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Fully Latent Principal Stratification With Measurement Models.

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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]
Copyright of Journal of Educational & Behavioral Statistics is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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DbLabel: Education Research Complete
An: 191631082
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Fully Latent Principal Stratification With Measurement Models.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Educational+%26+Behavioral+Statistics%22">Journal of Educational & Behavioral Statistics</searchLink>. Apr2026, Vol. 51 Issue 2, p344-369. 26p.
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  Data: *<searchLink fieldCode="DE" term="%22Item+response+theory%22">Item response theory</searchLink><br />*<searchLink fieldCode="DE" term="%22Longitudinal+method%22">Longitudinal method</searchLink><br /><searchLink fieldCode="DE" term="%22Latent+variables%22">Latent variables</searchLink><br /><searchLink fieldCode="DE" term="%22Measurement-model+comparison%22">Measurement-model comparison</searchLink><br /><searchLink fieldCode="DE" term="%22Behavioral+sciences%22">Behavioral sciences</searchLink><br /><searchLink fieldCode="DE" term="%22Causal+inference%22">Causal inference</searchLink><br /><searchLink fieldCode="DE" term="%22Randomized+controlled+trials%22">Randomized controlled trials</searchLink>
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  Data: 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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  Data: <i>Copyright of Journal of Educational & Behavioral Statistics is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=191631082
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        Value: 10.3102/10769986251321428
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      – Code: eng
        Text: English
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        PageCount: 26
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        Type: general
      – SubjectFull: Longitudinal method
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      – SubjectFull: Latent variables
        Type: general
      – SubjectFull: Measurement-model comparison
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      – SubjectFull: Behavioral sciences
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      – SubjectFull: Causal inference
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      – SubjectFull: Randomized controlled trials
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      – TitleFull: Fully Latent Principal Stratification With Measurement Models.
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            NameFull: Lee, Sooyong
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            NameFull: Sales, Adam C.
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            NameFull: Kang, Hyeon-Ah
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              Text: Apr2026
              Type: published
              Y: 2026
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