Modeling the co-development of correlated processes with longitudinal and cross-construct effects

Area of Science:

• Developmental Psychology
• Educational Psychology
• Quantitative Psychology

Background:

• Analyzing developmental trends requires appropriate statistical methods.
• Existing methods include individual growth curve analysis, cross-lagged regression, and multilevel frameworks.
• The choice of analysis impacts the conceptualization of developmental change.

Purpose of the Study:

• To compare the utility of latent change score models with other growth models.
• To analyze developmental trajectories of oral reading fluency and reading comprehension.
• To examine implications for modeling correlated traits in longitudinal data.

Main Methods:

• Latent change score analysis was applied.
• Data from a longitudinal sample of ~16,000 students (grades 1-4) were used.
• Analyses included comparisons with linear and nonlinear quadratic latent growth models.

Main Results:

• Latent change score models demonstrated utility in analyzing developmental change.
• The findings highlight the effectiveness of this approach over alternative growth models.
• Implications for modeling correlated traits in developmental research were identified.

Conclusions:

• Latent change score analysis provides a valuable framework for studying developmental trends.
• This method offers a more nuanced understanding of changes in reading skills over time.
• The findings support the use of latent change score models in educational and psychological research.