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Cited by 277

The DOI matches its Crossref record; 120 of the 120 references it cites were found in a registry or an index.

The Use of Latent Trajectory Models in Psychopathology Research.

Open access
Curran, Patrick J., Hussong, Andrea M.·2003·Journal of Abnormal Psychology

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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Crossref · 10.1037/0021-843x.112.4.526

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Metadata

Authors
Curran, P. J., & Hussong, A. M.
Venue
Journal of Abnormal Psychology
Published
2003-11
Volume
112
Issue
4
Pages
526-544
ISSN
  • 0021-843X (ISSN-L)
  • 1939-1846
OpenAlex
W2011434798
Cited by
277
References
120
FWCI
6.02
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: The Use of Latent Trajectory Models in Psychopathology Research. https://app.cochat.ai/papers/10.1037/0021-843x.112.4.526
The paper
Curran, P. J., & Hussong, A. M. (2003). The Use of Latent Trajectory Models in Psychopathology Research. Journal of Abnormal Psychology. https://doi.org/10.1037/0021-843x.112.4.526

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.