The New Face of Qualitative Research Software QSR Has Released Version Two of Xsight the Very Latest in Software from QSR Is Version Two of Xsight
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Audiovisual Media Annotation Using Qualitative Data Analysis Software: a Comparative Analysis
The Qualitative Report Volume 23 Number 13 Article 4 3-6-2018 Audiovisual Media Annotation Using Qualitative Data Analysis Software: A Comparative Analysis Liliana Melgar Estrada University of Amsterdam, [email protected] Marijn Koolen Huygens ING, [email protected] Follow this and additional works at: https://nsuworks.nova.edu/tqr Part of the Library and Information Science Commons, and the Quantitative, Qualitative, Comparative, and Historical Methodologies Commons Recommended APA Citation Melgar Estrada, L., & Koolen, M. (2018). Audiovisual Media Annotation Using Qualitative Data Analysis Software: A Comparative Analysis. The Qualitative Report, 23(13), 40-60. https://doi.org/10.46743/ 2160-3715/2018.3035 This Article is brought to you for free and open access by the The Qualitative Report at NSUWorks. It has been accepted for inclusion in The Qualitative Report by an authorized administrator of NSUWorks. For more information, please contact [email protected]. Audiovisual Media Annotation Using Qualitative Data Analysis Software: A Comparative Analysis Abstract The variety of specialized tools designed to facilitate analysis of audio-visual (AV) media are useful not only to media scholars and oral historians but to other researchers as well. Both Qualitative Data Analysis Software (QDAS) packages and dedicated systems created for specific disciplines, such as linguistics, can be used for this purpose. Software proliferation challenges researchers to make informed choices about which package will be most useful for their project. This paper aims to present an information science perspective of the scholarly use of tools in qualitative research of audio-visual sources. It provides a baseline of affordances based on functionalities with the goal of making the types of research tasks that they support more explicit (e.g., transcribing, segmenting, coding, linking, and commenting on data). -
Nvivo 8 Help Using the Software
NVivo 8 Help Using the Software This is a printable version of the NVivo 8 help that is built into the software. The help is divided into two sections - Using the Software and Working with Your Data. This section of the help contains step-by-step instructions and provides the fundamental information you need to work with the software. To electronically navigate through this help and link to other topics, go to the NVivo Help under the Help menu in your NVivo 8 software. Click a button for related instructions or concepts. Produced November 2008. Copyright © 2008 QSR International Pty Ltd ABN 47 006 357 213. All rights reserved. NVivo and QSR words and logos are trademarks or registered trademarks of QSR International Pty Ltd. Patent pending. Microsoft, .NET, SQL Server, Windows, XP, Vista, Windows Media Player, Word, PowerPoint and Excel are trademarks or registered trademarks of the Microsoft Corporation in the United States and/or other countries. Adobe, .pdf and Flash Player are trademarks or registered trademarks of Adobe Systems Integrated in the United States and/or other countries. QuickTime and the QuickTime logo are trademarks or registered trademarks of Apple Computer, Inc., used under license there from. Crystal Reports is a trademark or registered trademark of Business Objects SA. This information is subject to change without notice. NVivo 8 Help - Using the Software Contents Introduction ............................................................................................................................................................ -
Qualitative Data Analysis Software: a Call for Understanding, Detail, Intentionality, and Thoughtfulness
