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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 analyti... Full description

Journal Title: Journal of Abnormal Psychology 2019, Vol.128(6), pp.547-562
Main Author: King, Kevin M.
Other Authors: Pullmann, Michael D. , Lyon, Aaron R. , Dorsey, Shannon , Lewis, Cara C.
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: ISSN: 0021-843X ; E-ISSN: 1939-1846 ; DOI: 10.1037/abn0000417
Link: http://dx.doi.org/10.1037/abn0000417
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recordid: apa_articles10.1037/abn0000417
title: Using Implementation Science to Close the Gap Between the Optimal and Typical Practice of Quantitative Methods in Clinical Science
format: Article
creator:
  • King, Kevin M.
  • Pullmann, Michael D.
  • Lyon, Aaron R.
  • Dorsey, Shannon
  • Lewis, Cara C.
subjects:
  • Quantitative Methods
  • Open Science
  • Implementation Science
  • Quantitative Implementation
ispartof: Journal of Abnormal Psychology, 2019, Vol.128(6), pp.547-562
description: 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. ; Making studies more replicable will require more effective use of statistics in research, but there is a large gap between how statistics are applied in psychological research and how they should be applied. The current article describes how the lessons of clinical implementation science, which has focused on getting evidence based treatments into community practice settings, may be applied to improve research in clinical psychology.
language: eng
source:
identifier: ISSN: 0021-843X ; E-ISSN: 1939-1846 ; DOI: 10.1037/abn0000417
fulltext: fulltext
issn:
  • 0021-843X
  • 0021843X
  • 1939-1846
  • 19391846
url: Link


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descriptionQuantitative 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. ; Making studies more replicable will require more effective use of statistics in research, but there is a large gap between how statistics are applied in psychological research and how they should be applied. The current article describes how the lessons of clinical implementation science, which has focused on getting evidence based treatments into community practice settings, may be applied to improve research in clinical psychology.
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abstractQuantitative 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. ; Making studies more replicable will require more effective use of statistics in research, but there is a large gap between how statistics are applied in psychological research and how they should be applied. The current article describes how the lessons of clinical implementation science, which has focused on getting evidence based treatments into community practice settings, may be applied to improve research in clinical psychology.
pubAmerican Psychological Association
doi10.1037/abn0000417
orcidid0000-0001-8358-9946
date2019-08-01