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| data analysis in nursing: Nursing Research Using Data Analysis Mary De Chesnay, 2014-12-05 This is a concise, step-by-step guide to conducting qualitative nursing research using various forms of data analysis. It is part of a unique series of books devoted to seven different qualitative designs and methods in nursing, written for both novice researchers and specialists seeking to develop or expand their competency. This practical resource encompasses such methodologies as content analysis, a means of organizing and interpreting data to elicit themes and concepts; discourse analysis, used to analyze language to understand social or historical context; narrative analysis, in which the researcher seeks to understand human experience through participant stories; and focus groups and case studies, used to understand the consensus of a group or the experience of an individual and his or her reaction to a difficult situation such as disease or trauma. Written by a noted qualitative research scholar and contributing experts, the book describes the philosophical basis for conducting research using data analysis and delivers an in-depth plan for applying its methodologies to a particular study, including appropriate methods, ethical considerations, and potential challenges. It presents practical strategies for solving problems related to the conduct of research using the various forms of data analysis and presents a rich array of case examples from published nursing research. These include author analyses to support readers in decision making regarding their own projects. The book embraces such varied topics as data security in qualitative research, the image of nursing in science fiction literature, the trajectory of research in several nursing studies throughout Africa, and many others. Focused on the needs of both novice researchers and specialists, it will be of value to health institution research divisions, in-service educators and students, and graduate nursing educators and students. Key Features: Explains how to conduct nursing research using content analysis, discourse analysis, narrative analysis, and focus groups and case studies Presents state-of-the-art designs and protocols Focuses on solving practical problems related to the conduct of research Features rich nursing exemplars in a variety of health/mental health clinical settings in the United States and internationally |
| data analysis in nursing: Statistics and Data Analysis for Nursing Research Denise F. Polit, 2013-10-03 The second edition of Statistics and Data Analysis for Nursing, uses a conversational style to teach students how to use statistical methods and procedures to analyse research findings. Students are guided through the complete analysis process from performing a statistical analysis to the rationale behind doing so. In addition, management of data, including how and why to recode variables for analysis, how to clean data, and how to work around missing data, is discussed. The full text downloaded to your computer With eBooks you can: search for key concepts, words and phrases make highlights and notes as you study share your notes with friends eBooks are downloaded to your computer and accessible either offline through the Bookshelf (available as a free download), available online and also via the iPad and Android apps. Upon purchase, you'll gain instant access to this eBook. Time limit The eBooks products do not have an expiry date. You will continue to access your digital ebook products whilst you have your Bookshelf installed. |
| data analysis in nursing: Big Data-Enabled Nursing Connie W. Delaney, Charlotte A. Weaver, Judith J. Warren, Thomas R. Clancy, Roy L. Simpson, 2017-11-02 Historically, nursing, in all of its missions of research/scholarship, education and practice, has not had access to large patient databases. Nursing consequently adopted qualitative methodologies with small sample sizes, clinical trials and lab research. Historically, large data methods were limited to traditional biostatical analyses. In the United States, large payer data has been amassed and structures/organizations have been created to welcome scientists to explore these large data to advance knowledge discovery. Health systems electronic health records (EHRs) have now matured to generate massive databases with longitudinal trending. This text reflects how the learning health system infrastructure is maturing, and being advanced by health information exchanges (HIEs) with multiple organizations blending their data, or enabling distributed computing. It educates the readers on the evolution of knowledge discovery methods that span qualitative as well as quantitative data mining, including the expanse of data visualization capacities, are enabling sophisticated discovery. New opportunities for nursing and call for new skills in research methodologies are being further enabled by new partnerships spanning all sectors. |
| data analysis in nursing: Statistics and Data Analysis for Nursing Denise Polit, Eileen Lake, 2021-06-16 |
