determinants and impact," Journal of Banking & Finance, Elsevier, vol. 105(C), pages 178-193.
    9. Xi Liu & Yiqiao Jin & Yifan Yang & Xiaoqing Pan, 2023. "Properties and Estimations of a Multivariate Folded Normal Distribution," Mathematics, MDPI, vol. 11(23), pages 1-15, December.
    10. Piers Steel & Sjoerd Beugelsdijk & Herman Aguinis, 2021. "The anatomy of an award-winning meta-analysis: Recommendations for authors, reviewers, and readers of meta-analytic reviews," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 52(1), pages 23-44, February.
    11. Augusteijn, Hilde Elisabeth Maria & van Aert, Robbie Cornelis Maria & van Assen, Marcel A. L. M., 2021. "Posterior Probabilities of Effect Sizes and Heterogeneity in Meta-Analysis: An Intuitive Approach of Dealing with Publication Bias," OSF Preprints avkgj, Center for Open Science.
    12. Georgiou, George K. & Guo, Kan & Naveenkumar, Nithya & Vieira, Ana Paula Alves & Das, J.P., 2020. "PASS theory of intelligence and academic achievement: A meta-analytic review," Intelligence, Elsevier, vol. 79(C).
    13. Geller, Susann & Wilhelm, Oliver & Wacker, Jan & Hamm, Alfons & Hildebrandt, Andrea, 2017. "Associations of the COMT Val158Met polymorphism with working memory and intelligence – A review and meta-analysis," Intelligence, Elsevier, vol. 65(C), pages 75-92.
    14. Gignac, Gilles E. & Bates, Timothy C., 2017. "Brain volume and intelligence: The moderating role of intelligence measurement quality," Intelligence, Elsevier, vol. 64(C), pages 18-29.
    15. Stephan Kambach & Ingolf Kühn & Bastien Castagneyrol & Helge Bruelheide, 2016. "The Impact of Tree Diversity on Different Aspects of Insect Herbivory along a Global Temperature Gradient - A Meta-Analysis," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-14, November.
    16. Simon Grund & Oliver Lüdtke & Alexander Robitzsch, 2018. "Multiple Imputation of Missing Data at Level 2: A Comparison of Fully Conditional and Joint Modeling in Multilevel Designs," Journal of Educational and Behavioral Statistics, , vol. 43(3), pages 316-353, June.
    17. Michail Tsagris & Christina Beneki & Hossein Hassani, 2014. "On the Folded Normal Distribution," Mathematics, MDPI, vol. 2(1), pages 1-17, February.
    18. repec:cup:judgdm:v:14:y:2019:i:3:p:234-279 is not listed on IDEAS
    19. Senlin Zhou & Yunpeng Wu & Xizheng Xu, 2023. "Linking Cognitive Reappraisal and Expressive Suppression to Mindfulness: A Three-Level Meta-Analysis," IJERPH, MDPI, vol. 20(2), pages 1-16, January.
    20. Mahesh Shumsher Rughooputh & Rui Zeng & Ying Yao, 2015. "Protein Diet Restriction Slows Chronic Kidney Disease Progression in Non-Diabetic and in Type 1 Diabetic Patients, but Not in Type 2 Diabetic Patients: A Meta-Analysis of Randomized Controlled Trials ," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-17, December.
    21. de la Cruz, Vera Ysabel V. & Tantriani, & Cheng, Weiguo & Tawaraya, Keitaro, 2023. "Yield gap between organic and conventional farming systems across climate types and sub-types: A meta-analysis," Agricultural Systems, Elsevier, vol. 211(C).
    22. Christopher Winchester & Kelsey E. Medeiros, 2023. "In Bounds but Out of the Box: A Meta-Analysis Clarifying the Effect of Ethicality on Creativity," Journal of Business Ethics, Springer, vol. 183(3), pages 713-743, 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:sagope:v:8:y:2018:i:4:p:2158244018820380. 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.