Ideal for exercise science, athletic training, and physical therapy students, this updated edition of Knight and Draper'sTherapeutic Modalities: The Art and Science covers the knowledge and skills needed to select the best therapeutic modality for each client injury. This edition helps students hone their clinical decision-making skills by teaching both the how and the why of each therapeutic modality, offering the application that today's student craves. Retaining the accessible student-friendly writing style and focus on kinesthetic learning that made the book so successful, the third edition is enhanced by new chapters, new photos, and significant updates throughout that reflect the latest research and advances in the field. A focus on both the art and science of practice. The authors emphasize the importance of using evidence-based information while acknowledging that clinicians must also rely on experience and tradition when selecting the best modalities. New chapters on Positional Release Therapy, Nitrogen Vapor Cryotherapy, and Bio-Electro-Magnetic-Energy-Regulation (BEMER Opening) cover vital topics for today's professionals. Practical guidance. Each modality chapter is followed by step-by-step procedures for effectively using that modality in practice to prepare students for everyday challenges. Chapter-opening Scenes feature a short scenario that poses a situation related to chapter content, while chapter-closing Scenes complete the vignette, helping students see how content is put into practice. Five-Step Application Procedure Templates help students organize the information needed to apply a modality. Modality Myths present common misunderstandings and provide a scientific basis for setting the record straight. Concept Check exercises deepen understanding. Application Tips offer tips for practice and understanding. Reflections and Case Studies encourage students to reflect on and apply the different therapeutic modalities. Review Questions are ideal for review and self-testing. eBook available for purchase. Fast, smart, and convenient, today's eBooks can transform learning. These interactive, fully searchable tools offer 24/7 access on multiple devices, the ability to highlight and share notes, and more

This paper estimates the parameters of a stylized dynamic stochastic general equilibrium model using maximum likelihood and Bayesian methods, paying special attention to the issue of weak parameter identification. Given the model and the available data, the posterior estimates of the weakly identified parameters are very sensitive to the choice of priors. We provide a set of tools to diagnose weak identification, which include surface plots of the log-likelihood as a function of two parameters, heat plots of the log-likelihood as a function of three parameters, Monte Carlo simulations using artificial data, and Bayesian estimation using three sets of priors. We find that the policy coefficients and the parameter governing the elasticity of labor supply are weakly identified by the data, and posterior predictive distributions remind us that DSGE models may make poor forecasts even when they fit the data well. Although parameter identification is model- and data-specific, the lack of identification of some key structural parameters in a small-scale DSGE model such as the one we examine should raise a red flag to researchers trying to estimate--and draw valid inferences from--large-scale models featuring many more parameters.