Hyeon-Ah Kang | Searcharxiv

arXiv2024

Fully Latent Principal Stratification With Measurement Models

There is wide agreement on the importance of implementation data from randomized effectiveness studies in behavioral science; however, there are few methods available to incorporate these data into causal models, especially when they are multivariate or longitudinal, and interest is in low-dimensional summaries. We introduce a framework for studying how treatment effects vary between subjects who implement an intervention differently, combining principal stratification with latent variable measurement models; since principal strata are latent in both treatment arms, we call it "fully-latent principal stratification" or FLPS. We describe FLPS models including item-response-theory measurement, show that they are feasible in a simulation study, and illustrate them in an analysis of hint usage from a randomized study of computerized mathematics tutors.

stat.ME↗

arXiv2023

Markov Network for Modeling Local Item Dependence in Cognitively Diagnostic Classification Models

The study presents an exploratory graphical modeling approach for evaluating local item dependency within cognitively diagnostic classification models (DCMs). Current approaches to modeling local dependence require known item structure and have limited utility when such information is not available. In this study, we propose an exploratory approach to modeling local dependence so that items' own interactions can be revealed without dependency specification. The new framework is developed by integrating a Markov network into a generalized DCM. The framework unveils item interactions while performing regular cognitive diagnosis within a unified scheme. The inference on the model parameters is made on the regularized pseudo-likelihood and is implemented by an EM algorithm. Numerical experimentation from Monte Carlo simulation suggests that the proposed framework adequately recovers generating parameters and yields reliable standard error estimates. Compared with the regular DCM, the new model produced more accurate item parameter estimates as items exhibit local dependence. The study demonstrates application of the model using two real assessment data and discusses practical benefits of modeling local dependence.

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