| data analysis in nursing: The Application of Content Analysis in Nursing Science Research Helvi Kyngäs, Kristina Mikkonen, Maria Kääriäinen, 2019-10-31 This book provides principles on content analysis and its application into development of nursing theory. It offers clear guidance to students, lecturers and researchers to gain a deeper understanding of the method of content analysis, its implementation into their own research and criteria of trustworthiness evaluation. The book is written in user-friendly language with provided research examples and cases, and the content is illustrated by figures and tables. The authors offer their expertise in providing a well thought through explanation of content analysis in didactical style, which will enhance university education. The book includes highly experienced researchers who have published articles on content analysis and the trustworthiness of the method with more than 10 000 citations. Divided into two parts, this book explores the application of content analysis into nursing science. The first part presents the philosophical position of content analysis, inductive and deductive methods of using content analysis, trustworthiness of the method, and ethical consideration of using content analysis. The second part informs on the theory development based on content analysis, conceptualization of the concepts of content analysis into generation of items and instrument development, and statistical testing of a hypothetical model. The last chapter shows a new approach to using content analysis in systematic reviews and quality evaluation of methodology within systematic review process. The book is an essential tool for nursing science, providing instruction on key methodological elements in order to provide rigorously conducted empirical research for clinical practice and nursing education. |
| data analysis in nursing: Introductory Statistics for Health and Nursing Using SPSS Louise Marston, 2009-12-15 Introductory Statistics for Health & Nursing using SPSS is an impressive introductory statistics text ideal for all health science and nursing students. Health and nursing students can be anxious and lacking in confidence when it comes to handling statistics. This book has been developed with this readership in mind. This accessible text eschews long and off-putting statistical formulae in favour of non-daunting practical and SPSS-based examples. What′s more, its content will fit ideally with the common course content of stats courses in the field. Introductory Statistics for Health & Nursing using SPSS is also accompanied by a companion website containing data-sets and examples for use by lecturers with their students. The inclusion of real-world data and a host of health-related examples should make this an ideal core text for any introductory statistics course in the field. |
| data analysis in nursing: Clinical Analytics and Data Management for the DNP Martha L. Sylvia, PhD, MBA, RN, Mary F. Terhaar, PhD, RN, ANEF, FAAN, 2023-01-18 Praise for the first edition: DNP students may struggle with data management, since their projects are not research but quality improvement, and this book covers the subject well. I recommend it for DNP students for use during their capstone projects. Score: 98, 5 Stars -- Doody's Medical Reviews This unique text and reference—the only book to address the full spectrum of clinical data management for the DNP student—instills a fundamental understanding of how clinical data is gathered, used, and analyzed, and how to incorporate this data into a quality DNP project. The new third edition is updated to reflect changes in national health policy such as quality measurements, bundled payments for specialty care, and Advances to the Affordable Care Act (ACA) and evolving programs through the Centers for Medicare and Medicaid Services (CMS). The third edition reflects the revision of 2021 AACN Essentials and provides data sets and other examples in Excel and SPSS format, along with several new chapters. This resource takes the DNP student step-by-step through the complete process of data management, from planning through presentation, clinical applications of data management that are discipline-specific, and customization of statistical techniques to address clinical data management goals. Chapters are brimming with descriptions, resources, and exemplars that are helpful to both faculty and students. Topics spotlight requisite competencies for DNP clinicians and leaders such as phases of clinical data management, statistics and analytics, assessment of clinical and economic outcomes, value-based care, quality improvement, benchmarking, and data visualization. A progressive case study highlights multiple techniques and methods throughout the text. New to the Third Edition: New Chapter: Using EMR Data for the DNP Project New chapter solidifies link between EBP and Analytics for the DNP project New chapter highlights use of workflow mapping to transition between current and future state, while simultaneously visualizing process measures needed to ensure success of the DNP project Includes more examples to provide practical application exercises for students Key Features: Disseminates robust strategies for using available data from everyday practice to support trustworthy evaluation of outcomes Uses multiple tools to meet data management objectives [SPSS, Excel®, Tableau] Presents case studies to illustrate multiple techniques and methods throughout chapters Includes specific examples of the application and utility of these techniques using software that is familiar to graduate nursing students Offers real world examples of completed DNP projects Provides Instructor’s Manual, PowerPoint slides, data sets in SPSS and Excel, and forms for completion of data management and evaluation plan |
