Objective: To better understand the complexity of dyadic processes, such as the mechanisms of the working alliance, researchers recommend taking advantage of innovations in data analytic procedures when studying the interactions between therapists and patients that are associated with favorable therapeutic outcomes. Inspired by a recent line of alliance research using dyadic multilevel modeling, the present study investigated the hypothesis that convergence in the patient-therapist working alliance (i.e., increased similarity in ratings of the alliance across treatment) would be associated with better outcomes. Method: Data were retrieved from two samples: 1. A randomized controlled trial for treatment resistant depression (N = 96 dyads), and 2. An archival dataset of naturalistic psychotherapies from public health care (N = 139 dyads). Multilevel growth curve analysis was employed to investigate the degree of change in session-to-session agreement of global WAI ratings between therapists and patients (i.e., alliance convergence) as a predictor of symptom reduction in the BDI-II and the SCL-90R. Results: Contrary to our expectations, alliance convergence did not predict outcome in either sample, but was negatively associated with symptom severity in Study 2. Implications for understanding the complexity of dyadic processes and alliance work in psychotherapy are discussed.
Keywords: alliance; alliance convergence; dyadic multilevel modeling; outcome.