Fr. 189.00

Evidence-Based Health Care Management - Multivariate Modeling Approaches

Inglese · Copertina rigida

Spedizione di solito entro 6 a 7 settimane

Descrizione

Ulteriori informazioni

Evidence-Based Health Care Management introduces the principles and methods for drawing sound causal inferences in research on health services management. The emphasis is on the application of structural equation modeling techniques and other analytical methods to develop causal models in health care management. Topics include causality, theoretical model building, and model verification. Multivariate modeling approaches and their applications in health care management are illustrated.
The primary goals of the book are to present advanced principles of health services management research and to familiarize students with the multivariate analytic methods and procedures now in use in scientific research on health care management. The hope is to help health care managers become better equipped to use causal modeling techniques for problem solving and decision making.
Evidence-based knowledge is derived from scientific replication and verification of facts. Used consistently and appropriately, it enables a health care manager to improve organizational performance. Causal inference in health care management is a highly feasible approach to establishing evidence-based knowledge that can help navigate an organization to high performance. This book introduces the principles and methods for drawing causal inferences in research on health services management.

Sommario

Chapter1 An Introduction to Evidence-Based Management of Health Care.- 2 Causal Inference: Foundations for Health Care Managerial Decision-Making.- 3 Research on Health Services Management: The Search for Structure.- 4 Exploratory Analytical Modeling Methods.- 5 Introduction to Structural Equation Modeling.- 6 Confirmatory Factor Analysis.- 7 Structural Equations for Directly Observed Variables: Recursive and Non-Recursive Models.- 8 Structural Equation Models with Latent Variables.- 9 Covariance Structure Models.- 10 Multiple Group Comparison with Panel Data.- 11 Multilevel Covariance Modeling.- 12 Growth Curve Modeling with Longitudinal Data.- 13 Epilogue.

Riassunto

Evidence-Based Health Care Management introduces the principles and methods for drawing sound causal inferences in research on health services management. The emphasis is on the application of structural equation modeling techniques and other analytical methods to develop causal models in health care management. Topics include causality, theoretical model building, and model verification. Multivariate modeling approaches and their applications in health care management are illustrated.
The primary goals of the book are to present advanced principles of health services management research and to familiarize students with the multivariate analytic methods and procedures now in use in scientific research on health care management. The hope is to help health care managers become better equipped to use causal modeling techniques for problem solving and decision making.
Evidence-based knowledge is derived from scientific replication and verification of facts. Used consistently and appropriately, it enables a health care manager to improve organizational performance. Causal inference in health care management is a highly feasible approach to establishing evidence-based knowledge that can help navigate an organization to high performance. This book introduces the principles and methods for drawing causal inferences in research on health services management.

Dettagli sul prodotto

Autori Thomas T H Wan, Thomas T. H. Wan, Thomas T.H. Wan
Editore Springer, Berlin
 
Lingue Inglese
Formato Copertina rigida
Pubblicazione 29.06.2009
 
EAN 9780792374855
ISBN 978-0-7923-7485-5
Pagine 233
Dimensioni 163 mm x 246 mm x 20 mm
Peso 544 g
Illustrazioni XVII, 233 p. 2 illus.
Categorie Scienze sociali, diritto, economia > Economia > Economia politica

Management, Performance, B, Service, Research, Wirtschaftspolitik, politische Ökonomie, Economic Policy, Organization, Modeling, Public Economics, Economics and Finance, Value-at-Risk, Political Economy, Management science, Public finance, Science and technology

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