MSVU e-Commons ec.msvu.ca/xmlui Qualitative data analysis software: A call for understanding, detail, intentionality, and thoughtfulness Áine M. Humble Version Post-print/Accepted manuscript Citation Humble, A. M. (2012). Qualitative data analysis software: A call for (published version) understanding, detail, intentionality, and thoughtfulness. Journal of Family Theory & Review, 4(2), 122-137. doi:10. 1111/j.1756- 2589.2012.00125.x Publisher’s Statement This article may be downloaded for non-commercial and no derivative uses. This article appears in the Journal of Family Theory and Review, a journal of the National Council on Family Relations; copyright National Council on Family Relations. How to cite e-Commons items Always cite the published version, so the author(s) will receive recognition through services that track citation counts. If you need to cite the page number of the author manuscript from the e-Commons because you cannot access the published version, then cite the e-Commons version in addition to the published version using the permanent URI (handle) found on the record page. This article was made openly accessible by Mount Saint Vincent University Faculty. Qualitative Data Analysis Software 1 Humble, A. M. (2012). Qualitative data analysis software: A call for understanding, detail, intentionality, and thoughtfulness. Journal of Family Theory & Review, 4(2), 122-137. doi:10. 1111/j.1756-2589.2012.00125.x This is an author-generated post-print of the article- please refer to published version for page numbers Abstract Qualitative data analysis software (QDAS) programs have gained in popularity but family researchers may have little training in using them and a limited understanding of important issues related to such use. -
Qualitative Research 1
Qualitative research 1 Dr Raqibat Idris, MBBS, DO, MPH Geneva Foundation for Medical Education and Research 28 November 2017 From Research to Practice: Training Course in Sexual and Reproductive Health Research Geneva Workshop 2017 Overview of presentation This presentation will: • Introduce qualitative research, its advantages, disadvantages and uses • Discuss the various approaches to qualitative design Introduction • Qualitative research is a study done to explain and understand the meaning or experience of a phenomenon or social process and the viewpoints of the affected individuals. • Investigates opinions, feelings and experiences. • Understands and describes social phenomena in their natural occurrence- holistic approach. • Does not test theories but can develop theories. Mason, 2002 Features of qualitative research • Exploratory • Fluid and flexible • Data-driven • Context sensitive • Direct interaction with affected individuals Mason, 2002 Advantages and disadvantages Advantages: • Richer information • Deeper understanding of the phenomenon under study Disadvantages: • Time consuming • Expensive • Less objective • Findings cannot be generalized Mason, 2002 Uses of qualitative studies Exploratory or pilot study: • Precedes a quantitative study to help refine hypothesis • Pilot study to examine the feasibility of a program/ project implementation • Designing survey questionnaires • To improve the reliability, validity and sensibility of new or existing survey instruments in a new population Green, 2013 Uses of qualitative studies To explain quantitative data findings: • Can follow a quantitative research to help provide a deeper understanding of the results. For example, the use of ethnography to explain the social context in which mortality and birth rate data are produced. • Parallel studies in a mixed qualitative and quantitative design to provide greater understanding of a phenomenon under study. -
Current Issues in Qualitative Data Analysis Software (QDAS): a User and Developer Perspective
The Qualitative Report Volume 23 Number 13 Article 5 3-6-2018 Current Issues in Qualitative Data Analysis Software (QDAS): A User and Developer Perspective Jeanine C. Evers Erasmus University of Rotterdam, [email protected] Follow this and additional works at: https://nsuworks.nova.edu/tqr Part of the Law Commons, Quantitative, Qualitative, Comparative, and Historical Methodologies Commons, and the Social Statistics Commons Recommended APA Citation Evers, J. C. (2018). Current Issues in Qualitative Data Analysis Software (QDAS): A User and Developer Perspective. The Qualitative Report, 23(13), 61-73. https://doi.org/10.46743/2160-3715/2018.3205 This Article is brought to you for free and open access by the The Qualitative Report at NSUWorks. It has been accepted for inclusion in The Qualitative Report by an authorized administrator of NSUWorks. For more information, please contact [email protected]. Current Issues in Qualitative Data Analysis Software (QDAS): A User and Developer Perspective Abstract This paper describes recent issues and developments in Qualitative Data Analysis Software (QDAS) as presented in the opening plenary at the KWALON 2016 conference. From a user perspective, it reflects current features and functionality, including the use of artificial intelligence and machine learning; implications of the cloud; user friendliness; the role of digital archives; and the development of a common exchange format. This user perspective is complemented with the views of software developers who took part in the “Rotterdam Exchange Format Initiative,” an outcome of the conference. Keywords Qualitative Data Analysis Software, QDAS, Artificial Intelligence, Machine Learning, TLA AS.ti, Cassandre, Dedoose, f4analyse, MAXQDA, NVivo, QDA Miner, Quirkos, Transana, Exchange format, Interoperability, Qualitative Data Analysis, Learning Curve QDAS, Textual Data Mining, Cloud services. -
