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Double serial correlation for multilevel growth curve models

Author

Listed:
  • Dickson Anumendem
  • Geert Verbeke
  • Bieke De Fraine
  • Patrick Onghena
  • Jan Van Damme

Abstract

Multilevel growth curve models for repeated measures data have become increasingly popular and stand as a flexible tool for investigating longitudinal change in students’ outcome variables. In addition, these models allow the estimation of school effects on students’ outcomes though making strong assumptions about the serial independence of level-1 residuals. This paper introduces a method which takes into account the serial correlation of level-1 residuals and also introduces such serial correlation at level-2 in a complex double serial correlation (DSC) multilevel growth curve model. The results of this study from both real and simulated data show a great improvement in school effects estimates compared to those that have previously been found using multilevel growth curve models without correcting for DSC for both the students’ status and growth criteria. Copyright Springer Science+Business Media B.V. 2013

Suggested Citation

  • Dickson Anumendem & Geert Verbeke & Bieke De Fraine & Patrick Onghena & Jan Van Damme, 2013. "Double serial correlation for multilevel growth curve models," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(3), pages 1413-1427, April.
    • Handle: RePEc:spr:qualqt:v:47:y:2013:i:3:p:1413-1427
    DOI: 10.1007/s11135-011-9598-7
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