| data analysis in nursing: Secondary Analysis of Electronic Health Records MIT Critical Data, 2016-09-09 This book trains the next generation of scientists representing different disciplines to leverage the data generated during routine patient care. It formulates a more complete lexicon of evidence-based recommendations and support shared, ethical decision making by doctors with their patients. Diagnostic and therapeutic technologies continue to evolve rapidly, and both individual practitioners and clinical teams face increasingly complex ethical decisions. Unfortunately, the current state of medical knowledge does not provide the guidance to make the majority of clinical decisions on the basis of evidence. The present research infrastructure is inefficient and frequently produces unreliable results that cannot be replicated. Even randomized controlled trials (RCTs), the traditional gold standards of the research reliability hierarchy, are not without limitations. They can be costly, labor intensive, and slow, and can return results that are seldom generalizable to every patient population. Furthermore, many pertinent but unresolved clinical and medical systems issues do not seem to have attracted the interest of the research enterprise, which has come to focus instead on cellular and molecular investigations and single-agent (e.g., a drug or device) effects. For clinicians, the end result is a bit of a “data desert” when it comes to making decisions. The new research infrastructure proposed in this book will help the medical profession to make ethically sound and well informed decisions for their patients. |
| data analysis in nursing: Statistics for Nursing and Allied Health Stacey Beth Plichta, Laurel S. Garzon, 2009 This introductory textbook explores the role of research in health care and focuses in particular on the importance of organizing and describing research data using basic statistics. The goal of the text is to teach students how to analyze data and present the results of evidence-based data analysis. Based on the commonly-used SPSS software, a comprehensive range of statistical techniques—both parametric and non-parametric—are presented and explained. Examples are given from nursing, health administration, and health professions, followed by an opportunity for students to immediately practice the technique. |
| data analysis in nursing: Statistics for Nursing Research Susan K. Grove, Daisha J. Cipher, 2016-02-01 Understand the statistical methods used in nursing research articles! Statistics for Nursing Research: A Workbook for Evidence-Based Practice, 2nd Edition helps you interpret and analyze the statistical data found in health sciences research articles. Practical exercises show how to critically appraise sampling and measurement techniques, evaluate results, and conduct a power analysis for a study. Written by nursing statistics experts Susan Grove and Daisha Cipher, this is the only statistics workbook for nursing to include research examples from both nursing and medical literature for a complete perspective on health sciences research. Comprehensive coverage includes exercises that address all common techniques of sampling, measurement, and statistical analysis that you are likely to see in nursing and medical literature. A literature-based approach incorporates a relevant research article into each exercise/chapter, with key excerpts. 45 sampling, measurement, and statistical analysis exercises provide a practical review of both basic and advanced techniques, and prepare you to apply statistics to nursing practice. Consistent format for all chapters facilitates quick review and easier learning, covering the statistical technique in review, results from a research article, and study questions. Study questions in each chapter help you apply concepts to clinical practice. Questions to Be Graded in each chapter may be completed and submitted online, to assess your mastery of key statistical techniques. A concise index makes it easy to locate information quickly. NEW examples show the latest, high-quality research studies. NEW! Expanded coverage helps undergraduate students apply the information learned in statistics and research courses, serves as a refresher/review for graduate students, and also helps in critically appraising studies to determine whether their findings may be used in evidence-based practice. NEW! Understanding Statistical Methods section includes exercises to help in understanding the levels of measurement (nominal, ordinal, interval, and ratio) and in appraising the samples and measurement methods in studies. NEW! Conducting and Interpreting Statistical Analyses section includes exercises to help in understanding the power analysis and how to conduct a power analysis for a study, showing how to determine the most appropriate statistical method(s) for analyzing data for a class project, for a clinical agency project, or for an actual research study. NEW! Answers to study questions are located in the back of the book. |
| data analysis in nursing: Measuring Capacity to Care Using Nursing Data Evelyn Hovenga, Cherrie Lowe, 2020-03-13 Measuring Capacity to Care Using Nursing Data presents evidence-based solutions regarding the adoption of safe staffing principles and the optimum use of operational data to enable health service delivery strategies that result in improved patient and organizational outcomes. Readers will learn how to make better use of informatics to collect, share, link and process data collected operationally for the purpose of providing real-time information to decision- makers. The book discusses topics such as dynamic health care environments, health care operational inefficiencies and costly events, how to measure nursing care demand, nursing models of care, data quality and governance, and big data. The content of the book is a valuable source for graduate students in informatics, nurses, nursing managers and several members involved in health care who are interested in learning more about the beneficial use of informatics for improving their services. Presents and discusses evidences from real-world case studies from multiple countries Provides detailed insights of health system complexity in order to improve decision- making Demonstrates the link between nursing data and its use for efficient and effective healthcare service management Discusses several limitations currently experienced and their impact on health service delivery |
