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Evaluating Longitudinal Anchoring Methods for Rasch Models
Tara L Valladares1, Karen M Schmidt
1Tara Valladares, 120 Westwood Circle, Charlottesville, VA 22903, USA, tls8vx@virginia.cdu.
Journal of Applied Measurement
|May 13, 2021
Summary
New longitudinal anchoring methods improve Rasch model analysis for many timepoints. The Mean and Stacked anchoring methods effectively estimate person and item parameters and track changes over time in large datasets.
Area of Science:
- Psychometrics
- Statistical modeling
- Educational measurement
Background:
- Modern Rasch models struggle with numerous timepoints in longitudinal studies.
- Accurate person and item estimation is crucial for evaluating test function over time.
- New methods are needed for analyzing extensive longitudinal data.
Purpose of the Study:
- To evaluate the effectiveness of six longitudinal anchoring methods for Rasch models.
- To identify optimal methods for estimating latent trait levels across multiple occasions.
- To address limitations of current models in handling many timepoints.
Main Methods:
- A simulation study was conducted to compare six longitudinal anchoring methods: Floated, Racked, Time One, Mean, Random, and Stacked.
- Performance was evaluated based on the recovery of population change, person/item estimates, and model fit.
- Longitudinal anchoring involves using a common item parameter scale across different time points.
Main Results:
- The Mean and Stacked anchoring methods demonstrated superior performance in recovering population change, person/item estimates, and model fit.
- The Racked method failed to produce reliable change estimates and is not recommended.
- Longitudinal anchoring allows for the analysis of numerous timepoints without limiting the number of observations.
Conclusions:
- Longitudinal anchoring offers a viable solution for analyzing large longitudinal datasets in psychometrics.
- The Mean and Stacked methods are recommended for their accuracy and efficiency.
- This approach provides a low-computation method for generating latent trait estimates in longitudinal research.
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