Book•
Qualitative Data Analysis: An Expanded Sourcebook
12 Jan 1994-
TL;DR: This book presents a step-by-step guide to making the research results presented in reports, slideshows, posters, and data visualizations more interesting, and describes how coding initiates qualitative data analysis.
Abstract: Matthew B. Miles, Qualitative Data Analysis A Methods Sourcebook, Third Edition. The Third Edition of Miles & Huberman's classic research methods text is updated and streamlined by Johnny Saldana, author of The Coding Manual for Qualitative Researchers. Several of the data display strategies from previous editions are now presented in re-envisioned and reorganized formats to enhance reader accessibility and comprehension. The Third Edition's presentation of the fundamentals of research design and data management is followed by five distinct methods of analysis: exploring, describing, ordering, explaining, and predicting. Miles and Huberman's original research studies are profiled and accompanied with new examples from Saldana's recent qualitative work. The book's most celebrated chapter, "Drawing and Verifying Conclusions," is retained and revised, and the chapter on report writing has been greatly expanded, and is now called "Writing About Qualitative Research." Comprehensive and authoritative, Qualitative Data Analysis has been elegantly revised for a new generation of qualitative researchers. Johnny Saldana, The Coding Manual for Qualitative Researchers, Second Edition. The Second Edition of Johnny Saldana's international bestseller provides an in-depth guide to the multiple approaches available for coding qualitative data. Fully up-to-date, it includes new chapters, more coding techniques and an additional glossary. Clear, practical and authoritative, the book: describes how coding initiates qualitative data analysis; demonstrates the writing of analytic memos; discusses available analytic software; suggests how best to use the book for particular studies. In total, 32 coding methods are profiled that can be applied to a range of research genres from grounded theory to phenomenology to narrative inquiry. For each approach, Saldana discusses the method's origins, a description of the method, practical applications, and a clearly illustrated example with analytic follow-up. A unique and invaluable reference for students, teachers, and practitioners of qualitative inquiry, this book is essential reading across the social sciences. Stephanie D. H. Evergreen, Presenting Data Effectively Communicating Your Findings for Maximum Impact. This is a step-by-step guide to making the research results presented in reports, slideshows, posters, and data visualizations more interesting. Written in an easy, accessible manner, Presenting Data Effectively provides guiding principles for designing data presentations so that they are more likely to be heard, remembered, and used. The guidance in the book stems from the author's extensive study of research reporting, a solid review of the literature in graphic design and related fields, and the input of a panel of graphic design experts. Those concepts are then translated into language relevant to students, researchers, evaluators, and non-profit workers - anyone in a position to have to report on data to an outside audience. The book guides the reader through design choices related to four primary areas: graphics, type, color, and arrangement. As a result, readers can present data more effectively, with the clarity and professionalism that best represents their work.
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TL;DR: The situations in which qualitative approaches are most helpful are described and the primary principles and practices in study design, sampling, data collection, and data analysis for qualitative studies are summarized to synthesize current standards for qualitative research methods.
Abstract: Outcomes research examines the effects of medical care interventions and policies on the health outcomes of individuals and society.1 Investigators conducting outcomes research seek to inform the development of clinical practice guidelines, to evaluate the quality of medical care, and to foster effective interventions to improve the quality of care.2 Outcomes research has traditionally used quantitative sciences to examine the utilization, cost, and clinical effectiveness of medical care through randomized and nonrandomized experimental designs. Quantitative methods are not as well suited to measure other complex aspects of the healthcare delivery system, such as organizational change, clinical leadership in implementing evidence-based guidelines, and patient perceptions of quality of care, which are also critical issues in outcomes research.3–7 These more nuanced aspects of healthcare delivery may be most appropriately examined with qualitative research methods.8–10
Qualitative approaches are becoming more common in clinical medicine and health services research.5,11–15 Federal encouragement of qualitative research is regularly reflected in funding program announcements issued by the National Institutes of Health.16 For more than a decade, federal agencies and foundations such as the National Science Foundation have demonstrated a commitment to supporting qualitative research through funding scientific conferences, workshops, and monographs on this field of inquiry.17–20 Despite this steady growth in qualitative research, outcomes investigators in cardiology have relatively little guidance on when and how best to implement these methods in their investigations.
The purpose of the present report is to introduce qualitative methods as providing unique and critical contributions to outcomes research. This report will describe the situations in which qualitative approaches are most helpful; summarize the primary principles and practices in study design, sampling, data collection, and data analysis for qualitative studies; present representative examples of cardiovascular outcomes research that uses qualitative methods; and synthesize current standards for …
991 citations
01 Jan 2002
TL;DR: This paper presents a predictive QoS model that makes it possible to compute the quality of service for workflows automatically based on atomic task QoS attributes, and presents the implementation of the model for the METEOR workflow system.
