Advanced Rasch, mixture-IRT, and imputation sensitivity

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This article collects optional diagnostic/sensitivity adapters that complement, rather than replace, the stable process-IRT core.

True response-mixture IRT

mix <- fit_mixture_irt_process_classes(
  binary_response_matrix,
  n_classes = 2,
  itemtype = "2PL"
)
plot(mix)

The mixture components are latent response-distribution classes. They must not be named as cognitive strategies without external response-process evidence.

If defensible class assignments have been extracted, compare them with independent process summaries:

alignment <- map_latent_classes_to_process_profiles(
  class_membership,
  person_process_data,
  person = "person_id",
  class_col = "class",
  process_features = c("dwell_ms", "pupil_peak", "aoi_entropy", "revisits")
)
plot(alignment)

Nonparametric Rasch diagnostics

np <- audit_nonparametric_rasch(
  binary_response_matrix,
  methods = c("T1", "T10"),
  n = 100
)
np$status
plot(np, method = "T1")

Stepwise item-reduction sensitivity

red <- audit_item_reduction_sensitivity(
  erm_rasch,
  criterion = list("itemfit"),
  alpha = 0.05,
  maxstep = 5
)
red$eliminated_items
plot(red)

Automated elimination is never sufficient evidence for deleting an item; content validity, theoretical coverage, DIF, local dependence, and process evidence remain required.

Biometric-feature imputation sensitivity

imp <- biometric_imputation_sensitivity(
  trial_process_data,
  variables = c("rt_ms", "dwell_ms", "pupil_peak", "pupil_auc", "valid_gaze_prop"),
  methods = c("mice", "missForest")
)
imp$missingness
imp$status
plot(imp)

Completed/imputed data are sensitivity datasets by default and do not silently replace the primary missingness strategy.

Process-informed Rasch trees

tree <- fit_process_rasch_tree(
  binary_response_matrix,
  covariates = person_process_covariates
)
plot(tree)

Tree splits diagnose conditional item-parameter heterogeneity. They are not automatically psychological strategy classes.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.