A Software-Assisted Qualitative Content Analysis of News Articles: Example and Reflections
Volume 16, No. 2, Art. 8 May 2015 A Software-Assisted Qualitative Content Analysis of News Articles: Example and Reflections Florian Kaefer, Juliet Roper & Paresha Sinha Key words: Abstract: This article offers a step-by-step description of how qualitative data analysis software can qualitative content be used for a qualitative content analysis of newspaper articles. Using NVivo as an example, it analysis; method; illustrates how software tools can facilitate analytical flexibility and how they can enhance news articles; transparency and trustworthiness of the qualitative research process. Following a brief discussion NVivo software; of the key characteristics, advantages and limitations of qualitative data analysis software, the qualitative data article describes a qualitative content analysis of 230 newspaper articles, conducted to determine analysis software international media perceptions of New Zealand's environmental performance in connection with (QDAS); climate change and carbon emissions. The article proposes a multi-level coding approach during computer-assisted the analysis of news texts that combines quantitative and qualitative elements, allowing the qualitative data researcher to move back and forth in coding and between analytical levels. The article concludes analysis that while qualitative data analysis software, such as NVivo, will not do the analysis for the (CAQDAS) researcher, it can make the analytical process more flexible, transparent and ultimately more trustworthy. Table of Contents 1. Introduction 2. Literature Review 2.1 Qualitative content analysis (QCA) 2.2 Software-assisted qualitative data analysis 3. A Step-by-Step Approach to Software-Assisted Qualitative Content Analysis of News Articles 3.1 Step 1: Selecting and learning the software 3.1.1 Getting to know the software: Sample project, literature review 3.2 Step 2: Data import and preparation 3.3 Step 3: Multi-level coding 3.3.1 Coding: Inductive vs. -
The Coding Manual for Qualitative Researchers for Manual Coding The
2E Second Edition The Coding Manual for Qualitative Researchers ‘This book fills a major gap in qualitative research methods courses. Saldaña has accomplished what has not been done before - creating a text that clearly identifies the many choices one has in coding their data. I wish I had this book when I started conducting qualitative research. It should be required reading for all.’ Mark Winton, Criminal Justice Instructor, University of Central Florida ‘An excellent handbook that helps demystify the coding process with a comprehensive assessment of different coding types, examples and exercises. As such it is a valuable teaching resource and it will also be of use to anyone undertaking qualitative analysis.’ Kevin Meethan, Associate Professor in Sociology, Plymouth University The ‘The Coding Manual describes the qualitative coding process with clarity and expertise. Its wide array of strategies, from the more straightforward to the more complex, are skillfully explained and exemplified. This extremely usable manual is a must-have resource for qualitative researchers at all levels.’ Coding Manual for Tara M. Brown, Assistant Professor of Education, Brandeis University The second edition of Johnny Saldaña’s international bestseller provides an in-depth guide to the Qualitative Researchers 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 Coding Manual for Qualitative Researchers 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. -
Benefits to Qualitative Data Quality with Multiple Coders: Two Case Studies in Multi-Coder Data Analysis." Journal of Rural Social Sciences, 34(1): Article 2
