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Fully Latent Principal Stratification With Measurement Models.

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Bibliographic Details
Title: Fully Latent Principal Stratification With Measurement Models.
Authors: Lee, Sooyong1 (AUTHOR) sooyongl09@utexas.edu, Sales, Adam C.2 (AUTHOR) asales@wpi.edu, Kang, Hyeon-Ah (AUTHOR) hkang@austin.utexas.edu, Whittaker, Tiffany1 (AUTHOR) t.whittaker@austin.utexas.edu
Source: Journal of Educational & Behavioral Statistics. Apr2026, Vol. 51 Issue 2, p344-369. 26p.
Subject Terms: *Item response theory, *Longitudinal method, Latent variables, Measurement-model comparison, Behavioral sciences, Causal inference, Randomized controlled trials
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" (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. [ABSTRACT FROM AUTHOR]
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Database: Education Research Complete
Description
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" (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. [ABSTRACT FROM AUTHOR]
ISSN:10769986
DOI:10.3102/10769986251321428