Using implementation science to close the gap between the optimal and typical practice of quantitative methods in clinical science.

Quantitative methods remain the fundamental approach for hypothesis testing, but in approaches to data analysis there is substantial evidence of a gap between what is optimal and what is typical. It is clear that diffusion and dissemination alone are not maximally effective at improving data analytic practices in clinical psychological science. Amid declines in quantitative psychology training, and growing demand for advanced quantitative methods, applied researchers are increasingly called upon to conduct and evaluate research using methods in which they lack expertise. This "research-to-practice" gap in which rigorously developed and empirically supported quantitative methods are not applied in practice has received little attention. In this article, we describe how implementation science, which aims to reduce the research-to-practice gap in health care, offers a promising set of methods for closing the gap for quantitative methods. By identifying determinants of practice (i.e., barriers and facilitators of change), implementation strategies can be selected to increase adoption and high-fidelity application of new quantitative methods to improve scientific inferences and policy and practice decisions in clinical psychological science. (PsycINFO Database Record (c) 2019 APA, all rights reserved).