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Modeling local dependence in longitudinal IRT models.

Maja Olsbjerg1, Karl Bang Christensen2

  • 1Department of Biostatistics, University of Copenhagen, Copenhagen, Denmark.

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Summary
This summary is machine-generated.

This study introduces a new method to accurately measure changes in latent variables over time by addressing response dependence. The item splitting technique in the longitudinal 2-parameter logistic (2PL) model corrects biased estimates for better change detection.

Keywords:
IRT modelLocal dependenceLongitudinal Rasch modelRasch modellongitudinal IRT model

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Area of Science:

  • Psychometrics
  • Longitudinal Data Analysis
  • Item Response Theory

Background:

  • Repeatedly using the same instrument to measure latent variables over time can create artificial correlations between responses from the same individual.
  • This within-person correlation, exceeding what the latent variable alone explains, can lead to inaccurate estimations of change over time.

Purpose of the Study:

  • To develop and propose a statistical method for modeling local dependence within the longitudinal 2-parameter logistic (2PL) model.
  • To provide a way to obtain unbiased estimates of latent variable change in longitudinal studies.

Main Methods:

  • The proposed method utilizes the concept of item splitting to account for local dependence.
  • This approach is integrated into the longitudinal 2PL model framework.

Main Results:

  • The item splitting method effectively models local dependence in longitudinal data.
  • This modeling allows for the correct estimation of changes in the latent variable, mitigating bias caused by response dependencies.

Conclusions:

  • The proposed method offers a robust solution for analyzing longitudinal data where local dependence is a concern.
  • Accurate estimation of latent variable change is crucial for understanding developmental processes and intervention effects.