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The impact of coding time on the estimation of school effects

Author

Listed:
  • Nkafu Anumendem
  • Bieke De Fraine
  • Patrick Onghena
  • Jan Van Damme

Abstract

Multilevel growth curve models are becoming invaluable in educational research because they model changes in student outcomes efficiently. The coding of the time variable in these models plays a crucial role as illustrated in this study for the case of a three-level quadratic growth curve model. This paper shows clearly how the choice of a time coding affects school effects estimates and their interpretation. A new definition for school effects for growth curve models with random intercepts and slopes is proposed. This study recommends that the choice of a time coding should not only be based on the ease of interpretation and model convergence but also on its consequences on the student status and growth parameter estimates. The current application illustrates that in general the school effects for student growth in well-being and language achievement in secondary school, are greater for student growth than for student status. Copyright Springer Science+Business Media B.V. 2013

Suggested Citation

  • 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.
    • Handle: RePEc:spr:qualqt:v:47:y:2013:i:2:p:1021-1040
    DOI: 10.1007/s11135-011-9581-3
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    References listed on IDEAS

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    1. 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.
    2. David Rogosa & John Willett, 1985. "Understanding correlates of change by modeling individual differences in growth," Psychometrika, Springer;The Psychometric Society, vol. 50(2), pages 203-228, June.
    3. M. Yang & H. Goldstein & A. Heath, 2000. "Multilevel models for repeated binary outcomes: attitudes and voting over the electoral cycle," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 163(1), pages 49-62.
    4. Ledyard Tucker, 1958. "Determination of parameters of a functional relation by factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 23(1), pages 19-23, March.
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