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

Structured latent growth curves for twin data.

M C Neale1, J J McArdle

  • 1Department of Psychiatry, Medical College of Virginia, Richmond 23298, USA. neale@psycho.psi.vcu.edu

Twin Research : the Official Journal of the International Society for Twin Studies
|October 18, 2000
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

Baseline brain function in the preadolescents of the ABCD Study.

Nature neuroscience·2021
Same author

Multilevel Twin Models: Geographical Region as a Third Level Variable.

Behavior genetics·2021
Same author

Genome-wide association study across European and African American ancestries identifies a SNP in DNMT3B contributing to nicotine dependence.

Molecular psychiatry·2017
Same author

Do DSM-5 Section II personality disorders and Section III personality trait domains reflect the same genetic and environmental risk factors?

Psychological medicine·2017
Same author

The association between personality disorders with alcohol use and misuse: A population-based twin study.

Drug and alcohol dependence·2017
Same author

The Genetic and Environmental Sources of Resemblance Between Normative Personality and Personality Disorder Traits.

Journal of personality disorders·2016

This study introduces a structured latent growth curve model for analyzing twin data, revealing familial influences on growth components like asymptote, initial value, and rate of change. Shared environment significantly impacts these growth factors, with some additive genetic influence also noted.

Area of Science:

  • Developmental Psychology
  • Behavioral Genetics
  • Biostatistics

Background:

  • Longitudinal data analysis requires sophisticated models to capture developmental trajectories.
  • Traditional growth curves may not adequately account for genetic and environmental influences on developmental parameters.
  • Twin studies are crucial for disentangling genetic and environmental contributions to individual differences.

Purpose of the Study:

  • To present a structured latent growth curve modeling approach for analyzing longitudinal data from monozygotic (MZ) and dizygotic (DZ) twins.
  • To estimate the genetic and environmental variation and covariation in the components of growth curves (asymptote, initial value, rate of change).
  • To apply and illustrate the model using existing infant developmental scale data.

Main Methods:

Related Experiment Videos

  • Development of structured latent growth curve models incorporating Gompertz, logistic, and exponential growth functions.
  • Extension of these models to analyze longitudinal data from MZ and DZ twins.
  • Estimation of genetic and environmental parameters for growth curve components and occasion-specific residuals.

Main Results:

  • All three growth curve components (asymptote, initial value, rate of change) showed strong familial influences, primarily attributed to the shared environment.
  • Occasion-specific residual factors accounted for approximately 40% of the variance, with a significant portion being additive genetic.
  • The structured latent growth curve models provided restrictive, falsifiable predictions about twin covariance across measurement occasions.

Conclusions:

  • Structured latent growth curve models offer an economical yet powerful method for analyzing twin longitudinal data.
  • The shared environment plays a dominant role in shaping the asymptote, initial value, and rate of growth.
  • While the model fit was not superior to all alternatives, its predictive and explanatory power for genetic and environmental influences on growth trajectories is significant.