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The consequences of “anchoring” in longitudinal multilevel models: bias in the estimation of patient variability and therapist effects

Abstract Increasingly, longitudinal multilevel models are being used to investigate change in psychotherapy. An issue related to these methods concerns how measurement error is modeled; one proposed model anchors patient trajectories on the initial observation, thereby assuming that the initial observation contains no error. Wampold and Bolt (2006) claimed that anchoring overestimates patient variability and consequently underestimates therapist effects. In this study, a simulation study was conducted to test this conjecture, and it was found that anchoring yields estimates of patient variability that are substantially higher than the actual variability and, therefore, therapist effects are underestimated.