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Non-linear Growth Models in Mplus and SAS.
1University of California, Davis Department of Psychology, One Shields Avenue, Davis, CA 95616, 530-752-1880, kjgrimm@ucdavis.edu.
Structural Equation Modeling : a Multidisciplinary Journal
|July 25, 2013
Summary
Researchers can model complex growth patterns using non-linear functions like logistic and Gompertz curves. This study demonstrates fitting these sigmoid curves with Mplus and SAS NLMIXED using academic achievement data.
Area of Science:
- Developmental Psychology
- Biostatistics
- Educational Research
Background:
- Modeling complex developmental patterns requires non-linear growth curves.
- Interpretable parameters are crucial for understanding developmental trajectories.
- Sigmoid functions offer flexible modeling of S-shaped growth.
Purpose of the Study:
- To demonstrate fitting various sigmoid growth curves (logistic, Gompertz, Richards).
- To illustrate the use of Mplus and SAS NLMIXED for non-linear mixed-effects modeling.
- To apply these methods to longitudinal academic achievement data.
Main Methods:
- Utilized Mplus structural equation modeling software.
- Employed the NLMIXED procedure in SAS for non-linear mixed-effects models.
- Fitted logistic, Gompertz, and Richards growth functions to achievement data.
Main Results:
- Successfully fitted multiple non-linear sigmoid growth models to longitudinal data.
- Demonstrated the practical application of Mplus and SAS NLMIXED for growth curve analysis.
- Provided insights into the benefits and limitations of chosen modeling approaches.
Conclusions:
- Non-linear growth curve modeling provides interpretable parameters for complex development.
- Mplus and SAS NLMIXED are effective tools for fitting sigmoid growth functions.
- These methods facilitate the analysis of developmental patterns in educational contexts.
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