Bibliographic Details
| Title: |
Latent Growth Curve Models. |
| Authors: |
Burant, Christopher J.1,2 cxb43@case.edu |
| Source: |
International Journal of Aging & Human Development. Apr2016, Vol. 82 Issue 4, p336-350. 15p. 8 Diagrams. |
| Subject Terms: |
*Mental depression, *Longitudinal method, Chi-squared test, Confidence intervals, Goodness-of-fit tests, Research funding, Structural models, Structural equation modeling, Data analysis software, Descriptive statistics |
| Geographic Terms: |
Midwest (U.S.) |
| Abstract: |
The latent growth curve model (LGCM) is a useful tool in analyzing longitudinal data. It is particularly suitable for gerontological research because the LGCM can track the trajectories and changes of phenomena (e.g., physical health and psychological well-being) over time. Specifically, the LGCM compares lines of change across a set of individuals and determines the overall model's line of change. LGCMs can be used to track either linear or curvilinear trajectories. Since the technique uses structural equation modeling, models are also adjusted for measurement error. This article will present a step-by-step approach to setting up, analyzing, and interpreting an LGCM using post—hospitalization recovery in depressive symptomatology as an example. This article will demonstrate how to test linear, quadratic, and freely estimated lines of change using LGCMs with the purpose of finding the line of trajectory for depressive symptoms that best fits the data. [ABSTRACT FROM AUTHOR] |
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| Database: |
Education Research Complete |
