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Related Concept Videos

Derivatives: Problem Solving01:26

Derivatives: Problem Solving

Temperature-Dependent Growth of Brook TroutThe growth of brook trout is closely influenced by water temperature. Experimental data demonstrate how trout weight changes over a 24-day period in response to varying water temperatures. At lower temperatures, such as 15.5 degrees Celsius, brook trout show significant weight gain. However, as the temperature increases, the amount of weight gained steadily decreases. At the highest temperature measured, 24.4 degrees Celsius, trout experience a net...
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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 squares (OLS)...
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Variation

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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

Using derivative estimates to describe intraindividual variability at multiple time scales.

Pascal R Deboeck1, Mignon A Montpetit, C S Bergeman

  • 1Department of Psychology, University of Notre Dame, USA. pascal@ku.edu

Psychological Methods
|December 9, 2009
PubMed
Summary

Understanding intraindividual variability requires examining specific time scales, not just overall variance. This study introduces a method using estimated derivatives for a more nuanced analysis of psychological variability.

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Area of Science:

  • Psychology
  • Quantitative Psychology

Background:

  • Intraindividual variability is crucial in psychological research.
  • Existing measures like standard deviation may not fully capture this variability.

Purpose of the Study:

  • To propose a more productive method for studying intraindividual variability.
  • To highlight the limitations of traditional variance measures.
  • To introduce time-scale specific analysis.

Main Methods:

  • Utilizing estimated derivatives to analyze intraindividual variability.
  • Examining variance and distributional properties at multiple time scales.
  • Comparing time-scale specific analysis with traditional methods.

Main Results:

  • Demonstrated that analyzing variability at specific time scales provides richer insights.
  • Showcased the limitations of solely examining overall variance in time series data.
  • Illustrated the proposed method with simulated and real-world (negative affect and neuroticism) data.

Conclusions:

  • Intraindividual variability analysis benefits from time-scale specific approaches.
  • Variability and variance are distinct concepts, especially across different time scales.
  • Estimated derivatives offer a powerful tool for nuanced intraindividual variability research.