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A comparative study of estimators in multilevel linear models

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  • Sabz Ali
  • Said Ali Shah
  • Seema Zubair
  • Sundas Hussain

Abstract

Multilevel Models are widely used in organizational research, educational research, epidemiology, psychology, biology and medical fields. In this paper, we recommend the situations where Bootstrap procedures through Minimum Norm Quadratic Unbiased Estimator (MINQUE) can be extremely handy than that of Restricted Maximum Likelihood (REML) in multilevel level linear regression models. In our simulation study the bootstrap by means of MINQUE is superior to REML in conditions where normality does not hold. Moreover, the real data application also supports our findings in terms of accuracy of estimates and their standard errors.

Suggested Citation

  • Sabz Ali & Said Ali Shah & Seema Zubair & Sundas Hussain, 2021. "A comparative study of estimators in multilevel linear models," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-10, November.
    • Handle: RePEc:plo:pone00:0259960
    DOI: 10.1371/journal.pone.0259960
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    References listed on IDEAS

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    1. Cora J. M. Maas & Joop J. Hox, 2004. "Robustness issues in multilevel regression analysis," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 58(2), pages 127-137, May.
    2. Amjad Ali & Sabz Ali & Sajjad Ahmad Khan & Dost Muhammad Khan & Kamran Abbas & Alamgir Khalil & Sadaf Manzoor & Umair Khalil, 2019. "Sample size issues in multilevel logistic regression models," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-13, November.
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