title: Multilevel modeling of longitudinal data for psychotherapy researchers: I. The basics.
abstract: Psychotherapy researchers are often interested in change or development over time (i.e., pre- to posttreatment to follow-up or the development of process variables across multiple sessions). Traditional methods of assessing change and development are often unsatisfactory because of violations of statistical assumptions and because they do not model individual change. Modern longitudinal data analysis methods, including multilevel models (MLMs), provide an opportunity to model dynamic fluctuations in individual data across time. The objective of this article is to focus on the fundamentals of MLMs for longitudinal data analysis in psychotherapy research. To do so, the authors illustrate basic equations of MLMs and a strategy for developing increasingly complex models. They also present data from a psychotherapy research as an example of the application of MLMs. Finally, they offer some caveats and advice for conducting and presenting MLMs.
authors.affiliation: Department of Psychology, The Ottawa Hospital-General Campus, Ottawa, Ontario K1H 8L6, Canada. gtasca@ottawahospital.on.ca
authors.forename: Giorgio A
authors.initials: GA
authors.lastname: Tasca
authors.forename: Robert
authors.initials: R
authors.lastname: Gallop
mesh_terms: D006801:Humans; D001523:Mental Disorders / Q000628:therapy*; D008960:Models, Psychological; D011584:Psychology / Q000379:methods* / Q000706:statistics & numerical data*; D011594:Psychometrics / Q000379:methods* / Q000706:statistics & numerical data*; D011613:Psychotherapy / Q000379:methods* / Q000706:statistics & numerical data*