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Understanding Individual-level Change Through the Basis Functions of a Latent Curve Model

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  • Shelley A. Blozis
  • Jeffrey R. Harring

Abstract

Latent curve models have become a popular approach to the analysis of longitudinal data. At the individual level, the model expresses an individual’s response as a linear combination of what are called “basis functions†that are common to all members of a population and weights that may vary among individuals. This article uses differential calculus to define the basis functions of a latent curve model. This provides a meaningful interpretation of the unique and dynamic impact of each basis function on the individual-level response. Examples are provided to illustrate this sensitivity, as well as the sensitivity of the basis functions, to changes in the measure of time.

Suggested Citation

  • 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.
    • Handle: RePEc:sae:somere:v:46:y:2017:i:4:p:793-820
    DOI: 10.1177/0049124115605341
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    References listed on IDEAS

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    1. P. Diggle & M. G. Kenward, 1994. "Informative Drop‐Out in Longitudinal Data Analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 43(1), pages 49-73, March.
    2. 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.
    3. William Meredith & John Tisak, 1990. "Latent curve analysis," Psychometrika, Springer;The Psychometric Society, vol. 55(1), pages 107-122, March.
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