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Published on: July 3, 2020
Piecewise latent growth models: beyond modeling linear-linear processes.
Jeffrey R Harring1, Marian M Strazzeri2, Shelley A Blozis3
1Measurement, Statistics and Evaluation Program, Department of Human Development and Quantitative Methodology, 3492 Campus Drive, 1230-E Benjamin Building, University of Maryland, College Park, MD, 20742-1115, USA. harring@umd.edu.
This study extends piecewise latent growth models (LGMs) beyond linear processes to include higher-order polynomials, three-phase models, and nonlinear functions. These advanced LGMs allow for flexible estimation of changepoints, aiding psychological research.
Area of Science:
- Statistics
- Psychometrics
- Quantitative Psychology
Background:
- Piecewise latent growth models (LGMs) are established for linear processes but lack extensions for complex growth trajectories.
- Existing LGM frameworks have limited advancements in incorporating higher-order polynomials, multiple phases, or nonlinear functions.
- Changepoint estimation in LGMs is often restricted, hindering the analysis of nuanced developmental or behavioral changes.
Purpose of the Study:
- To extend piecewise latent growth models (LGMs) to accommodate higher-order polynomials and inherently nonlinear functions.
- To develop a three-phase piecewise LGM framework for analyzing more complex developmental trajectories.
- To enable the estimation of changepoints as parameters, allowing them to be fixed or vary across individuals.
Main Methods:
- Development of advanced piecewise latent growth models (LGMs) incorporating higher-order polynomials.
- Extension of the basic piecewise LGM framework to a three-phase structure.
- Integration of inherently nonlinear functions within the piecewise LGM framework.
- Estimation of changepoints as parameters, with options for fixed or subject-varying estimation.
Main Results:
- Demonstrated the feasibility of incorporating higher-order polynomials into piecewise latent growth models (LGMs).
- Successfully extended the piecewise LGM framework to model three distinct developmental phases.
- Showcased the application of inherently nonlinear functions within piecewise LGMs for complex growth patterns.
- Illustrated the estimation of changepoints as parameters, adaptable to research needs.
Conclusions:
- The proposed extensions significantly broaden the applicability of piecewise latent growth models (LGMs) in psychological research.
- These advanced LGMs provide a more flexible and accurate approach to modeling complex change over time.
- The availability of annotated software facilitates the adoption of these enhanced methodologies by practitioners and researchers.
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