Abstract
Latent variable models offer a conceptual and statistical framework for evaluating the underlying structure of psychological constructs, including personality and psychopathology. Complex structures that combine or compare categorical and dimensional latent variables can be accommodated using mixture modeling approaches, which provide a powerful framework for testing nuanced theories about psychological structure. This special series includes introductory primers on cross-sectional and longitudinal mixture modeling, in addition to empirical examples applying these techniques to real-world data collected in clinical settings. This group of articles is designed to introduce personality assessment scientists and practitioners to a general latent variable framework that we hope will stimulate new research and application of mixture models to the assessment of personality and its pathology.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 253-255 |
| Number of pages | 3 |
| Journal | Journal of Personality Assessment |
| Volume | 96 |
| Issue number | 3 |
| DOIs | |
| State | Published - May 4 2014 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
All Science Journal Classification (ASJC) codes
- Clinical Psychology
- Psychiatry and Mental health
- Health, Toxicology and Mutagenesis
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