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Measurement error and person-specific reliability in multilevel autoregressive modeling.

Noémi K Schuurman1, Ellen L Hamaker2

  • 1Department of Methodology and Statistics.

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Researchers often neglect measurement error in psychological time series analysis. This study introduces a model to account for measurement error, improving the reliability of intraindividual dynamic models.

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Area of Science:

  • Psychology
  • Quantitative Psychology
  • Longitudinal Data Analysis

Background:

  • Intensive longitudinal data are increasingly used to study intraindividual psychological processes.
  • Autoregressive time series modeling, including multilevel extensions, is a popular analysis method.
  • Measurement error is often overlooked in psychological autoregressive time series modeling.

Purpose of the Study:

  • To discuss the impact of measurement error in autoregressive time series models.
  • To present a preliminary model accounting for measurement error in single-indicator constructs.
  • To explore the investigation of between-person and within-person reliabilities.

Main Methods:

  • Discussed reliability and measurement error concepts within dynamic (VAR(1)) models.
  • Presented a preliminary model to address measurement error for single indicators.
  • Illustrated consequences of assuming perfect reliability versus the proposed model.

Main Results:

  • Disregarding measurement error variance can lead to inaccurate dynamic models.
  • The proposed model accounts for measurement error, offering a more realistic assessment of psychological dynamics.
  • Empirical application demonstrated the impact of measurement error on affect dynamics.

Conclusions:

  • Measurement error is a critical factor to consider in psychological time series analysis.
  • The presented model enhances the reliability and accuracy of intraindividual dynamic models.
  • Future research should incorporate measurement error into dynamic models for robust psychological insights.