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Adjusting for Measurement Error in Multilevel Analysis

Author

Listed:
  • Geoffrey Woodhouse
  • Min Yang
  • Harvey Goldstein
  • Jon Rasbash

Abstract

The effects of adjustment for measurement error are illustrated in a two‐level analysis of an educational data set. It is shown how estimates and conclusions can vary, depending on the degree of measurement error that is assumed to exist in explanatory variables at level 1 and level 2, and in the response variable. The importance of obtaining satisfactory prior estimates of measurement error variances and covariances, and of correctly adjusting for them during analysis, is demonstrated.

Suggested Citation

  • Geoffrey Woodhouse & Min Yang & Harvey Goldstein & Jon Rasbash, 1996. "Adjusting for Measurement Error in Multilevel Analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 159(2), pages 201-212, March.
    • Handle: RePEc:bla:jorssa:v:159:y:1996:i:2:p:201-212
    DOI: 10.2307/2983168
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    Cited by:

    1. Martine AUDIBERT & Jean-Yves LE HESRAN & Stéphanie DOS SANTOS & Hervé LAFARGE & Richard LALOU & Georges Karna KONE, 2013. "Use of health care among the urban poor in Africa: Does the neighbourhood have an impact?," Working Papers 201319, CERDI.
    2. Maaike Jappens & Jan Van Bavel, 2012. "Regional family cultures and child care by grandparents in Europe," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 27(4), pages 85-120.
    3. Francisca Richter & B. Brorsen, 2006. "Aggregate Versus Disaggregate Data in Measuring School Quality," Journal of Productivity Analysis, Springer, vol. 25(3), pages 279-289, June.
    4. Georges Kone & Richard Lalou & Martine Audibert & Hervé Lafarge & Stéphanie dos Santos & Jean-Yves Le Hesran, 2013. "Use of health care among the urban poor in Africa: Does the neighbourhood have an impact?," CERDI Working papers halshs-00878946, HAL.
    5. Harvey Goldstein & Jon Rasbash & William Browne & Geoffrey Woodhouse & Michel Poulain, 2000. "Multilevel Models in the Study of Dynamic Household Structures," European Journal of Population, Springer;European Association for Population Studies, vol. 16(4), pages 373-387, December.
    6. Stephen W. Raudenbush & Robert Sampson, 1999. "Assessing Direct and Indirect Effects in Multilevel Designs with Latent Variables," Sociological Methods & Research, , vol. 28(2), pages 123-153, November.
    7. Micklewright, John & Schnepf, Sylke V. & Silva, Pedro N., 2012. "Peer effects and measurement error: The impact of sampling variation in school survey data (evidence from PISA)," Economics of Education Review, Elsevier, vol. 31(6), pages 1136-1142.
    8. Artur Pokropek, 2015. "Phantom Effects in Multilevel Compositional Analysis," Sociological Methods & Research, , vol. 44(4), pages 677-705, November.
    9. 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.
    10. John Micklewright & Sylke V. Schnepf & Pedro N. Silva, 2010. "Peer effects and measurement error: the impact of sampling variation in school survey data," DoQSS Working Papers 10-13, Quantitative Social Science - UCL Social Research Institute, University College London.
    11. Dougal Hutchison, 2004. "The Effect of Measurement Errors on Apparent Group Level Effects in Educational Progress," Quality & Quantity: International Journal of Methodology, Springer, vol. 38(4), pages 407-424, August.
    12. Raymundo M. Campos Vázquez & Freddy D. Urbina Romero, 2011. "Desempeño educativo en México: la prueba Enlace," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, vol. 26(2), pages 249-292.
    13. Keenan A. Pituch, 1999. "Describing School Effects with Residual Terms," Evaluation Review, , vol. 23(2), pages 190-211, April.

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