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Related Experiment Videos

Explained Variance and Intraclass Correlation in a Two-Level AR(1) Model.

J Jongerling1, H Hoijtink2

  • 1a Department of Psychology, Education, and Child Studies, Faculty of Social Sciences , Eramus University.

Multivariate Behavioral Research
|April 5, 2017
PubMed
Summary
This summary is machine-generated.

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This study introduces an approximate equation for the total variance in two-level autoregressive AR(1) time series models. This helps calculate explained variance and intraclass correlation (ICC) for multilevel data analysis.

Area of Science:

  • Statistics
  • Time Series Analysis
  • Multilevel Modeling

Background:

  • The total variance of first-order autoregressive (AR(1)) time series is established.
  • Two-level AR(1) models are increasingly used, but lack a total variance equation.
  • This gap hinders the analysis of multilevel time series data.

Purpose of the Study:

  • To derive an approximate equation for the total variance of two-level AR(1) models.
  • To enable computation of explained and unexplained variance at each model level.
  • To facilitate the calculation of the intraclass correlation (ICC) for multilevel data.

Main Methods:

  • Development of an approximate equation for total variance in two-level AR(1) models.
  • Application of the derived equation to compute level-specific variances.
Keywords:
Bayesian Statisticsdynamic modelingmultilevel modelingother topicstime series analysis

Related Experiment Videos

  • Illustration using structured diary data on positive affect in married women.

    • Main Results:

    • An approximation for the total variance of two-level AR(1) models is presented.
    • The method allows for the calculation of explained and unexplained variance components.
    • Intraclass correlation (ICC) can be computed, aiding in understanding data structure.

    Conclusions:

    • The proposed variance approximation is valuable for analyzing two-level AR(1) time series.
    • This facilitates a deeper understanding of variance partitioning in multilevel models.
    • The approach is applicable to real-world data, such as psychological diary studies.