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Detecting intervention effects using a multilevel latent transition analysis with a mixture IRT model.

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Area of Science:

  • Educational Psychology
  • Quantitative Methods
  • Statistical Modeling

Background:

  • Assessing intervention effectiveness requires robust statistical methods capable of handling complex data structures.
  • Latent transition analysis (LTA) is a powerful tool for examining change over time, but traditional methods may not fully capture multilevel influences.
  • The mixture item response theory (MixIRT) model offers a way to account for individual differences in response patterns.

Purpose of the Study:

  • To introduce and evaluate a multilevel latent transition analysis (LTA) integrated with a mixture IRT measurement model (MixIRTM).
  • To investigate the effectiveness of an educational intervention by considering heterogeneity in student responses and change over time at both student and teacher levels.
  • To compare the results of a standard LTA-MixIRTM with a multilevel LTA-MixIRTM.

Main Methods:

  • A multilevel latent transition analysis (LTA) framework was employed.
  • A mixture item response theory measurement model (MixIRTM) was incorporated into the LTA.
  • The models were applied to data from an educational intervention study, comparing outcomes between intervention and control groups.

Main Results:

  • Both LTA-MixIRTM and multilevel LTA-MixIRTM identified patterns in problem-solving and transition behaviors.
  • Ignoring the multilevel structure in LTA-MixIRTM led to discrepancies in group membership assignment.
  • The multilevel LTA-MixIRTM revealed significant individual differences in transition patterns and confirmed the intervention's positive impact on the Fractions Computation test.
  • A notable proportion (27.4%) of students transitioned from lower to higher ability latent classes.
  • Student characteristics varied based on teacher-level latent classes.

Conclusions:

  • The multilevel LTA-MixIRTM provides a more accurate representation of intervention effects by accounting for nested data structures (students within teachers).
  • The developed model effectively identifies distinct student trajectories and the impact of interventions on these trajectories.
  • Findings highlight the importance of considering multilevel effects in educational research to fully understand intervention outcomes and student development.