Evidence-Based Health Care Management: Multivariate Modeling Approaches / Edition 1

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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.

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Product Details

  • ISBN-13: 9780792374855
  • Publisher: Springer US
  • Publication date: 10/31/2001
  • Edition description: 2002
  • Edition number: 1
  • Pages: 233
  • Product dimensions: 9.21 (w) x 6.14 (h) x 0.69 (d)

Table of Contents

List of Tables. List of Figures. About the Author. Foreword. Acknowledgments. 1. 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. Index.

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