Journal of Rural Social Sciences Volume 34 Issue 1 Volume 34, Issue 1 Article 2 8-9-2019 Benefits ot Qualitative Data Quality with Multiple Coders: Two Case Studies in Multi-coder Data Analysis Sarah P. Church Montana State University, [email protected] Michael Dunn Centre for Ecosystems, Society and Biosecurity, Forest Research, Northern Research Station (UK), [email protected] Linda S. Prokopy Purdue University, [email protected] Follow this and additional works at: https://egrove.olemiss.edu/jrss Part of the Demography, Population, and Ecology Commons, and the Rural Sociology Commons Recommended Citation Church, Sarah, Michael Dunn, and Linda Prokopy. 2019. "Benefits to Qualitative Data Quality with Multiple Coders: Two Case Studies in Multi-coder Data Analysis." Journal of Rural Social Sciences, 34(1): Article 2. Available At: https://egrove.olemiss.edu/jrss/vol34/iss1/2 This Research Note is brought to you for free and open access by the Center for Population Studies at eGrove. It has been accepted for inclusion in Journal of Rural Social Sciences by an authorized editor of eGrove. For more information, please contact [email protected]. Benefits ot Qualitative Data Quality with Multiple Coders: Two Case Studies in Multi-coder Data Analysis Cover Page Footnote Please address all correspondence to Dr. Sarah Church ([email protected]). This research note is available in Journal of Rural Social Sciences: https://egrove.olemiss.edu/jrss/vol34/iss1/2 Church et al.: Benefits to Qualitative Data Quality with Multiple Coders Benefits to Qualitative Data Quality with Multiple Coders: Two Case Studies in Multi-coder Data Analysis Sarah P. -
Using Nvivo to Analyze Qualitative Classroom Data on Constructivist Learning Environments
The Qualitative Report Volume 9 Number 4 Article 2 12-1-2004 Using NVivo to Analyze Qualitative Classroom Data on Constructivist Learning Environments Betul C. Ozkan University of West Georgia, [email protected] Follow this and additional works at: https://nsuworks.nova.edu/tqr Part of the Quantitative, Qualitative, Comparative, and Historical Methodologies Commons, and the Social Statistics Commons Recommended APA Citation Ozkan, B. C. (2004). Using NVivo to Analyze Qualitative Classroom Data on Constructivist Learning Environments. The Qualitative Report, 9(4), 589-603. https://doi.org/10.46743/2160-3715/2004.1905 This Article is brought to you for free and open access by the The Qualitative Report at NSUWorks. It has been accepted for inclusion in The Qualitative Report by an authorized administrator of NSUWorks. For more information, please contact [email protected]. Using NVivo to Analyze Qualitative Classroom Data on Constructivist Learning Environments Abstract This article describes how a qualitative data analysis package, NVivo, was used in a study of authentic and constructivist learning and teaching in the classroom. The paper starts with a summary of the research study in which NVivo was used to analyze the data and overviews the methodology that was adopted in this study. It, then, describes how NVivo was used in the analysis of observational (video) data, interviews and field notes. Keywords Computer Based Qualitative Data Analysis, Qualitative Data Analysis, Computer Based Data Analysis, NVivo, and Constructivist Learning Environments Creative Commons License This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 License. This article is available in The Qualitative Report: https://nsuworks.nova.edu/tqr/vol9/iss4/2 The Qualitative Report Volume 9 Number 4 December 2004 589-603 http://www.nova.edu/ssss/QR.QR9-4/ozkan.pdf Using NVivo to Analyze Qualitative Classroom Data on Constructivist Learning Environments Betul C. -
A Tool for Researching Chronic Pelvic Pain Using Thematic Analysis
QUALITATIVE RESEARCH: PRACTICES AND CHALLENGES USING RQDA IN QUALITATIVE DATA ANALYSIS: A TOOL FOR RESEARCHING CHRONIC PELVIC PAIN USING THEMATIC ANALYSIS Bruna Helena Mellado1, Catarina Brandão2, 3 , Taynara Louisi Pilger1 , Francisco José Candido dos Reis1 1Department of Gynecology and Obstetrics, Ribeirão Preto Medical School, University of São Paulo, Brazil. [email protected] [email protected]; [email protected] 2Faculty of Psychology and Educational Sciences of the University of Porto, Portugal. [email protected] 3 Psychology Center of the University of Porto, Portugal Abstract. Computer-assisted qualitative data analysis software (CAQDAS) provides tools that help researchers develop qualitative research projects. These software packages support users with tasks such as transcription analysis, text encoding and interpretation, writing and annotation, searching, recursive abstraction, and data mapping, enabling the use of different methods (e.g., grounded