| data analysis in nursing: Nursing Research Using Data Analysis Mary De Chesnay, PhD, RN, PMHCNS-BC, FAAN, 2014-12-05 This is a concise, step-by-step guide to conducting qualitative nursing research using various forms of data analysis. It is part of a unique series of books devoted to seven different qualitative designs and methods in nursing, written for both novice researchers and specialists seeking to develop or expand their competency. This practical resource encompasses such methodologies as content analysis, a means of organizing and interpreting data to elicit themes and concepts; discourse analysis, used to analyze language to understand social or historical context; narrative analysis, in which the researcher seeks to understand human experience through participant stories; and focus groups and case studies, used to understand the consensus of a group or the experience of an individual and his or her reaction to a difficult situation such as disease or trauma. Written by a noted qualitative research scholar and contributing experts, the book describes the philosophical basis for conducting research using data analysis and delivers an in-depth plan for applying its methodologies to a particular study, including appropriate methods, ethical considerations, and potential challenges. It presents practical strategies for solving problems related to the conduct of research using the various forms of data analysis and presents a rich array of case examples from published nursing research. These include author analyses to support readers in decision making regarding their own projects. The book embraces such varied topics as data security in qualitative research, the image of nursing in science fiction literature, the trajectory of research in several nursing studies throughout Africa, and many others. Focused on the needs of both novice researchers and specialists, it will be of value to health institution research divisions, in-service educators and students, and graduate nursing educators and students. Key Features: Explains how to conduct nursing research using content analysis, discourse analysis, narrative analysis, and focus groups and case studies Presents state-of-the-art designs and protocols Focuses on solving practical problems related to the conduct of research Features rich nursing exemplars in a variety of health/mental health clinical settings in the United States and internationally |
| data analysis in nursing: Secondary Qualitative Data Analysis in the Health and Social Sciences Cheryl Tatano Beck, 2019-01-15 Despite a long history in quantitative research, it is only recently that enthusiasm for secondary analysis of qualitative data has gained momentum across health and social science disciplines. Given that researchers have long known the inordinate amount of time and energy invested in conducting qualitative research, the appeal of secondary analysis of qualitative data is clear. Involving the use of an existing dataset to answer research questions that are different from those asked in the original study, this method allows researchers to once again make use of their hard-earned qualitative dataset and to listen to their participants’ voices to the best of their ability in order to improve care and promote understanding. As secondary qualitative data analysis continues to evolve, more methodological guidance is needed. This book outlines three approaches to secondary data analysis and addresses the key issues that researchers need to wrestle with, such as ethical considerations, voice, and representation. Intellectual and interpretive hazards that can jeopardize the outcome of these analyses are highlighted and discussed, as are the criteria for assessing their quality and trustworthiness. Written as a thought-provoking guide for qualitative researchers from across the health and social sciences, this text includes a review of the state of the science in nursing and a number of in-depth illustrative case studies. |
| data analysis in nursing: Data Management and Analysis Using JMP Jane E Oppenlander, Patricia Schaffer, 2017-10-17 A holistic, step-by-step approach to analyzing health care data! Written for both beginner and intermediate JMP users working in or studying health care, Data Management and Analysis Using JMP: Health Care Case Studies bridges the gap between taking traditional statistics courses and successfully applying statistical analysis in the workplace. Authors Jane Oppenlander and Patricia Schaffer begin by illustrating techniques to prepare data for analysis, followed by presenting effective methods to summarize, visualize, and analyze data. The statistical analysis methods covered in the book are the foundational techniques commonly applied to meet regulatory, operational, budgeting, and research needs in the health care field. This example-driven book shows practitioners how to solve real-world problems by using an approach that includes problem definition, data management, selecting the appropriate analysis methods, step-by-step JMP instructions, and interpreting statistical results in context. Practical strategies for selecting appropriate statistical methods, remediating data anomalies, and interpreting statistical results in the domain context are emphasized. The cases presented in Data Management and Analysis Using JMP use multiple statistical methods. A progression of methods--from univariate to multivariate--is employed, illustrating a logical approach to problem-solving. Much of the data used in these cases is open source and drawn from a variety of health care settings. The book offers a welcome guide to working professionals as well as students studying statistics in health care-related fields. |
