Estimating Age-Based Developmental Trajectories Using Latent Change Score Models Based on Measurement Occasion

Latent change scores models for applied research: A practical guide using Mplus

The present article provides a practical guide for modelling and interpreting several basic applications of the latent change scores (LCS) model, a useful and flexible approach for the analysis of change. The article is addressed to students, researchers and practitioners who are familiar with structural equation modelling but new to LCS. We first provided a gentle introduction to the LCS model using non-technical language and minimal mathematical formalism. We illustrated the basic ideas behind this approach, introducing LCS in its simplest form. We show how this model can be straightforwardly extended to more complex applications, including the dual change score (DCS) model and some of its variants (i.e., the proportional change and the constant change models). We illustrated how the univariate LCS model can be used to determine the growth trajectory of a variable across multiple waves of assessment. Next, we focused on the bivariate case, which allows for the modelling of the dynamic relations between two variables. For each model, we provided easy-to-follow examples of applications based on Schwartz's theory of basic personal values. The examples are accompanied by annotated syntax and output showing how they can be implemented with the Mplus software and how results can be interpreted.

Factors Underpinning the Shift to Eveningness during Early Adolescence: Pubertal Development and Family Conflicts

Biological and psychosocial factors have been related to the shift to eveningness during early adolescence but it is necessary to study them from a longitudinal perspective. This longitudinal study examined the contribution of these factors to the onset of a shift towards eveningness in early adolescence. A sample of 440 (49.9% boys) Spanish adolescents were assessed for pubertal development, family conflicts, and morningness/eveningness. The same measures were taken twice at the age of 12 and one year later (T1: M = 12.47, SD = 0.75 and T2: M = 13.64, SD = 0.78). Pubertal development and family conflicts were considered predictors of morningness/eveningness in a mixed-effects multilevel model. The developmental shift towards eveningness appeared in girls but not in boys. The shift was related to more advanced pubertal development and more conflicts in the family. This study has implications for shaping healthy sleep habits in adolescents and possible interventions focused on family dynamics.

The Importance of Time Metric Precision When Implementing Bivariate Latent Change Score Models

The literature on latent change score models does not discuss the importance of using a precise time metric when structuring the data. This study examined the influence of time metric precision on model estimation, model interpretation, and parameter estimate accuracy in bivariate LCS (BLCS) models through simulation. Longitudinal data were generated with a panel study where assessments took place during a given time window with variation in start time and measurement lag. The data were analyzed using precise time metric, where variation in time was accounted for, and then analyzed using coarse time metric indicating only that the assessment took place during the time window. Results indicated that models estimated using the coarse time metric resulted in biased parameter estimates as well as larger standard errors and larger variances and covariances for intercept and slope. In particular, the coupling parameter estimates-which are unique to BLCS models-were biased with larger standard errors. An illustrative example of longitudinal bivariate relations between math and reading achievement in a nationally representative survey of children is then used to demonstrate how results and conclusions differ when using time metrics of varying precision. Implications and future directions are discussed.

Catching Up on Multilevel Modeling

This review focuses on the use of multilevel models in psychology and other social sciences. We target readers who are catching up on current best practices and sources of controversy in the specification of multilevel models. We first describe common use cases for clustered, longitudinal, and cross-classified designs, as well as their combinations. Using examples from both clustered and longitudinal designs, we then address issues of centering for observed predictor variables: its use in creating interpretable fixed and random effects of predictors, its relationship to endogeneity problems (correlations between predictors and model error terms), and its translation into multivariate multilevel models (using latent-centering within multilevel structural equation models). Finally, we describe novel extensions—mixed-effects location–scale models—designed for predicting differential amounts of variability.

Dynamical Properties and Conceptual Interpretation of Latent Change Score Models

Latent Change Score models (LCS) are a popular tool for the study of dynamics in longitudinal research. They represent processes in which the short-term dynamics have direct and indirect consequences on the long-term behavior of the system. However, this dual interpretation of the model parameters is usually overlooked in the literature, and researchers often find it difficult to see the connection between parameters and specific patterns of change. The goal of this paper is to provide a comprehensive examination of the meaning and interpretation of the parameters in LCS models. Importantly, we focus on their relation to the shape of the trajectories and explain how different specifications of the LCS model involve particular assumptions about the mechanisms of change. On a supplementary website, we present an interactive Shiny App that allows users to explore different sets of parameter values and examine their effects on the predicted trajectories. We also include fully explained code to estimate some of the most relevant specifications of the LCS model with the R-packages lavaan and OpenMx.

Body Mass Index Trajectories during 6-18 Years Old and the Risk of Hypertension in Young Adult: A Longitudinal Study in Chinese Population

Background

Overweight/obesity in childhood is suggested to increase the risk of hypertension later in life. We aimed to assess whether and how body mass index (BMI) trajectories during 6–18 years of age are associated with hypertension in young adulthood (18–37 years) in the Chinese population.

Methods

Based on the China Health and Nutrition Survey (CHNS), a total of 1,872 participants who received ≥2 measurements of BMI during 6–18 years and had assessment of blood pressure (BP) in young adulthood were included. BMI trajectories were explored using latent class growth mixture models, and associations between identified trajectories with hypertension in young adulthood were examined by logistic regression analyses.

Results

Five heterogeneous BMI trajectories were identified: the low slow-increasing (20.03%), low moderate-increasing (56.14%), low rapid-increasing (17.04%), moderate-increasing (3.63%), and elevated-decreasing (3.15%) groups. Compared with the low slow-increasing group, another three increasing groups had gradually elevated risk of hypertension, yielding maximally adjusted odds ratio (95% confidence interval) (OR (95% CI)) of 2.48 (1.39–4.42), 3.24 (1.66–6.31), and 3.28 (1.19–9.08), respectively, whereas the elevated-decreasing group reversed overweight/obesity to normal weight in childhood, rendering its association with hypertension as not statistically significant (OR (95% CI) = 2.74 (0.98–7.65)).

Conclusion

Our study indicates that there are varied BMI trajectories from childhood to adulthood and that an elevated BMI trajectory during childhood is related with an increased risk of hypertension in young adulthood. In contrast, weight loss of children with high initial BMI may mitigate or reverse the risk. Our findings emphasize the importance of BMI continuous monitoring during early life.