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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2309.04047 |
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| _version_ | 1866910448717135872 |
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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 |