| data analysis in nursing: Narrative Research in Nursing Immy Holloway, Dawn Freshwater, 2009-07-23 Narrative research is an increasingly popular way of carrying outqualitative research by analysing the stories or experience. Thefindings of this type of qualitative research can be used toimprove nursing education, nursing practice and patient care and toexplore the experience of illness and the interaction betweenprofessionals. Narrative Research in Nursing provides acomprehensive yet straightforward introduction to narrativeresearch which examines the skills needed to perform narrativeinterviews, analyse data, and publish results and enables nurseresearchers to use the method systematically and rigorously. Narrative Research in Nursing examines the nature of narratives andtheir role in the development of nursing and health care.Strategies and procedures are identified, including thepracticalities of sampling, data collection, analysis andpresentation of findings. The authors discuss authenticity ofevidence and ethical issues while also exploring problems andpracticalities inherent in narrative inquiry and its dissemination.Narrative Research in Nursing is a valuable resource for nursesinterested in writing and publishing narrative research. |
| data analysis in nursing: Critical Thinking Assessment in Nursing Education Programs Noreen C. Facione, Peter A. Facione, 1997 |
| data analysis in nursing: Burns and Grove's The Practice of Nursing Research - E-Book Jennifer R. Gray, Susan K. Grove, Suzanne Sutherland, 2016-08-10 - NEW Mixed Methods Research chapter and emphasis covers this increasingly popular approach to research. - NEW! Expanded emphasis on qualitative research provides more balanced coverage of qualitative and quantitative methods, addressing the qualitative research methodologies that are often the starting point of research projects, particularly in magnet hospitals and DNP programs. - ENHANCED emphasis on evidence-based practice addresses this key graduate-level QSEN competency. - UPDATED emphasis on the most currently used research methodologies focuses on the methods used in both quantitative research and qualitative research, as well as outcomes research and mixed methods research. - NEW! Quick-reference summaries are located inside the book's covers, including a table of research methods on the inside front cover and a list of types of research syntheses (with definitions) inside the back cover. - NEW student resources on the Evolve companion website include 400 interactive review questions along with a library of 10 Elsevier research articles. - NEW! Colorful design highlights key information such as tables and research examples |
| data analysis in nursing: Data Analytics in Medicine Information Resources Management Association, 2019-11-18 This book examines practical applications of healthcare analytics for improved patient care, resource allocation, and medical performance, as well as for diagnosing, predicting, and identifying at-risk populations-- |
| data analysis in nursing: Applications Manual to Accompany Data Analysis & Statistics for Nursing Research Denise F. Polit, 1996 Designed to be used with the textbook Data Analysis and Statistics for Nursing Research, this hands-on manual provides nursing students with an opportunity to broaden their statistical and data analysis skills using a computer. The manual includes a guidebook with computer exercises, arranged in chapters corresponding to those in the textbook. Also included is a CD-ROM that contains a large raw data set, and commands for accessing the data set for two widely used statistical software packages, SPSS and SAS. - Back cover. |
| data analysis in nursing: Nursing Research and Statistics Suresh Sharma, 2010-09-17 Nursing Research and Statistics |
| data analysis in nursing: Nursing Research Janice M. Morse, Peggy-Anne Field, 2013-11-11 |
| data analysis in nursing: Advances in Patient Safety Kerm Henriksen, 2005 v. 1. Research findings -- v. 2. Concepts and methodology -- v. 3. Implementation issues -- v. 4. Programs, tools and products. |
| data analysis in nursing: Statistics for Advanced Practice Nurses and Health Professionals Manfred Stommel, PhD, Katherine J. Dontje, 2014-06-09 Print+CourseSmart |
| data analysis in nursing: Introduction to Nursing Research Melinda Blackman, Colleen Kvaska, 2011 Nutrition Psychology: Improving Dietary Adherence presents prominent psychological theories that are known to drive human eating behavior, and reveal how these models can be transformed into proactive strategies for adhering to healthy dietary regimens. |
