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Related Concept Videos

Longitudinal Research02:20

Longitudinal Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
Longitudinal Studies01:26

Longitudinal Studies

Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
Methods of Medium Optimization01:28

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
Multiple Comparison Tests01:13

Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...

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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

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Published on: September 17, 2019

Growth Mixture Modeling: A Method for Identifying Differences in Longitudinal Change Among Unobserved Groups.

Nilam Ram1, Kevin J Grimm

  • 1The Pennsylvania State University ; Max Planck Institute for Human Development.

International Journal of Behavioral Development
|July 26, 2013
PubMed
Summary

Growth mixture modeling (GMM) identifies hidden population subgroups and their unique developmental trajectories. This guide offers a practical primer for researchers applying GMM to longitudinal data analysis.

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Area of Science:

  • Statistics
  • Psychology
  • Biostatistics

Background:

  • Growth mixture modeling (GMM) is a statistical technique for uncovering latent subgroups within a population.
  • It allows for the examination of distinct developmental trajectories across these subgroups over time.
  • Understanding these subgroups is crucial for nuanced interpretation of longitudinal data.

Purpose of the Study:

  • To provide a practical primer on Growth Mixture Modeling (GMM) for researchers.
  • To explain the foundational concepts of GMM and its relationship to latent growth curve models.
  • To outline a clear, step-by-step approach for conducting GMM analyses.

Main Methods:

  • Review of standard latent basis growth curve models.
  • Introduction of GMM as an extension of multiple-group growth modeling.
  • Description of a four-step procedure for implementing GMM analysis.

Main Results:

  • The primer illustrates GMM procedures using example data from a cortisol stress-response study.
  • The methods described facilitate the identification of unobserved subpopulations.
  • The approach allows for detailed examination of longitudinal change within identified subgroups.

Conclusions:

  • GMM is a powerful tool for analyzing complex longitudinal data with unobserved heterogeneity.
  • This primer equips researchers with practical guidance for incorporating GMM into their studies.
  • The described methodology enhances the ability to understand diverse developmental patterns within populations.