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

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.