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Related Concept Videos

Longitudinal Research02:20

Longitudinal Research

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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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Longitudinal Studies01:26

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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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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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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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Multicompartment Models: Overview01:14

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Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
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Quadratic Models01:23

Quadratic Models

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Quadratic models are mathematical representations used to describe relationships in which the rate of change changes at a constant rate. These models appear in a wide variety of natural and engineered systems, especially those involving motion, forces, and optimization. One common application is analyzing the vertical motion of objects influenced by gravity, such as a ball thrown into the air.In such scenarios, the object's height changes over time in a curved pattern, rising to a maximum point...
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Modeling with Differential Equations01:25

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Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

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Longitudinal models for studying multivariate changes and dynamics.

Emilio Ferrer1, Joseph E Gonzales

  • 1Department of Psychology, University of California, Davis, Calif., USA.

Annals of Nutrition & Metabolism
|November 22, 2014
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Summary
This summary is machine-generated.

This study introduces a new longitudinal modeling approach using latent change scores within structural equation modeling to analyze complex multivariate changes over time. The method effectively captures dynamics among multiple processes using empirical data.

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Area of Science:

  • Statistics
  • Quantitative Psychology
  • Longitudinal Data Analysis

Background:

  • Analyzing multivariate changes over time is complex.
  • Existing methods may not fully capture dynamic interrelationships.

Purpose of the Study:

  • To present a novel longitudinal modeling approach.
  • To examine multivariate changes and dynamics using latent change scores.
  • To provide a framework for understanding multiple processes over time.

Main Methods:

  • Utilizes latent change scores.
  • Employs structural equation modeling (SEM).
  • Applies the model to empirical longitudinal data.

Main Results:

  • The proposed model effectively identifies dynamics among multiple processes.
  • Demonstrates the application of the technique with real-world longitudinal data.

Conclusions:

  • The latent change score approach within SEM is a powerful tool for longitudinal research.
  • Further advancements in modeling possibilities are suggested for future research.