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Review
. 2003 Nov;112(4):526-44.
doi: 10.1037/0021-843X.112.4.526.

The use of latent trajectory models in psychopathology research

Patrick J Curran  1 , Andrea M Hussong
Affiliations
Review

The use of latent trajectory models in psychopathology research

Patrick J Curran et al. J Abnorm Psychol. 2003 Nov.

Abstract

Despite the recent surge in the development of powerful modeling strategies to test questions about individual differences in stability and change over time, these methods are not currently widely used in psychopathology research. In an attempt to further the dissemination of these new methods, the authors present a pedagogical introduction to the structural equation modeling based latent trajectory model, or LTM. They review several different types of LTMs, discuss matching an optimal LTM to a given question of interest, and highlight several issues that might be particularly salient for research in psychopathology. The authors augment each section with a review of published applications of these methods in psychopathology-related research to demonstrate the implementation and interpretation of LTMs in practice.

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Figures

Figure 1
Figure 1
Fitted trajectories discussed in Curran and Hussong (2001) for four repeated measures of reading ability assessed on a single child (left panel) and on 75 children (right panel).
Figure 2
Figure 2
Unconditional linear trajectory model for four repeated measures assessed at equal intervals. Y = year.
Figure 3
Figure 3
Unconditional quadratic trajectory model for four repeated measures assessed at equal intervals. Y = year.
Figure 4
Figure 4
Conditional linear trajectory model adapted from Chassin et al. (1996). Factor loadings are fixed to predefined values. Only significant effects are shown in the diagram, and all coefficients are standardized and significant (p < .05). dx = disorders.
Figure 5
Figure 5
Unconditional linear latent trajectory model with time-varying covariates from Hussong et al. (2003). See Hussong et al. (2003) for results from more complex models of this type.
Figure 6
Figure 6
Fully multivariate unconditional latent trajectory model from Curran et al. (1997). Factor loadings are fixed to predefined values. Only significant effects are shown in the diagram, and all coefficients are standardized and significant (p < .05).
Figure 7
Figure 7
Autoregressive latent trajectory model from Hussong et al. (2001). Path coefficients are presented in Table 2 of Hussong et al. (2001).
See this image and copyright information in PMC

References

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