Modeling Growth in Latent Variables Using a Piecewise Function

Nidhi Kohli, Jeffrey R. Harring

Research output: Contribution to journalArticlepeer-review

31 Scopus citations

Abstract

Latent growth curve models with piecewise functions for continuous repeated measures data have become increasingly popular and versatile tools for investigating individual behavior that exhibits distinct phases of development in observed variables. As an extension of this framework, this research study considers a piecewise function for describing segmented change of a latent construct over time where the latent construct is itself measured by multiple indicators gathered at each measurement occasion. The time of transition from one phase to another is not known a priori and thus is a parameter to be estimated. Utility of the model is highlighted in 2 ways. First, a small Monte Carlo simulation is executed to show the ability of the model to recover true (known) growth parameters, including the location of the point of transition (or knot), under different manipulated conditions. Second, an empirical example using longitudinal reading data is fitted via maximum likelihood and results discussed. Mplus (Version 6.1) code is provided in Appendix C to aid in making this class of models accessible to practitioners.

Original languageEnglish (US)
Pages (from-to)370-397
Number of pages28
JournalMultivariate Behavioral Research
Volume48
Issue number3
DOIs
StatePublished - May 2013

Bibliographical note

Funding Information:
The research reported here was funded by a grant from the Institute of Education Sciences, U.S. Department of Education, to the University of Maryland (R305A090152). The opinions expressed are those of the authors and do not represent views of the institute or the U.S. Department of Education.

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