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Sensitivity Analysis of Mixed Models for Incomplete Longitudinal Data

Author

Listed:
  • Shu Xu

    (Pennsylvania State University, State College)

  • Shelley A. Blozis

    (University of California, Davis)

Abstract

Mixed models are used for the analysis of data measured over time to study population-level change and individual differences in change characteristics. Linear and nonlinear functions may be used to describe a longitudinal response, individuals need not be observed at the same time points, and missing data, assumed to be missing at random (MAR), may be handled. While the mechanism giving rise to the missing data cannot be determined by the observations, the sensitivity of parameter estimates to missing data assumptions can be studied, for example, by fitting multiple models that make different assumptions about the missing data process. Sensitivity analysis of a mixed model that may include nonlinear parameters when some data are missing is discussed. An example is provided.

Suggested Citation

  • Shu Xu & Shelley A. Blozis, 2011. "Sensitivity Analysis of Mixed Models for Incomplete Longitudinal Data," Journal of Educational and Behavioral Statistics, , vol. 36(2), pages 237-256, April.
    • Handle: RePEc:sae:jedbes:v:36:y:2011:i:2:p:237-256
    DOI: 10.3102/1076998610375836
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    References listed on IDEAS

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    5. William Meredith & John Tisak, 1990. "Latent curve analysis," Psychometrika, Springer;The Psychometric Society, vol. 55(1), pages 107-122, March.
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    Citations

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    Cited by:

    1. Ting Dai & Adam Davey, 2023. "Determining Dimensionality with Dichotomous Variables: A Monte Carlo Simulation Study and Applications to Missing Data in Longitudinal Research," Mathematics, MDPI, vol. 11(6), pages 1-25, March.
    2. Betsy J. Feldman & Sophia Rabe-Hesketh, 2012. "Modeling Achievement Trajectories When Attrition Is Informative," Journal of Educational and Behavioral Statistics, , vol. 37(6), pages 703-736, December.
    3. Andrew T. Karl & Yan Yang & Sharon L. Lohr, 2013. "A Correlated Random Effects Model for Nonignorable Missing Data in Value-Added Assessment of Teacher Effects," Journal of Educational and Behavioral Statistics, , vol. 38(6), pages 577-603, December.
    4. Shelley A. Blozis & Jeffrey R. Harring, 2017. "Understanding Individual-level Change Through the Basis Functions of a Latent Curve Model," Sociological Methods & Research, , vol. 46(4), pages 793-820, November.
    5. Shelley A. Blozis & Ricardo Villarreal & Sweta Thota & Nicholas Imparato, 2019. "Using a two-part mixed-effects model for understanding daily, individual-level media behavior," Journal of Marketing Analytics, Palgrave Macmillan, vol. 7(4), pages 234-250, December.

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