Using Hierarchical Linear Models to study psychotherapy efficacy
Hierarchical Linear Models (HLM) represents a valuable statistical tool for psychotherapy research, given that they allow dealing with the usual dependency presented in its data. These methods are useful to estimate change, disaggregate sources of variations, and analyze the effect of different leve...
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| Autores principales: | , , , |
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| Formato: | Artículo revista |
| Lenguaje: | Español |
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Universidad Nacional de Córdoba
2019
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| Materias: | |
| Acceso en línea: | https://revistas.unc.edu.ar/index.php/racc/article/view/20412 |
| Aporte de: |
| Sumario: | Hierarchical Linear Models (HLM) represents a valuable statistical tool for psychotherapy research, given that they allow dealing with the usual dependency presented in its data. These methods are useful to estimate change, disaggregate sources of variations, and analyze the effect of different level predictors. Considering that, these analyses required a highly sophisticated technical knowledge that might remain inaccessible for many researchers, the aim of this paper is to present a guide on how to understand, apply, and report HLM for psychotherapy effects research. To illustrate how to apply HLM, we have drawn on a naturalistic clinical dataset. Disseminating these methods in the Latin-America might represent a meaningful contribution both for research and practice, improving the soundness of clinical studies and helping to develop a more robust knowledge that might leads to greater process and outcome in psychotherapy. |
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