Leseprobe
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Statistics and Data Analysis Literacy for Nurses
James B. Schreiber (Hrsg.), Melanie Turk (Hrsg.)
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Naturwissenschaften, Medizin, Informatik, Technik / Allgemeines
Beschreibung
Takes the fear out of statistics through a combination of visual and practical learning!
"Statistical acumen and confidence have evaded many nurses for multiple reasons requiring educators to find alternate ways to bridge this knowledge gap. [This book] fills a void and meets this need. Students and educators alike will benefit from its straightforward, pragmatic style in explaining applied statistical analysis. Drawing on the deep expertise of its authors, the book should become a go-to reference for anyone looking to gain knowledge in applied statistics."
—Mary Ellen Smith Glasgow, PhD, RN, ACNS-BC, ANEF, FAAN
Dean and Professor
Duquesne University School of Nursing
The only biostatistics resource written specifically for MSN and DNP nursing students, this text provides a unique graphic representation of data to help students understand difficult analytic information. Modelled on an effective and proven method of learning—graphical illustrations first, technical details second—the text helps students understand appropriate research design, measurement, and analysis of how results are calculated. Providing a gentle progression into the complexities of statistics, it builds upon concepts step-by-step, helping students to reinforce prior knowledge before moving on the new information.
Chapters initially focus on the practical application of content to illustrate how statistics are used, followed by working examples—addressing both research and evidence-based content and epidemiology and population health content—to enhance understanding and underscore the importance of the methodology. The book integrates the latest changes from the ASA related to statistical significance tested and addresses recent deliberations about sample size. Abundant Instructor Resources include PowerPoints, a test bank, data sets, information on how to obtain online programming classes, and sample lesson plans and activities.
Key Features:
- Uses a proven, step-by-step method including a graphic representation of data followed by analytic content
- Follows graphical representation with examples of appropriate research design, measurement, and analysis of how results are calculated
- Includes a basic review checklist
- Provides chapter objectives, summaries, review questions, and key terms
- Offers data sets for those instructors/students who wish to do analyses
- Addresses SPSS and other software, including open source (JAMOVI, JASP, and R-Studio)
- Includes a comprehensive Instructor Resource Package to ensure faculty have the tools they need to work with students
