Routledge International Handbook of Advanced Quantitative Methods in Nursing Research
Edited by Susan J. Henly
Routledge – 2016 – 468 pages
Routledge – 2016 – 468 pages
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.
Part 1: The Domain of Nursing Science 1. The Domain of Nursing Science Patricia A. Grady and Jessica M. McIlvane 2. Theorising in Nursing Science Susan J Henly Part 2: Measurement 3. Classical Test Theory Klaas Sijtsma 4. Clinimetrics Henrica de Vet and Anna Beurskens 5. Item Response Theory: A Statistical Theory of Measurement Based on Fungible Items Peter Baldwin and Howard Wainer 6. Behavioral Observation Tondi M Harrison 7. Biophysical Observation Ulf G Bronas and Diane Treat-Jacobsen Part 3: Prediction and Explanation 8. Structural Equation Modeling Sarah J. Schmiege and Angela D Bryan 9. General Growth Mixture Models Sarah A Stoddard 10. Multilevel Models Ulrike Grittner and Nils Lahmann 11. Analysis of Dynamic Systems: The Modeling of Change and Variability Pascal R Deboeck and Steven M Boker Part 4: Experimental and Quasi-experimental Design 12. Theory-based Nursing Interventions Souraya Sidani and Julie Fleury 13. Pilot Studies for Randomized Clinical Trials Nancy Feeley and Sylvie Cossette 14. Causality in Experiments and Observational Studies Donald B Rubin and Elizabeth R Zell 15. Quasi-experimental Design in Nursing Research Patricia Eckhardt and David Rindskopf 16. Third Variables: Scientific Meanings and Modeling in Non-randomized Studies Donna L Coffman 17. Joint Models for Longitudinal Data and Time-to-event Occurrence Yue Liu and Lei Liu Part 5: E-science Methods 18. Data, Information, Knowledge, Wisdom Nancy Staggers and Ramona Nelson 19. Big Data in Nursing Research Bonnie L Westra, Karen A Monsen and Connie W Delaney 20. Data Mining and Data Visualization Krzysztof J Cios and Dat T Nguyen 21. Genomic, Transcriptomic, Epigenomic, and Proteomic Approaches Yvette P Conley 22. A Survey of Sources of Noise in FMRI Douglas N. Greve, Gregory G. Brown, Bryon A. Mueller, Gary Glover, Thomas T. Liu, and the Functional Biomedical Research Network Part 6: Applications and Special Topics 23. Comparative Effectiveness Research and Meta-analysis Nicky Cullum and Jo Dumville 24. Patient Safety Research: Methodological Challenges Sean P Clarke and Maria Schubert 25. Economic Evaluations for Nursing Research Mayuko Uchida and Patricia W Stone 26. Mixed Methods Louise Doyle 27. Global Generation and Dissemination of Nursing Science Molly C Dougherty and Nalini Jairath
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.