Causal Rasch models

Causal Rasch models

Rasch's unidimensional models for measurement show how to connect object measures (e.g., reader abilities), measurement mechanisms (e.g., machine-generated cloze reading items), and observational outcomes (e.g., counts correct on reading instruments). Substantive theory shows what interventions or manipulations to the measurement mechanism can be traded off against a change to the object measur...

Flexible Rasch Mixture Models with Package psychomix

This introduction to the R package psychomix is a (slightly) modified version of Frick, Strobl, Leisch, and Zeileis (2012), published in the Journal of Statistical Software. Measurement invariance is an important assumption in the Rasch model and mixture models constitute a flexible way of checking for a violation of this assumption by detecting unobserved heterogeneity in item response data. H...

Multidimensional Rasch Models for Partial - Credit Scoring

Collateral information and Mixed Rasch models

A simulation study is conducted to evaluate the usefulness of incorporating collateral information in the Mixed Rasch model. The results show that the standard errors as well as latent class membership assignment can benefit substantially from incorporating external variables that associate with the latent class variable. Especially when the difference in probability structure between the laten...

Pairwise Parameter Estimation in Rasch Models

on comparing responses to pairs of items irrespective of other items. The pseudo-likelihood method is comparable to Fischer’s (1974) Minchi method. A simulation study found that the pseudo-likelihood estimates and their (estimated) standard errors were comparable to conditional and marginal maximum likelihood estimates. The method is extended to estimate parameters of the linear logistic test m...