2010/07/15 by Daniel Bauer, H. Luz McNaughton Reyes · 1 citation
Computer Science · Mathematics · Psychology · Social Sciences · #Advanced Statistical Modeling Techniques #Artificial intelligence #Cognitive psychology #Computer science #Data mining #Data science #Degree (music) #Developmental psychology #Econometrics #Growth curve (statistics) #Interpretation (philosophy) #Latent growth modeling #Longitudinal data #Machine learning #Mathematics #Multilevel model #Psychology #School Choice and Performance #Statistical model #Trajectory #Urban, Neighborhood, and Segregation Studies
paper · doi:10.1111/j.1750-8606.2010.00129.x
openalex publication_date 2010/07/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
The proper use of statistical models for analyzing individual change over time is critical for the progress of developmental science. Latent curve models, hierarchical linear growth models, group-based trajectory models, and growth mixture models constitute increasingly important tools for longitudinal data analysis. To facilitate their understanding and use, this paper clarifies similarities and differences between these models, with particular attention to the assumptions they make about individual development. An example shows how the results and interpretation vary across model types. Discussion centers on reviewing the strengths and limitations of each approach for developmental research.