English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Longitudinal processes are often associated with each other over time;therefore, it is important to investigate the associations among developmentalprocesses and understand their joint development. The latent growth curve model(LGCM) with a time-varying covariate (TVC) provides a method to estimate theTVC's effect on a longitudinal outcome while simultaneously modeling theoutcome's change. However, it does not allow the TVC to predict variations inthe random growth coefficients. We propose decomposing the TVC's effect intoinitial trait and temporal states using three methods to address thislimitation. In each method, the baseline of the TVC is viewed as an initialtrait, and the corresponding effects are obtained by regressing randomintercepts and slopes on the baseline value. Temporal states are characterizedas (1) interval-specific slopes, (2) interval-specific changes, or (3) changesfrom the baseline at each measurement occasion, depending on the method. Wedemonstrate our methods through simulations and real-world data analyses,assuming a linear-linear functional form for the longitudinal outcome. Theresults demonstrate that LGCMs with a decomposed TVC can provide unbiased andprecise estimates with target confidence intervals. We also provide OpenMx andMplus 8 code for these methods with commonly used linear and nonlinearfunctions.

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