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Dynamic Fit Index Cutoffs for Time Series Network Models.

Siwei Liu1, Christopher M Crawford2, Zachary F Fisher2

  • 1Human Ecology, University of California, Davis, Davis, CA, USA.

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Summary
This summary is machine-generated.

This study adapts the dynamic fit index (DFI) for time series analysis, offering tailored cutoffs for network models. New methods, DFI_A and DFI_B, improve detection of model misspecification, especially with small sample sizes.

Keywords:
Time seriesdynamic fit indexintensive longitudinal datamodel fitnetwork

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Area of Science:

  • Statistics
  • Psychometrics
  • Network Analysis

Background:

  • The dynamic fit index (DFI) is a simulation-based method for determining model fit index cutoffs.
  • Existing methods may not adequately detect model misspecification in time series network models.

Purpose of the Study:

  • Extend the dynamic fit index (DFI) to time series analysis.
  • Develop improved methods for deriving fit index cutoffs to detect omitted paths in time series network models.
  • Address limitations of the original DFI with small effect or sample sizes.

Main Methods:

  • Simulation studies to evaluate DFI cutoffs for time series network models.
  • Comparison of DFI cutoffs with established benchmarks (Hu & Bentler).
  • Development and evaluation of two alternative DFI approaches (DFI_A and DFI_B) using lenient criteria.

Main Results:

  • DFI cutoffs for detecting omitted paths in time series networks are closer to exact fit than traditional benchmarks.
  • Cutoff values are influenced by the number of variables, network density, time points, and misspecification type.
  • Original DFI fails to identify cutoffs for omitted paths under small effect or sample sizes with strict error rate limits.
  • DFI_A and DFI_B provide viable alternatives for deriving cutoffs under more lenient criteria.

Conclusions:

  • The extended DFI provides more accurate fit index cutoffs for time series network models.
  • DFI_A and DFI_B offer practical solutions for detecting model misspecification, particularly in challenging data conditions.
  • These methods enhance the reliability of model evaluation in time series network analysis.