Fully Latent Principal Stratification With Measurement Models

Fuente: arXiv

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Detalles Bibliográficos
Autores principales:	Lee, Sooyong, Sales, Adam C, Kang, Hyeon-Ah, Whittaker, Tiffany A.
Formato:	Preprint
Publicado:	2023
Materias:	Methodology
Acceso en línea:	Acceder al recurso
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author	Lee, Sooyong Sales, Adam C Kang, Hyeon-Ah Whittaker, Tiffany A.
author_facet	Lee, Sooyong Sales, Adam C Kang, Hyeon-Ah Whittaker, Tiffany A.
contents	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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2309_04047
institution	arXiv
publishDate	2023
record_format	arxiv
spellingShingle	Fully Latent Principal Stratification With Measurement Models Lee, Sooyong Sales, Adam C Kang, Hyeon-Ah Whittaker, Tiffany A. Methodology 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.
title	Fully Latent Principal Stratification With Measurement Models
topic	Methodology
url	https://arxiv.org/abs/2309.04047

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