Regression Discontinuity Analysis with Latent Variables
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| Title: | Regression Discontinuity Analysis with Latent Variables |
|---|---|
| Language: | English |
| Authors: | Monica Morell, Muwon Kwon, Youngjin Han, Youjin Sung, Yang Liu, Ji Seung Yang |
| Source: | Grantee Submission. 2026 61(1):178-191. |
| Peer Reviewed: | Y |
| Page Count: | 15 |
| Publication Date: | 2026 |
| Sponsoring Agency: | Institute of Education Sciences (ED) |
| Contract Number: | R305D220030 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Regression (Statistics), Models, Item Response Theory, Research Design, Research Problems, Scores, Statistical Inference, Sample Size |
| DOI: | 10.1080/00273171.2025.2565591 |
| ISSN: | 0027-3171 1532-7906 |
| Abstract: | A regression discontinuity (RD) design is often employed to provide causal evidence when the randomization of the treatment assignment is infeasible. When variables of interest are latent constructs measured by observed indicators, the conventional RD analysis using observed variable scores does not allow researchers to examine heterogeneity in the estimated local average treatment effect (ATE) and to generalize the ATE to participants away from the cutoff. We propose a novel methodological augmentation to the conventional RD analysis, which assumes the availability of multiple indicator variables (i.e., raw item responses) that measure the latent construct underlying the running variable. By specifying an explicit measurement model based on those indicator variables, our latent RD framework allows 1) defining the local ATE conditional on the latent construct, 2) disentangling the heterogeneity of the local ATE, and 3) generalizing the local ATE to running variable scores away from the cutoff. In a proof-of-concept simulation we illustrate the proposed augmentation recovers parameters of interest well under practical test length and sample size conditions. |
| Abstractor: | As Provided |
| IES Funded: | Yes |
| Entry Date: | 2026 |
| Accession Number: | ED680414 |
| Database: | ERIC |
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| Abstract: | A regression discontinuity (RD) design is often employed to provide causal evidence when the randomization of the treatment assignment is infeasible. When variables of interest are latent constructs measured by observed indicators, the conventional RD analysis using observed variable scores does not allow researchers to examine heterogeneity in the estimated local average treatment effect (ATE) and to generalize the ATE to participants away from the cutoff. We propose a novel methodological augmentation to the conventional RD analysis, which assumes the availability of multiple indicator variables (i.e., raw item responses) that measure the latent construct underlying the running variable. By specifying an explicit measurement model based on those indicator variables, our latent RD framework allows 1) defining the local ATE conditional on the latent construct, 2) disentangling the heterogeneity of the local ATE, and 3) generalizing the local ATE to running variable scores away from the cutoff. In a proof-of-concept simulation we illustrate the proposed augmentation recovers parameters of interest well under practical test length and sample size conditions. |
|---|---|
| ISSN: | 0027-3171 1532-7906 |
| DOI: | 10.1080/00273171.2025.2565591 |