| data analysis in nursing: Nursing Research Denise F. Polit, Bernadette P. Hungler, 1983 The Sixth Edition of this classic text maintains its place as the Gold Standard of nursing research. Nationally and internationally known, respected and used, the text provides readers with the skills they need to design and implement a research investigation and critically evaluate published research reports. Now completely revised and updated to reflect the latest trends in quantitative and qualitative research, this essential guide offers a focused, how-to approach. New in this edition: expanded discussion of qualitative approaches; demonstration of qualitative and quantitative approaches working together; charts and tables offer description of qualitative approaches; stronger emphasis on the hands-on, how-to methodology; more in-depth examination of reasearch difference; research more powerful research utilization. |
| data analysis in nursing: Statistics for Evidence-Based Practice in Nursing MyoungJin Kim, Caroline Mallory, 2017 Statistics for Evidence-Based Practice in Nursing, Second Edition presents statistics in a readable, user-friendly manner for both graduate students and the professional nurse. |
| data analysis in nursing: Nursing Research & Statistics Rajesh Kumar, 2019-01-04 1 Introduction to Nursing Research 2 Research Problem, Research Question and Hypothesis 3 The Research Process: An Overview 4 Ethical Issues in Research 5 Review of Literature 6 Theories and Conceptual Models in Research 7 Research Designs 8 Sample and Sampling Techniques 9 Data Collection Methods in Research 10 Data Analysis and Interpretation 11 Communication and Dissemination of Research Findings 12 Introduction to Statistics Appendices Glossary Index |
| data analysis in nursing: The Fourth Paradigm Anthony J. G. Hey, 2009 Foreword. A transformed scientific method. Earth and environment. Health and wellbeing. Scientific infrastructure. Scholarly communication. |
| data analysis in nursing: Nursing Research Peggy-Anne Field, Janice M. Morse, 1985 This indispensable handbook to qualitative researchthe first of its kindleads you from theory development to research proposals through preparation and methods of research to analysis and reporting data. |
| data analysis in nursing: Nursing Research Carolyn Feher Waltz, R. Barker Bausell, 1981 |
| data analysis in nursing: Qualitative Research in Nursing Helen Streubert Speziale, Helen J. Streubert, Dona Rinaldi Carpenter, 2011 Qualitative Research in Nursing is a user-friendly text that systematically provides a sound foundation for understanding a wide range of qualitative research methodologies, including triangulation. It approaches nursing education, administration, and practice and gives step-by-step details to instruct students on how to implement each approach. Features include emphasis on ethical considerations and methodological triangulation, instrument development and software usage; critiquing guidelines and questions to ask when evaluating aspects of published research; and tables of published research that offer resources for further reading--Provided by publisher. |
| data analysis in nursing: SAGE Secondary Data Analysis John Goodwin, 2012-07-23 One central and enduring image of the social science researcher is of an individual who commits a great deal of time to collecting original, primary data from a field of enquiry. This approach is often underpinned by a sincerely held belief that key research questions can only be explored by the collection of ever new, and ever greater amounts of data, or that already existing data are insufficient for researchers to test their ideas. Yet such an approach to social science research can be problematic not least because the collection of primary data can be an expensive, time-consuming, and even wasteful approach to social enquiry. Secondary analysis can serve many purposes, as well as being a valid approach in its own right. However, despite its widespread application, secondary analysis is often undervalued or perceived to be the preserve of only those interested in the re-use of large-scale survey data. Highlighting both the theory and practice of secondary analysis and the use of secondary sources, this collection considers the nature of secondary analysis as a research tool; reflects on the definitional debates surrounding terms such as secondary analysis, data re-use and restudies; illustrates how secondary analysis is used in social science research; and finally reviews the practical, methodological and ethical aspects of secondary analysis. Volume One: Using Secondary Sources and Secondary Analysis Volume Two: Quantitative Approaches to Secondary Analysis Volume Three: Qualitative Data and Research in Secondary Analysis Volume Four: Ethical, Methodological and Practical Issues in Secondary Analysis |
| data analysis in nursing: Statistics & Data Analytics for Health Data Management Nadinia A. Davis, Betsy J. Shiland, 2015-12-04 Introducing Statistics & Data Analytics for Health Data Management by Nadinia Davis and Betsy Shiland, an engaging new text that emphasizes the easy-to-learn, practical use of statistics and manipulation of data in the health care setting. With its unique hands-on approach and friendly writing style, this vivid text uses real-world examples to show you how to identify the problem, find the right data, generate the statistics, and present the information to other users. Brief Case scenarios ask you to apply information to situations Health Information Management professionals encounter every day, and review questions are tied to learning objectives and Bloom's taxonomy to reinforce core content. From planning budgets to explaining accounting methodologies, Statistics & Data Analytics addresses the key HIM Associate Degree-Entry Level competencies