Routledge International Handbook of Advanced Quantitative Methods in Nursing Research

Designed to support global development of nursing science, the Routledge International Handbook of Advanced Quantitative Methods in Nursing Research provides a new, comprehensive, and authoritative treatment of advanced quantitative methods for nursing research. Incorporating past approaches that have served as the foundation for the science, this cutting edge book also explores emerging approaches that will shape its future. Divided into six parts, it covers: -the domain of nursing science - measurement-classical test theory, IRT, clinimetrics, behavioral observation, biophysical measurement -models for prediction and explanation-SEM, general growth mixture models, hierarchical models, analysis of dynamic systems -intervention research-theory-based interventions, causality, third variables, pilot studies, quasi-experimental design, joint models for longitudinal data and time to event -e-science-DIKW paradigm, big data, data mining, omics, FMRI -special topics-comparative effectiveness and meta-analysis, patient safety, economics research in nursing, mixed methods, global research dissemination Written by a distinguished group of international nursing scientists, scientists from related fields, and methodologists, the Handbook is the ideal reference for everyone involved in nursing science, whether they are graduate students, academics, editors and reviewers, or clinical investigators.

Dr. Susan J. Henly is Professor Emerita, University of Minnesota, School of Nursing, Minneapolis, Minnesota, USA. She earned her BS with a major in nursing from the College of St. Teresa, Winona, Minnesota and practiced in rural acute care, perinatal nursing, and neonatal intensive care in Alaska, New Mexico and Minnesota before returning to graduate school. Her MS degree in nursing, focused on perinatal health research, is from the University of Minnesota, Twin Cities. She earned her PhD in psychometric methods from the University of Minnesota, Twin Cities. She served on the College of Nursing faculty at the University of North Dakota, Grand Forks prior to her appointment at Minnesota. Over the past 30 years, Sue's research has focused on psychmetric methods for nursing research with special interests in robustness of estimators in the analysis of covariance structures, model selection, and longitudinal models for health trajectories. She was Methods Director for the National Institute of Nursing Research-funded Center for Health Trajectory Research at the University of Minnesota, School of Nursing. Sue has a special interest in advancing quantitative methods in nursing PhD programs. She was director of the American Indian MS to PhD Nursing Science Bridge Program (funded by the National Institute of General Medical Sciences) and chaired the Council for the Advancement of Nursing Sciences Idea Festival for Nursing Science Education. She has extensive service as a peer reviewer for nursing science, related fields, and methodology journals and has contributed to the peer review literature. Sue is Editor of Nursing Research. She is a member of the Japan Academy of Nursing Science and the American Academy of Nursing.

Part 1: The Domain of Nursing Science 1. The Domain of Nursing Science 2. Theorising in Nursing Science Part 2: Measurement 3. Classical Test Theory 4. Clinimetrics 5. Item Response Theory: A Statistical Theory of Measurement Based on Fungible Items 6. Behavioral Observation 7. Biophysical Observation Part 3: Prediction and Explanation 8. Structural Equation Modeling 9. General Growth Mixture Models 10. Multilevel Models 11. Analysis of Dynamic Systems: The Modeling of Change and Variability Part 4: Experimental and Quasi-experimental Design 12. Theory-based Nursing Interventions 13. Pilot Studies for Randomized Clinical Trials 14. Causality in Experiments and Observational Studies 15. Quasi-experimental Design in Nursing Research 16. Third Variables: Scientific Meanings and Modeling in Non-randomized Studies 17. Joint Models for Longitudinal Data and Time-to-event Occurrence Part 5: E-science Methods 18. Data, Information, Knowledge, Wisdom 19. Big Data in Nursing Research 20. Data Mining and Data Visualization 21. Genomic, Transcriptomic, Epigenomic, and Proteomic Approaches 22. A Survey of Sources of Noise in FMRI Part 6: Applications and Special Topics 23. Comparative Effectiveness Research and Meta-analysis 24. Patient Safety Research: Methodological Challenges 25. Economic Evaluations for Nursing Research 26. Mixed Methods 27. Global Generation and Dissemination of Nursing Science

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