Abstract: Workflow management systems (WfMSs) have been used to support various types of business processes for more than a decade now. In workflows for e-commerce and Web service applications, suppliers and customers define a binding agreement or contract between the two parties, specifying Quality of Service (QoS) items such as products or services to be delivered, deadlines, quality of products, and cost of services. The management of QoS metrics directly impacts the success of organizations participating in e-commerce. Therefore, when services or products are created or managed using workflows, the underlying workflow system must accept the specifications and be able to estimate, monitor, and control the QoS rendered to customers. In this paper, we present a predictive QoS model that makes it possible to compute the quality of service for workflows automatically based on atomic task QoS attributes. To this end, we present a model that specifies QoS and describe an algorithm and a simulation system in order to compute, analyze and monitor workflow QoS metrics.
980 citations
Cites methods from "Qualitative Data Analysis: An Expan..."
...In view of the fact humans often feel awkward in handling and interpreting such quantitative values (Tversky and Kahneman 1974), we allow the designer with the help of a domain expert to map the value resulting from applying the fidelity function to a qualitative scale (Miles and Huberman 1994)....
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01 Jan 2006
TL;DR: In this article, Tashakkori and Teddlie's (2003, 2006) evaluation criteria frameworks involving the concept of inference quality are summarized and nine types of legitimation are described.
Abstract: In quantitative research, the importance of validity has been long accepted. In qualitative research, discussions of validity have been more contentious and different typologies and terms have been produced. In mixed methods research, wherein quantitative and qualitative approaches are combined, discussions about “validity” issues are in their infancy. We argue that because mixed research involves combining complementary strengths and nonoverlapping weaknesses of quantitative and qualitative research, assessing the validity of findings is particularly complex; we call this the problem of integration. We recommend that validity in mixed research be termed legitimation in order to use a bilingual nomenclature. Tashakkori and Teddlie’s (2003, 2006) evaluation criteria frameworks involving the concept of inference quality are summarized. Although providing a framework for assessing legitimation in mixed research always will be incomplete, it is important to address several legitimation types that come to the fore as a result of combining inferences from the quantitative and qualitative components of the study into the formation of meta-inferences. Nine types of legitimation are described here in order to continue this emerging and important dialogue among researchers and methodologists.
977 citations
Cites background from "Qualitative Data Analysis: An Expan..."
...…literature: Creswell (1998), Glaser and Strauss (1967), Kvale (1995), Lather (1986, 1993), Lincoln and Guba (1985, 1990), Longino (1995), Maxwell (1992, 1996), Miles and Huberman (1984, 1994), Onwuegbuzie and Leech (in press-a), Schwandt (2001), Strauss and Corbin (1998), and Wolcott (1990)....
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DOI•
31 May 2002TL;DR: In this article, the authors vergleiche manuelle Methoden der qualitativen Datenanalyse von Interviewtranskripten with der Anwendung von Nvivo fur den Auswertungsprozess, und befasse mich mit der Frage der Reliabilitat und Validitat des Analyseprozesses and seiner Ergebnisse.
Abstract: Dieser Aufsatz behandelt die Art und Weise, wie eine QDA-Software – hier Nvivo – im qualitativen Analyseprozess eingesetzt werden kann. Computerunterstutzte qualitative Datenanalysesoftware (CAQDAS) wird als ein Werkzeug beschrieben, das Forschende in ihrer Suche nach einer akkuraten und transparenten Reprasentation der Daten unterstutzt, wahrend zur gleichen Zeit ein Audit des Analyseprozess als Ganzes ermoglicht wird – etwas, was oft in Darstellungen des qualitativen Datenanalyseprozesses fehlt. In diesem Beitrag vergleiche ich manuelle Methoden der qualitativen Datenanalyse von Interviewtranskripten mit der Anwendung von Nvivo fur den Auswertungsprozess. Im besonderen untersuche ich die Schwierigkeiten, die im Zusammenhang mit der detaillierten Analyse von Interviewtranskripten auftreten konnen, und befasse mich mit der Frage der Reliabilitat und Validitat des Analyseprozesses und seiner Ergebnisse. Die zeitlichen Investitionen, die erforderlich sind, um das gesamte Potential von NVivo auszuschopfen, werden ebenfalls diskutiert. Es wird gezeigt, dass eine Kombination von manuellen und computerunterstutzten Verfahren am sinnvollsten zu sein scheint.
URN: urn:nbn:de:0114-fqs0202260
970 citations
Cites background or methods from "Qualitative Data Analysis: An Expan..."
...There are many different approaches to qualitative data analysis and these have been widely debated in the social sciences literature (BRYMAN & BURGESS, 1994; COFFEY & ATKINSON, 1996; DEY, 1993; MASON, 1996; MILES & HUBERMAN, 1994; SILVERMAN, 1993; STRAUSS, 1987)....
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...…or "quality" of the data, it is nevertheless important that qualitative research and data analysis are carried out in a thorough and transparent manner (CRAWFORD, LEYBOURNE & ARNOTT, 2000; CRESWELL, 1998; KIRK & MILLER, 1986; LINCOLN & GUBA, 1985; MILES & HUBERMAN, 1994; SEALE, 1999)....
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TL;DR: In this paper, a review of the e-government literature is presented, where the authors argue that eGovernment research suffers from definitional vagueness, oversimplification of eGovernment development processes within complex political and institutional environments.
965 citations