Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Testing main effects and interactions in latent curve analysis.

Patrick J Curran1, Daniel J Bauer, Michael T Willoughby

  • 1Department of Psychology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-3270, USA. curran@unc.edu

Psychological Methods
|May 13, 2004
PubMed
Summary

Latent curve analysis (LCA) can better model individual change over time by incorporating time interactions. This study shows how to generalize multiple regression techniques for analyzing these interactions in LCA models.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Improving the Evaluation of Construct Change Over Time: Advantages of Longitudinal Moderated Nonlinear Factor Analysis Over Conventional First-Order Growth Models.

Multivariate behavioral research·2026
Same author

Identifying prospective temperament predictors of callous-unemotional traits using machine learning.

European child & adolescent psychiatry·2026
Same author

Predictive Relations Between Early Adversity, Nonlinear Trajectories of Hypothalamic-Pituitary-Adrenal Axis Functioning, and Later Callous-Unemotional Traits.

Child psychiatry and human development·2026
Same author

Using Machine Learning to Identify Infant and Child Environmental and Biological Predictors of Callous-Unemotional Traits.

Research on child and adolescent psychopathology·2026
Same author

The interplay between family routines and aggressive parenting in predicting externalizing problems during the transition to primary school.

Developmental psychology·2026
Same author

Exploring the role of classroom quality and student-teacher relationship in the executive function and academic performance of first grade children.

Journal of experimental child psychology·2026

Area of Science:

  • Statistics
  • Psychometrics
  • Longitudinal Data Analysis

Background:

  • Latent curve analysis (LCA) models individual differences in change over time.
  • Time is crucial, interacting multiplicatively with explanatory variables in predicting repeated measures.
  • Current LCA methods often underutilize the role of time due to subtle incorporation via factor loadings.

Purpose of the Study:

  • To demonstrate analytically and empirically that standard multiple regression interaction techniques can be adapted for LCA.
  • To highlight the importance of explicitly modeling time interactions in conditional latent curve analysis.
  • To provide practical guidance for researchers using LCA.

Main Methods:

  • Generalizing classic multiple regression interaction probing techniques to the LCA framework.

Related Experiment Videos

  • Analytical derivations to support the proposed generalization.
  • Empirical validation using a worked example.

    • Main Results:

    • Established that multiple regression interaction techniques are generalizable to LCA.
    • Demonstrated the practical application of these techniques through a worked example.
    • Showcased how to effectively capitalize on the multiplicative interaction of time and explanatory variables.

    Conclusions:

    • Recommends the use of generalized interaction techniques for estimating conditional LCAs.
    • Enhances the ability of LCA to model individual variability in rates of change.
    • Improves the analytical power of LCA by explicitly incorporating time dynamics.