Inferring latent structure in polytomous data with a higher-order diagnostic model
psychometrics
journal
Summary
This paper introduces an exploratory higher-order diagnostic model for polytomous (multi-category) response data, combining Bayesian variable selection with a higher-order factor structure to uncover dependencies among discrete latent attributes. The method is validated through simulation and demonstrated on 2012 PISA problem-solving data, extending diagnostic modeling to item types beyond simple right/wrong scoring.
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