theory) and analysis techniques (e.g., content analysis or discourse analysis). The choice of CAQDAS is directly related to the researcher’s chosen methodological approach. The amount and variety of software available often makes the choice process difficult. In Brazil, the data on the use of CAQDAS is still scarce. Commercial software packages are more frequently cited. Open- source packages, like RQDA, are less popular. In this chapter, we present our experience with the use of RQDA to analyze the data from a qualitative study on chronic pelvic pain in women. Keywords: Computer-Assisted Qualitative Data Analysis Software, RQDA, Thematic Analysis, Pelvic Pain. 1. INTRODUCTION The field of qualitative research is characterized by different epistemological perspectives and methodological orientations, which often leads to intense academic debate (Ghedin & Franco, 2008; Merriam, 2007). -
Investigating Computer Assisted Qualitative Data Analysis Software (CAQDAS)
1 Proposal for Workshop for iConference 2019 Workshop Title: InVivo Inspiration: Investigating Computer Assisted Qualitative Data Analysis Software (CAQDAS) Organizers: Marie L. Radford1, Vanessa Kitzie2, Diana Floegel3, Lynn Silipigni Connaway4, Jenny Bossaller5, Sean Burns6 Abstract: This half-day workshop will provide an overview and comparison of computer- assisted qualitative data analysis software (CAQDAS). Since adoption of these programs requires substantial time commitment and/or budget expenditure, it is vital to understand their capabilities and limitations, as well as the types of data best suited for each platform. A panel of experts will present advantages and disadvantages of several software packages, then demonstrate how to use popular CAQDAS platforms, including commercial (i.e., NVivo, ATLAS.ti, Qualtrics, Dedoose) and open source (i.e., RQDA) programs. Panelists will then invite attendees to participate in interactive breakout tables to learn more about and experiment with a product of their choice. Panelists will answer attendees’ questions and demonstrate advanced features. The workshop will conclude with a general Q&A session. Both novice and experienced researchers will benefit by learning about the variety of available CAQDAS options. Description: Purpose and Intended Audience Are you a novice or experienced researcher drowning in qualitative data? Are you wondering how on earth you will ever have the time to analyze reams of text, audio files, video footage, and field notes? Would you like to learn about opportunities and pitfalls involved in switching to a digital analysis tool? If so, this is the workshop for you. In an interactive and engaging workshop, experts in qualitative data analysis will introduce CAQDAS and will present important considerations for those wondering whether or not to adopt this type of software. -
Designing AI-Based Systems for Qualitative Data Collection and Analysis
Designing AI-Based Systems for Qualitative Data Collection and Analysis Zur Erlangung des akademischen Grades eines Doktors der Wirtschaftswissenschaften ( Dr. rer. pol. ) von der KIT-Fakultät für Wirtschaftswissenschaften des Karlsruher Instituts für Technologie (KIT) genehmigte DISSERTATION von Tim Rietz, M.Sc. ______________________________________________________________ Tag der mündlichen Prüfung: 01.07.2021 Referent: Prof. Dr. Alexander Mädche Korreferent: Prof. Dr. Paola Spoletini Karlsruhe Mai 2021 Acknowledgments Having started my PhD studies in December 2017, I remember the past three and a half years as a series of ups and downs, which probably goes for everything in life. Looking back at this exciting, inspiring, and challenging time, I distinctly remember many ups, while the downs seem almost forgotten. To a large extent, I attribute this to the wonderful people that I got to meet along the way, who never failed to make my time as a PhD student and as an IT consultant fun. Certainly, I want to thank my mentor and PhD supervisor Prof. Dr. Alexander M¨adche, for his guidance, inspiration, and feedback throughout my studies. While I did not know what to expect when I started my position at the institute, I quickly learned how lucky I was with my choice of a supervisor. Alexander always had an open door for my questions, ideas, and concerns. He also actively seeked updates on my process and encourage me to submit my research to prestigious outlets. I am incredibly grateful for your support. On that note, I also want to thank Prof. Dr. Paola Spoletini, Prof. Dr. Hagen Lindst¨adt, and Prof. Dr.