required by CAHIIM and covered in the RHIT exam. - Meets key HIM Associate Degree-Entry Level competencies, as required by CAHIIM and covered on the RHIT registry exam, so you get the most accurate and timely content, plus in-depth knowledge of statistics as used on the job. - Friendly, engaging writing style offers a student-centered approach to the often daunting subject of statistics. - Four-color design with ample visuals makes this the only textbook of its kind to approach bland statistical concepts and unfamiliar health care settings with vivid illustrations and photos. - Math review chapter brings you up-to-speed on the math skills you need to complete the text. - Brief Case scenarios strengthen the text's hands-on, practical approach by taking the information presented and asking you to apply it to situations HIM professionals encounter every day. - Takeaway boxes highlight key points and important concepts. - Math Review boxes remind you of basic arithmetic, often while providing additional practice. - Stat Tip boxes explain trickier calculations, often with Excel formulas, and warn of pitfalls in tabulation. - Review questions are tied to learning objectives and Bloom's taxonomy to reinforce core content and let you check your understanding of all aspects of a topic. - Integrated exercises give you time to pause, reflect, and retain what you have learned. - Answers to integrated exercises, Brief Case scenarios, and review questions in the back of the book offer an opportunity for self-study. - Appendix of commonly used formulas provides easy reference to every formula used in the textbook. - A comprehensive glossary gives you one central location to look up the meaning of new terminology. - Instructor resources include TEACH lesson plans, PowerPoint slides, classroom handouts, and a 500-question Test Bank in ExamView that help prepare instructors for classroom lectures. |
| data analysis in nursing: Quantitative Nursing Research Thomas R. Knapp, 1998-05-13 You may stop looking now. Quantitative Nursing Research is the answer to the prayers of graduate students and practitioners who have sought the key to this often intimidating subject. In this highly readable (dare we say enjoyable?) work, Thomas R. Knapp guides the reader through the basic definitions, fundamentals of design, and techniques of quantitative research |
| data analysis in nursing: Introduction to Research Methods and Data Analysis in the Health Sciences Gareth Hagger-Johnson, 2014-06-20 Whilst the ‘health sciences’ are a broad and diverse area, and includes public health, primary care, health psychology, psychiatry and epidemiology, the research methods and data analysis skills required to analyse them are very similar. Moreover, the ability to appraise and conduct research is emphasised within the health sciences – and students are expected increasingly to do both. Introduction to Research Methods and Data Analysis in the Health Sciences presents a balanced blend of quantitative research methods, and the most widely used techniques for collecting and analysing data in the health sciences. Highly practical in nature, the book guides you, step-by-step, through the research process, and covers both the consumption and the production of research and data analysis. Divided into the three strands that run throughout quantitative health science research – critical numbers, critical appraisal of existing research, and conducting new research – this accessible textbook introduces: Descriptive statistics Measures of association for categorical and continuous outcomes Confounding, effect modification, mediation and causal inference Critical appraisal Searching the literature Randomised controlled trials Cohort studies Case-control studies Research ethics and data management Dissemination and publication Linear regression for continuous outcomes Logistic regression for categorical outcomes. A dedicated companion website offers additional teaching and learning resources for students and lecturers, including screenshots, R programming code, and extensive self-assessment material linked to the book’s exercises and activities. Clear and accessible with a comprehensive coverage to equip the reader with an understanding of the research process and the practical skills they need to collect and analyse data, it is essential reading for all undergraduate and postgraduate students in the health and medical sciences. |
| data analysis in nursing: Computer Supported Qualitative Research António Pedro Costa, Luís Paulo Reis, Francislê Neri de Souza, António Moreira, 2017-06-19 This book includes a selection of the articles accepted for presentation and discussion at the second International Symposium on Qualitative Research (ISQR2017), held in Salamanca, Spain, July 12-14, 2017. ISQR2017 is part of the Iberian-American Congress on Qualitative Research (CIAIQ), and featured four main application fields (Education, Health, Social Sciences, and Engineering and Technology) and seven main subjects: Rationale and Paradigms of Qualitative Research; Systematization of approaches with Qualitative Studies; Qualitative and Mixed Methods Research; Data Analysis Types; Innovative Processes of Qualitative Data Analysis; Qualitative Research in Web Contexts; Qualitative Analysis with the Support of Specific Software. This book is a valuable resource for academics, researchers, teachers and students who need information on the above topics, as well as on the use of Computer Assisted Qualitative Data AnalysiS (CAQDAS). |
