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

Multiphase mixed-effects models for repeated measures data.

Robert Cudeck1, Kelli J Klebe

  • 1Department of Psychology, University of Minnesota, Minneapolis 55455, USA. cudeck@umn.edu

Psychological Methods
|April 4, 2002
PubMed
Summary
This summary is machine-generated.

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

A Version of Quadratic Regression with Interpretable Parameters.

Multivariate behavioral research·2016
Same author

Fitting Partially Nonlinear Random Coefficient Models as SEMs.

Multivariate behavioral research·2016
Same author

The Barriers to Mental Health Services Scale Revised: Psychometric Analysis Among Older Adults.

Mental health & prevention·2015
Same author

Motivational and cognitive pathways to medical help-seeking for Alzheimer's disease: a cognitive impairment response model.

The journals of gerontology. Series B, Psychological sciences and social sciences·2014
Same author

Nonlinear random-effects mixture models for repeated measures.

Psychometrika·2013
Same author

Utility of the SLUMS as a cognitive screening tool among a nonveteran sample of older adults.

The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry·2013
Same journal

Bayesian evaluation for latent variable models: A tutorial on computing information criteria and bayes factors with the r package bleval.

Psychological methods·2026
Same journal

A stochastic block prior for clustering in graphical models.

Psychological methods·2026
Same journal

Three-level vector autoregressive models.

Psychological methods·2026
Same journal

Scaling cognitive modeling to big data: A deep learning approach to studying individual differences in evidence accumulation model parameters.

Psychological methods·2026
Same journal

Best practices in multilevel modeling for within-cluster group comparisons: An evaluation of coding strategies reflecting group composition and heterogeneity.

Psychological methods·2026
Same journal

A unified framework for psychometrics in experimental psychology: The standardized generalized hierarchical factor model.

Psychological methods·2026
See all related articles

This study reviews a mixed-effects model to analyze phased behavioral development. The model incorporates a change point, allowing for individual transitions between developmental phases at varying times.

Area of Science:

  • Developmental Psychology
  • Biostatistics
  • Behavioral Science

Background:

  • Behavioral development often occurs in distinct phases with varying rates of change.
  • Traditional models may not adequately capture these phase-specific changes.
  • Identifying transition points between developmental phases is crucial for understanding individual trajectories.

Purpose of the Study:

  • To review a mixed-effects model designed for analyzing responses that exhibit identifiable developmental regimes.
  • To highlight the role and estimation of the change point within this statistical framework.
  • To demonstrate the model's flexibility in accommodating individual differences in developmental transitions.

Main Methods:

  • Utilized a mixed-effects modeling approach to statistically represent phased behavioral development.

Related Experiment Videos

  • Incorporated the concept of a 'change point' as a key component of the model.
  • Explored the possibility of the change point acting as a random coefficient to model individual variability.

    • Main Results:

    • The reviewed mixed-effects model effectively captures distinct developmental phases and their differing rates of change.
    • The change point, representing the transition between phases, can be modeled as a random coefficient.
    • This random coefficient approach allows for individual-specific transition times (e.g., age, treatment duration).

    Conclusions:

    • Mixed-effects models provide a robust framework for analyzing phased developmental behavior.
    • The inclusion of a random change point enhances the model's ability to represent individual developmental trajectories.
    • The described methodology is estimable using widely available statistical software.