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• W3126686159 abstract "Variability and changes in personality constructs are frequently assessed through longitudinal data. Measurement error, which is ubiquitous in psychology, can lead to bias in the measurement of change. Therefore, personality researchers frequently use latent variable models to analyze longitudinal data. Latent variable models correct for unreliability in the measured scores. In this chapter, we review different classes of longitudinal confirmatory factor approaches, including models for measuring short-term variability (latent state and state–trait models) and models for measuring more long-lasting trait changes (latent autoregressive, latent change, and latent growth curve models). Throughout our presentation, we highlight the flexibility of models for multiple indicators per measurement occasion when it comes to testing for measurement equivalence and examining variable-specific effects. We also present applications to a four-wave (12-month intervals) data set on oppositional defiant disorder and callous/unemotional traits in children." @default.
• W3126686159 created "2021-02-15" @default.
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• W3126686159 date "2021-01-01" @default.
• W3126686159 modified "2023-10-16" @default.
• W3126686159 title "Longitudinal structural equation modeling of personality data" @default.
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• W3126686159 doi "https://doi.org/10.1016/b978-0-12-813995-0.00037-6" @default.
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