| data analysis in nursing: Advanced Public and Community Health Nursing Practice Naomi E. Ervin, PhD, RN, PHCNS-BC, FNAP, FAAN, Pamela Kulbok, DNSc, RN, APHN-BC, FAAN, 2018-03-28 Written by advanced practice public/community health nurse experts, this comprehensive resource for advanced practice nursing students and clinicians builds upon the core foundations of practice: social justice, interdisciplinary practice, community involvement, disease prevention, and health promotion. Interweaving theory, practice, and contemporary issues, Advanced Public and Community Health Nursing Practice, Second Edition, provides essential knowledge needed to successfully assess communities, diagnose community situations, plan programs and budgets, and evaluate programs in public and community health. This revised edition has been thoroughly updated to encompass the evolution of public/community health nursing practice during the past 15 years. With several examples of community assessments, community health program plans, and evidence-based and best-practice interventions, the content in this publication addresses the core processes of advanced public/community health nursing practice. Chapters integrate new material about the physical environment and cover key changes in nursing education and practice and healthcare financing and delivery. This new edition includes additional content on culture and diversity, in-depth theory and conceptual frameworks, doctoral preparation, and policy. New to the Second Edition: Completely new information reflecting changes in nursing education and practice and healthcare financing and delivery Abundant examples of community assessments and community health program plans Evidence-based/best-practice interventions, programs, and services Clinical/practicum activities to help learners apply content in varied settings Suggested readings and references to support more in-depth study Additional information about the physical environment, culture and diversity, doctoral preparation, and policy Interprofessional/interdisciplinary practice In-depth information regarding theories and conceptual frameworks New references, examples, case studies, problems, and discussion questions Key Features: Provides comprehensive, in-depth information regarding community assessment, program planning, program implementation, evaluation, and program revision Delivers timely knowledge about using evidence, practice standards, public health ethics, Healthy People 2020, and competent practice in varied settings Includes realistic case studies of program and evaluation plans Presents examples of programs and projects conducted by advanced practice public/community health nurses |
| data analysis in nursing: Qualitative Data Analysis Patricia Bazeley, 2013-02-28 Written by an experienced researcher in the field of qualitative methods, this dynamic new book provides a definitive introduction to analysing qualitative data. It is a clear, accessible and practical guide to each stage of the process, including: - Designing and managing qualitative data for analysis - Working with data through interpretive, comparative, pattern and relational analyses - Developing explanatory theory and coherent conclusions, based on qualitative data. The book pairs theoretical discussion with practical advice using a host of examples from diverse projects across the social sciences. It describes data analysis strategies in actionable steps and helpfully links to the use of computer software where relevant. This is an exciting new addition to the literature on qualitative data analysis and a must-read for anyone who has collected, or is preparing to collect, their own data. |
| data analysis in nursing: Ethnography in Nursing Research Janice M. Roper, Jill Shapira, 2000 Describing the principles and methods of ethnography used by nurse researchers, the authors demonstrate how to: conduct ethnographic research in health settings; analyze and interpret data collected from field work; make ethical decisions related to the role of being an ethnographer; and how to put ideas in writing. |
| data analysis in nursing: Applied Missing Data Analysis Craig K. Enders, 2010-04-23 Walking readers step by step through complex concepts, this book translates missing data techniques into something that applied researchers and graduate students can understand and utilize in their own research. Enders explains the rationale and procedural details for maximum likelihood estimation, Bayesian estimation, multiple imputation, and models for handling missing not at random (MNAR) data. Easy-to-follow examples and small simulated data sets illustrate the techniques and clarify the underlying principles. The companion website includes data files and syntax for the examples in the book as well as up-to-date information on software. The book is accessible to substantive researchers while providing a level of detail that will satisfy quantitative specialists. This book will appeal to researchers and graduate students in psychology, education, management, family studies, public health, sociology, and political science. It will also serve as a supplemental text for doctoral-level courses or seminars in advanced quantitative methods, survey analysis, longitudinal data analysis, and multilevel modeling, and as a primary text for doctoral-level courses or seminars in missing data. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open …
Belmont Forum Adopts Open Data Principles for Environme…
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data …
Belmont Forum Data Accessibility Statement an…
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
