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A Latent Markov Model for Noninvariant Measurements: An Application to Interaction Log Data From Computer-Interactive

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Summary

This study refines the latent Markov model (LMM) to account for item measurement effects in computer-interactive assessments. The enhanced LMM framework provides more robust and relevant inference for large-scale assessment data.

Keywords:
computerized assessmentsinteraction loglatent Markov model (LMM)longitudinal measurement invariancemeasurement noninvarianceprocess datatransition analysis

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Statistical Modeling

Background:

  • Latent Markov models (LMM) are increasingly used for analyzing log data from computer-interactive assessments.
  • Current LMM applications often assume uniform item effects, neglecting their distinct psychometric qualities and contribution to outcome variance.

Purpose of the Study:

  • To propose and evaluate a refined LMM that relaxes the measurement invariance constraint.
  • To accommodate event-specific measurement effects of items in assessment data analysis.

Main Methods:

  • Modification of the LMM to handle noninvariant measurements.
  • Refinement of the inferential scheme to incorporate event-specific measurement effects.
  • Numerical experimentation to validate inference methods and evaluate framework performance.

Main Results:

  • The proposed inferential scheme adequately retrieves model parameters and state profiles.
  • The refined LMM framework demonstrates reliable performance in modeling latent processes.
  • The new framework shows greater relevance and yields more robust inference, especially when the model is ill-specified, compared to traditional methods.

Conclusions:

  • The refined LMM framework effectively accounts for item measurement effects in assessment data.
  • This new approach offers potential for improved analysis of large-scale assessment data with distinct measurement effects.
  • The findings support the utility of the enhanced LMM for more accurate psychometric evaluations.