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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 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 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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Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
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Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Right-sizing statistical models for longitudinal data.

Phillip K Wood1, Douglas Steinley1, Kristina M Jackson2

  • 1Department of Psychological Sciences, University of Missouri.

Psychological Methods
|August 4, 2015
PubMed
Summary
This summary is machine-generated.

Researchers should compare statistical models for longitudinal data to find the best fit. This approach helps select appropriate complexity, improving the analysis of growth and variation patterns.

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Area of Science:

  • Psychometrics
  • Statistics
  • Developmental Psychology

Background:

  • Longitudinal data analysis requires careful statistical model selection.
  • Overly simple or complex models can lead to inaccurate conclusions.
  • Existing models may not optimally fit diverse data patterns.

Purpose of the Study:

  • To propose a framework for comparing statistical models for longitudinal data.
  • To guide researchers in selecting appropriately complex models.
  • To enhance the analysis of mean and variation-covariation patterns in growth.

Main Methods:

  • A general framework for evaluating nested psychometric and growth curve models.
  • A 3-step approach focusing on variance components before growth model selection.
  • Utilizing the orthogonal free curve slope intercept (FCSI) growth model as a general case.

Main Results:

  • Demonstrated the utility of model comparison in a child vocabulary longitudinal study.
  • Illustrated the framework with a Monte Carlo study comparing parametric growth models.
  • Showcased how the FCSI model encompasses various other models like HLMs and MANOVA.

Conclusions:

  • Model comparison is crucial for "right-sizing" statistical models to longitudinal data.
  • The proposed framework aids in identifying better-fitting and more parsimonious models.
  • This approach improves the understanding of developmental trajectories and their variations.