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On generating plausible values for multilevel modelling with large-scale-assessment data.

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Summary
This summary is machine-generated.

This study introduces new single-level methods for latent regression in large-scale assessments (LSAs) to better model complex data structures. One proposed method efficiently supports random-slope estimation, offering an alternative to computationally intensive multilevel models.

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Area of Science:

  • Educational Measurement
  • Statistical Modeling

Background:

  • Large-scale assessments (LSAs) use latent regressions and plausible values (PVs) to link background variables to performance.
  • Multilevel modeling is often used for clustered LSA data, but single-level imputation models can mismatch analytic models.
  • Existing single-level methods struggle with random-slope models, a limitation this study addresses.

Purpose of the Study:

  • To propose and evaluate new single-level latent regression methods for supporting random-slope estimation in multilevel models.
  • To compare the performance of existing and proposed single-level methods against a multilevel latent regression approach.
  • To identify efficient and accurate methods for analyzing complex LSA data structures.

Main Methods:

  • Development of two novel single-level latent regression techniques.
  • Comparison of single-level methods with a multilevel latent regression approach.
  • Evaluation of methods based on their ability to support random-intercept and random-slope multilevel models.

Main Results:

  • Existing single-level methods adequately support random-intercept models but not random-slope models.
  • Multilevel latent regression provided acceptable estimates but faced computational challenges and variable performance.
  • One proposed single-level method demonstrated efficiency and accuracy comparable to multilevel latent regression for all parameters.

Conclusions:

  • The proposed single-level methods offer viable alternatives for analyzing LSA data with multilevel structures.
  • The new single-level method is an efficient and effective option for random-slope estimation.
  • Recommendations are provided for method selection based on specific analytical needs and computational resources.