Educational psychology in latin america: with linear hierarchical models

Research in clinical psychology, since its inception, has been aimed at analyzing, predicting and explaining the effect of treatments, by studying the change of patients in the course of them. To study the effects of therapy, research based on quantitative analysis models has historically used classical methods of parametric statistics, such as Pearson correlations, least squares regressions Student’s T-Tests and Variance Analysis (ANOVA). Hierarchical linear models (HLMs) represent a fundamental statistical strategy for research in psychotherapy, as they allow to overcome dependence on the observations usually presented in your data. The objective of this work is to present a guide to understanding, applying and reporting HLMs to study the effects of psychotherapy.

• Autores: silva d, jesus g; solano, darwin; Fernández, Claudia; Romero Marin, Ligia Cielo; ORELLANO LLINAS, NATALY; Negrete Sepúlveda, Ana María; León Coronado, Luz Estela; Barrios González, Rosio
• Tipo de recurso: http://purl.org/coar/resource_type/c_816b
• Fecha de publicación: 2019
• Institución: Corporación Universidad de la Costa
• Repositorio: REDICUC - Repositorio CUC
• Idioma: eng
• OAI Identifier: oai:repositorio.cuc.edu.co:11323/6078
• Acceso en línea: https://hdl.handle.net/11323/6078; https://repositorio.cuc.edu.co/
• Palabra clave: Hierarchical linear models; Growth curve models; Multilevel models; Psychotherapy
• Rights: openAccess
• License: CC0 1.0 Universal