A new ℓ1-regularized mixture asymmetric IRT framework jointly recovers latent classes for impact and selects DIF items without group labels or anchors, as shown in simulations and two educational datasets.
Psychometrika , volume=
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A refinement procedure for LCA that collapses redundant response probability levels per item to produce sparse, interpretable models with consistent recovery of the sparse pattern.
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Latent Impact and Differential Item Functioning Analysis for Asymmetric IRT Models
A new ℓ1-regularized mixture asymmetric IRT framework jointly recovers latent classes for impact and selects DIF items without group labels or anchors, as shown in simulations and two educational datasets.
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Sparse Latent Class Analysis: Post-Estimation Refinement via Item-level Pseudo-Likelihood
A refinement procedure for LCA that collapses redundant response probability levels per item to produce sparse, interpretable models with consistent recovery of the sparse pattern.