Mixture models such as latent class analysis offer a model-based alternative to k-modes clustering that accounts for classification errors and integrates subgroup membership into latent variable frameworks for physics education research.
Research commentary: A gap-gazing fetish in mathematics education? Problematizing research on the achievement gap
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Advancing clustering methods in physics education research: A case for mixture models
Mixture models such as latent class analysis offer a model-based alternative to k-modes clustering that accounts for classification errors and integrates subgroup membership into latent variable frameworks for physics education research.