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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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Clustering Vector Autoregressive Models: Capturing Qualitative Differences in Within-Person Dynamics.

Kirsten Bulteel1, Francis Tuerlinckx1, Annette Brose2

  • 1Faculty of Psychology and Educational Sciences, KU Leuven Leuven, Belgium.

Frontiers in Psychology
|October 25, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to identify subgroups with distinct within-person dynamics using vector autoregressive (VAR) modeling. The approach clusters individuals based on their VAR regression weights, revealing qualitative differences in psychological processes.

Keywords:
cluster analysisindividual differencespartitioningtime series analysisvector autoregressive modeling

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Area of Science:

  • Psychology
  • Quantitative Psychology
  • Network Science

Background:

  • Studying within-person multivariate dynamical processes is crucial in psychology.
  • Vector autoregressive (VAR) modeling is a common method to analyze temporal dynamics of interrelated variables.
  • Analyzing multiple individuals requires methods to capture similarities and differences in these dynamics.

Purpose of the Study:

  • To develop a method for identifying qualitative individual differences in within-person dynamics.
  • To cluster individuals based on their VAR regression weights.
  • To simultaneously fit a shared VAR model within identified subgroups.

Main Methods:

  • Clustering individuals based on vector autoregressive (VAR) regression weights.
  • Simultaneous fitting of a shared VAR model for each identified cluster.
  • Evaluation of the proposed algorithm through a simulation study.

Main Results:

  • The developed method successfully clusters individuals based on their VAR regression weights.
  • Simultaneous fitting of shared VAR models within clusters allows for analysis of subgroup dynamics.
  • The method is effective in identifying qualitative differences in within-person dynamics.

Conclusions:

  • This novel method enables the identification of subgroups with distinct within-person dynamics.
  • It facilitates the analysis of both shared and individual differences in multivariate temporal processes.
  • The approach is applicable to psychological research, such as analyzing depression symptom dynamics.