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Related Concept Videos

Longitudinal Studies01:26

Longitudinal Studies

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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...
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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...
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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...
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Exponential models are essential for describing rapid, multiplicative changes in natural systems, such as population growth. When a population doubles at regular intervals, the process can be modeled using a suitable base. For instance, a bacterial culture that doubles every three hours follows the model n(t)=n0⋅2t/3, where n(t) is the population at the time t.A more general model uses the natural base e, especially for continuous growth. This takes the form n(t)=n0⋅ert, where r is...
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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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Understanding Variation in Longitudinal Data Using Latent Growth Mixture Modeling.

Constance A Mara1,2, Adam C Carle2,3,4

  • 1Behavioral Medicine and Clinical Psychology, Cincinnati Children's Hospital Medical Center.

Journal of Pediatric Psychology
|February 20, 2021
PubMed
Summary
This summary is machine-generated.

Latent growth mixture modeling helps identify distinct subgroups with unique developmental trajectories in pediatric psychology research. This method uncovers hidden patterns in longitudinal data, offering crucial clinical insights.

Keywords:
adherence/self-managementlongitudinal researchresearch design and methodologystatistical approach

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Area of Science:

  • Psychology
  • Statistics
  • Biostatistics

Background:

  • Longitudinal studies in pediatric psychology often analyze changes over time.
  • Traditional mixed or latent growth models may obscure subgroup-specific trajectories.
  • Identifying distinct developmental patterns is crucial for clinical implications.

Purpose of the Study:

  • To guide researchers in specifying, troubleshooting, evaluating, and interpreting latent growth mixture models (LGMM).
  • To demonstrate the application of LGMM for uncovering subgroups with shared trajectories.
  • To highlight the importance of LGMM in pediatric psychology research.

Main Methods:

  • Latent growth mixture modeling was applied to a dataset of 117 pediatric patients.
  • Mplus software was used for the analysis.
  • Methods for selecting an optimal solution (3-class solution) and incorporating covariates were presented.

Main Results:

  • A 3-class solution was identified as optimal for the example dataset.
  • The study illustrated how to select the best-fitting model.
  • Techniques for including covariates in LGMM were demonstrated.

Conclusions:

  • Latent growth mixture modeling is a valuable tool for identifying subgroups with distinct developmental trajectories.
  • This method reveals critical information missed by traditional models, aiding in clinical assessment and treatment.
  • LGMM enhances understanding of individual variability in longitudinal data.