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arXiv · 2309.04047

Fully Latent Principal Stratification With Measurement Models

Sooyong Lee·Adam C Sales·Hyeon-Ah Kang·Tiffany A. Whittaker
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Abstract

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.

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Sooyong Lee, Adam C Sales, Hyeon-Ah Kang, Tiffany A. Whittaker. 2023-09-07. Fully Latent Principal Stratification With Measurement Models. https://arxiv.org/abs/2309.04047

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