Using Hierarchical Linear Models to study psychotherapy efficacy

Article Sidebar

PDF (Español (España))
Published: Apr 24, 2019
DOI: https://doi.org/10.32348/1852.4206.v11.n1.20412
Keywords:
hierarchical linear models, growth curve models, multilevel models, psychotherapy

Main Article Content

Juan Martín Gómez Penedo
Consejo Nacional de Investigaciones Científicas y Técnicas
Roberto Muiños
Universidad Nacional de Tres de Febrero
Pablo Hirsch
Centro Privado de Psicoterapias
Andrés Roussos
Universidad de San Andrés

Abstract

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.

Downloads

Download data is not yet available.

Article Details

How to Cite
Gómez Penedo, J. M., Muiños, R., Hirsch, P., & Roussos, A. (2019). Using Hierarchical Linear Models to study psychotherapy efficacy. Argentinean Journal of Behavioral Sciences, 11(1), 25–37. https://doi.org/10.32348/1852.4206.v11.n1.20412
Issue
Vol. 11 No. 1 (2019): REVISTA ARGENTINA DE CIENCIAS DEL COMPORTAMIENTO
Section
Technical or Methodological Articles

La RACC aplicará la licencia internacional de atribuciones comunes creativas (Reconocimiento 4.0 Internacional: https://creativecommons.org/licenses/by/4.0/).

Bajo esta licencia, se permite cualquier explotación de la obra, incluyendo la explotación con fines comerciales y la creación de obras derivadas, la distribución de las cuales también está permitida sin ninguna restricción. Esta licencia es una licencia libre según la Freedom Defined. La única condición es que siempre y en todos los casos se cite a los autores y a la fuente original de publicación (i.e., RACC). Esta licencia fue desarrollada para facilitar el acceso abierto, gratuito y libre a trabajos originales científicos y artísticos.

Author Biographies

Juan Martín Gómez Penedo, Consejo Nacional de Investigaciones Científicas y Técnicas

Universidad de Buenos Aires

Roberto Muiños, Universidad Nacional de Tres de Febrero

Universidad de Buenos Aires

Andrés Roussos, Universidad de San Andrés

Universidad de Buenos Aires.

Consejo Nacional de Investigaciones Científicas y Técnicas.

References

Areas, M., Roussos, A., Hirsch, H., Hirsch, P., Becerra, P., & Gómez Penedo, J. M. (2018). Evaluación de un dispositivo de investigación orientada a la práctica en el desarrollo de un sistema de feedback en psicoterapia. Revista Argentina de Clínica Psicológica, 27(2), 229-249.

Atkins, D. C. (2005). Using multilevel models to analyze couple and family treatment data: Basic and advanced issues. Journal of Family Psychology, 19(1), 98–110. doi: 10.1037/0893-3200.19.1.98

Baldwin, S. A., Imel, Z. E., Braithwaite, S. R., & Atkins, D. C. (2014). Analyzing multiple outcomes in clinical research using multivariate multilevel models. Journal of Consulting and Clinical Psychology, 82(5), 920–930. doi: 10.1037/a0035628

Bates, D., Mäechler, M., Bolker, B., & Walker, S. (2015). Fitting Linear Mixed-Effects Models using lme4. Journal of Statistical Software, 67(1), 1-48. doi: 10.18637/jss.v067.i01.

Behn, A. J., Errázuriz, P. A., Cottin, M., & Fischer, C. (2017). Change in symptomatic burden and life satisfaction during short‐term psychotherapy: Focusing on the role of family income. Counselling & Psychotherapy Research, 18(2), 133-142. doi: 10.1002/capr.12158

Bliese, P. (2016). Multilevel modeling in R (2.5). Recuperado de https://cran.rproject.org/doc/contrib/Bliese_Multilevel.pdf

Constantino, M. J., Westra, H. A., Antony, M. M., & Coyne, A. E. (2017). Specific and common processes as mediators of the long-term effects of cognitive-behavioral therapy integrated with motivational interviewing for generalized anxiety disorder. Psychotherapy Research, 5, 1-13. doi: 10.1080/10503307.2017.1332794

Curran, P. J., & Bauer, D. J. (2011). The disaggregation of within-person and betweenperson effects in longitudinal models of change. Annual Review of Psychology, 62(1), 583–619. doi: 10.1146/annurev.psych.093008.100356.

de Shazer, S., & Berg, I. K. (1997). “What works?” Remarks on research aspects of SolutionFocused Brief Therapy. Journal of Family Therapy, 19(2), 121–124. doi: 10.1111/1467-6427.00043

de Shazer, S., Berg, I. K., Lipchik, E., Nunnally, E., Molnar, A., Gingerich, W., & Weiner-Davis, M. (1986). Brief therapy: focused solution development. Family Process, 25(2), 207–221. doi: 10.1111/j.1545-5300.1986.00207.x

Etchebarne, I., O’Connell, M., & Roussos, A. (2008). Estudio de mediadores y moderadores en la investigación en Psicoterapia. Anuario de Investigaciones, 13(1), 33-56.

