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Detecting Intervention Effects in a Cluster-Randomized Design Using Multilevel Structural Equation Modeling for Binary Responses
Multilevel modeling (MLM) is frequently used to detect group differences, such as an intervention effect in a pre-test–post-test cluster-randomized design. Group differences on the post-test scores are detected by controlling for pre-test scores as a proxy variable for unobserved factors that predic...
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| I publikationen: | Appl Psychol Meas |
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| Huvudupphovsmän: | , , |
| Materialtyp: | Artigo |
| Språk: | Inglês |
| Publicerad: |
SAGE Publications
2015
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| Ämnen: | |
| Länkar: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5978494/ https://ncbi.nlm.nih.gov/pubmed/29881032 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1177/0146621615591094 |
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