Extending growth mixture model to assess heterogeneity in joint development with piecewise linear trajectories in the framework of individual measurement occasions.

Liu, Jin; Perera, Robert A.

doi:10.1037/met0000500

Cited by 5 publications

References 102 publications

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Unveiling the Heterogeneity of Sensation Seeking and Collectivism Development in Chinese Adolescents

Chen,

Wang,

Shen

et al. 2024

Journal of Personality

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ObjectiveUnderstanding the impact of sociocultural tendencies on the personality development of adolescents represents a critical theoretical and practical issue in the field of adolescent development. In the context of China's collectivist culture, the developmental trajectories of and the interaction between sensation seeking and collectivism among adolescents remain largely unknown.MethodThis study examined the heterogeneity of the joint growth patterns of sensation seeking and collectivism and their interactions across distinct latent trajectory classes. We collected 3‐year longitudinal data from 20,225 Chinese adolescents (60.45% male).ResultsWe identified four unique joint developmental trajectories. Contrary to the traditional view that collectivism inherently suppresses sensation seeking, most adolescents (89.52%) exhibited synchronous growth of both dimensions. The development of adolescent sensation‐seeking behavior was significantly influenced by their alignment with societal contribution‐driven happiness and an unquestioning prioritization of collective interests.ConclusionsOur findings underscore the nuanced interplay and commonalities between sensation seeking and collectivism development among Chinese adolescents.

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Unveiling the Heterogeneity of Sensation Seeking and Collectivism Development in Chinese Adolescents

Chen,

Wang,

Shen

et al. 2024

Journal of Personality

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Decomposing Impact on Longitudinal Outcome of Time-Varying Covariate into Baseline Effect and Temporal Effect

Liu

2024

Journal of Educational and Behavioral Statistics

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Longitudinal processes are often associated with each other over time; therefore, it is important to investigate the associations among developmental processes and understand their joint development. The traditional latent growth curve model (LGCM) with a time-varying covariate (TVC) provides a method to estimate the TVC effect on a longitudinal outcome while modeling the outcome’s change. However, it does not allow the TVC to predict variations in the random growth coefficients. We propose decomposing the TVC into initial trait and temporal states using three methods to address this limitation. In each method, the baseline of the TVC is viewed as an initial trait, and the corresponding effects are obtained by regressing random intercepts and slopes on the baseline value. Temporal states are characterized as (a) interval-specific slopes, (b) interval-specific changes, or (c) changes from the baseline at each measurement occasion, depending on the method. We demonstrate our methods through simulations and real-world data analyses, assuming a linear–linear functional form for the longitudinal outcome. The results demonstrate that LGCMs with a decomposed TVC can provide unbiased and precise estimates with target confidence intervals. We also provide OpenMx and Mplus 8 code for these methods with commonly used linear and nonlinear functions.

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nlpsem: Linear and Nonlinear Longitudinal Process in Structural Equation Modeling Framework

Liu

2023

CRAN: Contributed Packages

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Extending growth mixture model to assess heterogeneity in joint development with piecewise linear trajectories in the framework of individual measurement occasions.

Cited by 5 publications

References 102 publications

Unveiling the Heterogeneity of Sensation Seeking and Collectivism Development in Chinese Adolescents

Unveiling the Heterogeneity of Sensation Seeking and Collectivism Development in Chinese Adolescents

Decomposing Impact on Longitudinal Outcome of Time-Varying Covariate into Baseline Effect and Temporal Effect

nlpsem: Linear and Nonlinear Longitudinal Process in Structural Equation Modeling Framework

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