Finch, W. H., & Bolin, J. E. (2017). Multilevel modeling using Mplus. Boca Raton: CRC Press.

Gómez Penedo, J. M., Constantino, M. J., Coyne, A., Bernecker, S. L., & Lotte Smith-Hansen, L. (2018). Patient Baseline Interpersonal

Problems as Moderators of Outcome in Two Psychotherapies for Bulimia Nervosa. Psychotherapy Research. Publicación anticipada en línea. doi: 10.1080/1050337.2018.1425931

Gómez Penedo, J. M., Constantino, M. J., Coyne, A., Westra, H., & Antony, M. (2017). Markers for Context-Responsiveness: Client Baseline Interpersonal Problems Moderate the Efficacy of Two Psychotherapies for Generalized Anxiety Disorder. Journal of Consulting and Clinical Psychology, 85(10), 1000-1011. doi: 10.1037/ccp0000233

Gómez Penedo, J. M., Juan, S., Manubens, R. T., & Roussos, A. (2018). El estudio del cambio en psicoterapia: desafíos conceptuales y problemas de investigación empírica. Anuario de Investigaciones en Psicología, 24, 15-24.

Hayes, A. F. (2017). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. New York: Guilford Publications.

Hofmann, D. A. (1997). An overview of the logic and rationale of Hierarchical Linear Models. Journal of Management, 23(6), 723–744. doi: 10.1177/014920639702300602

Hox, J., & Maas, C. (2005). Multilevel analysis. Encyclopedia of Social Measurement, 2, 785– 793. doi: 10.1016/B0-12-369398-/00560-0

Mellado A., Suárez, N., Altimir, C., Martínez, C., Pérez J. C., Krause, M., & Horvath, A. (2017) Disentangling the change-alliance relationship: Observational assessment of the therapeutic alliance during change and stuck episodes. Psychotherapy Research, 27(5), 595-607. doi: 10.1080/10503307.2016.1147657

Ogles, B. M. (2013). Measuring change in psychotherapy research. En M. J. Lambert (Ed.), Bergin and Garfields’s Handbook of Psychotherapy and Behavior Change (pp.134–166). New Jersey: Wiley.

Power, M. J., & Freeman, C. (2012). A Randomized Controlled Trial of IPT versus CBT in primary care: with some cautionary notes about handling missing values in clinical trials. Clinical Psychology & Psychotherapy, 19(2), 159–169. doi: 10.1002/cpp.1781

Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd Ed.). Thousand Oaks, California: Sage.

Raudenbush, S. W., Bryk, A. S., Cheong, Y. F., Congdon, R. T., & du Toit, M. (2011). HLM7: Hierarchical Linear and Nonlinear Modeling. Chicago, IL: Scientific Software International.

Skrondal, A., & Rabe-Hesketh, S. (2004). Generalized latent variable modeling. Boca Raton: Chapman & Hall/CRC.

R Core Team (2018). R: A language and environment for statistical computing. [programa informático]. Viena, Austria: R Foundation for Statistical Computing. Recuperado de: https://www.R-project.org/.

Wang, J., Xie, H., & Fisher, J. H. (2012). Multilevel models: Applications using SAS. Berlin: De Gruyter.

Westra, H. A., Constantino, M. J., & Antony, M. M. (2016). Integrating motivational interviewing with cognitive-behavioral therapy for severe generalized anxiety disorder: an allegiancecontrolled randomized clinical trial. Journal of Consulting and Clinical Psychology, 84(9), 768– 782. doi: 10.1037/ccp0000098

Zilcha-Mano, S., & Errázuriz, P. (2015). One size does not fit all: Examining heterogeneity and identifying moderators of the alliance–outcome association. Journal of Counseling Psychology, 62(4), 579–591. doi: 10.1037/cou0000103